esa-sceva/satcom-chunk-collection huggingface dataset:
# Reproducibility and Data Storage for Active Learning-Aided Systematic Reviews
## 2 Reproducibility in the Context of Systematic Screening
### Reproducibility in the Three Phases  
In [PERSON], [PERSON] [36], the authors try to clarify the reproducibility terminology by distinguishing between three different kinds of reproducibility depending on the goal:'method reproducibility','results reproducibility', and 'inferential reproducibility'. We can relate this to the different steps of the screening phase outlined in Figure 2. The strictest form of reproduction would be that given an identical input dataset, annotator, choice of model, and initial training set, from which we obtain identical trained models, model rankings, and labeled records, seen in identical order. This form of reproduction checks that repeating the same steps leads to the same results at every point in between. It tells us that the system is behaving predictably. This is what is meant by'method reproducibility'.  
We could also keep everything fixed but allow for a different annotator. In that case, we can check that another person can obtain the same results using this system. If the annotator makes the same decision for each record, the end result should be the same. However, this will not always happen since annotators might disagree about whether certain records should be included or they make mistakes. This form of reproducibility is the simplest form of'results reproducibility' from [PERSON], [PERSON] [36].  
A more functionally oriented definition of reproduction would be to demand that the input dataset is identical but allow for variation in the other steps as long as the final set of included records is the same. This procedure checks that the final results are not dependent on the choices made for the active learning system. Of course, this is specific for RITL active learning (see Box 1), where the set of labeled records is the primary output. In the case of classical active learning, we would need to demand that the trained models are identical or at least very similar.  
Lastly, we could take an even broader view and argue that the set of included records is also allowed to vary as long as the conclusion coming out of the post-processing stage is still the same. This perspective encourages a broader view of reproducibility, where the focus is not only on individual records but also on the robustness of the overall conclusions when subjected to different analytical conditions. This is called 'inferential reproducibility' [36], suggesting that a robust conclusion should ideally be stable across different subsets of data,provided the subsets are representative and substantial. This does not imply that missing records are inconsequential, but rather that the conclusions of a well-conducted review should be resilient to the variations that might arise from different systematic screening approaches. Reproducing a systematic screening phase in this way shows whether missing a specific record is essential for the final conclusions; for an example, see [37].
## 2 Bayesian Functional Mapping
### Modeling the Mean-Covariance Structures  
Functional mapping models \(u_{j}(t)\) by a biologically meaningful mathematical equation and the time-dependent covariance matrix composed of \(\sigma^{2}(t)\) and \(\sigma(t_{1},t_{2})\) by statistically robust approaches. For example, the growth of a living entity can be defined as the irreversible increase of size with time. A series of mathematical models have been proposed to describe growth curves. Among these models, the sigmoidal (or logistic) growth function is regarded as being nearly universal in living systems to capture age-specific changes [10]. Specifically, this S-shaped curve for a QTL genotype \(j\) can be mathematically expressed as:  
\[g_{j}(t\,|\,\mathbf{\Theta}_{j})=\frac{\alpha_{j}}{1+\beta_{j}e^{-\gamma_{j}t}} \tag{2}\]  
where \(\mathbf{\Theta}_{j}=(\alpha_{j},\beta_{j},\gamma_{j})\) is set of parameters that determine curve shape of genotype \(j\). Parameter \(\alpha\) is the limiting value of growth as time \(t\) goes to infinity, \(\alpha/(1+\beta)\) is the initial value of growth, and \(\gamma\) is the relative growth rate. Many biologically important features of growth curves, such as the time of maximal growth rate and the duration of maximal growth, can be described by these parameters or their combinations.  
A number of statistical methods have been derived to model the structure of covariance between longitudinal measurements [21]. The first-order autoregressive (AR(1)) model has been applied to model the structure of the within-subject covariance matrix for functional mapping. This model uses two simplified assumptions, i.e., variance stationarity - the residual variance (\(\sigma^{2}\)) is constant over time, and covariance stationarity - the correlation between different measurements decreases proportionally (in \(\rho\)) with increased time interval. These two assumptions facilitate the computation of functional mapping. [PERSON] _et al._[22] embedded [PERSON]'s [23] transform-both-sides (TBS) model into the growth-incorporated finite mixture model, in order to reduce the heteroscedasticity of the residual variance and preserve original biological means of curve parameters after the data are transformed. \(\mathrm{N\ddot{u}inez-Ant\ddot{o}n}\) and colleagues proposed a series of so-called structured antedependence (SAD) models to approximate the age-specific change of correlation in the analysis of longitudinal variables [20]. The SAD model has been employed in several studies and displays many favorable properties for genetic mapping of dynamic traits [24].
## 5 Conclusions  
1. The largest absorption-dissipation is performed by dentine (i.e., approx. 40-93%) and enamel (i.e., approx. 40-65%) components, while the stainless-steel bracket base has a limited ability (i.e., approx. 16%).
2. The main absorption-dissipation of stresses takes place in coronal part since there is little difference (i.e., % of stress) at root level between the three structures (i.e., tooth with bracket, tooth without bracket, and dentine), the main differences being visible only in coronal part.
3. Enamel, dentine, and stainless-steel bracket biomechanically behave as a single-stand structure, allowing for only a limited fraction of stresses to reach the circulatory sensitive tissues (i.e., approx. 0.3-8.4% in PDL, 0.2-0.7% in NVB, 0.02-0.12% in dental pulp, and quantitatively under the physiological amount of 16 KPa of MHP).
4. Enamel component displayed dentine resemblance of absorption-dissipation ability, thus being proven to resemble more ductile materials (with a certain brittle flow mode).
5. Tooth behaves as a single-stand structure showing the highest absorption-dissipation ability among dental tissues.
6. These numerical simulations confirmed clinical biomechanical knowledge, showing that 0.6-1.2 N of force are safe to be applied in intact periodontium, while for reduced periodontium, forces higher than 0.6 N are prone to resorptive and ischemic risks.
7. For reducing ischemic and resorptive risks, after 4 mm of bone loss, 0.2-0.6 N of force are recommended, to keep stresses under the 16 KPa physiological limit.
8. The rotational and translational movements were the most stressful, followed by tipping.
9. Both intrusion and extrusion supplied similar quantitative and quantitative results.
10. Forces of 0.6 N, 1.2 N, and 2.4 N displayed similar qualitative results for all movements and bone levels, while quantitative results doubled for 1.2 N and quadrupled for 2.4 N when compared with 0.6 N.
11. The employment of isotropy, linear elasticity, and homogeneity as assumed boundary conditions for the study of dental tissues seems to be correct when Tresca criterion (for non-homogenous materials) is used, for loads up to 2.4 N.  
Conceptualization, R.-A.M.; methodology, R.-A.M.; software, R.-A.M.; validation, R.-A.M. and C.D.O.; formal analysis, R.-A.M.; investigation, R.-A.M.; resources, R.-A.M.; data curation, R.-A.M.; writing--original draft preparation, R.-A.M.; writing--review and editing, R.-A.M., A.G.D. and C.D.O.; visualization, supervision, project administration, R.-A.M., A.G.D. and C.D.O.; funding acquisition, R.-A.M., A.G.D. and C.D.O. All authors have read and agreed to the published version of the manuscript.  
The authors were the funders of this research project.  
The research protocol has been approved by the Ethical Committee of the University of Medicine (158/2.04.2018).  
Informed Consent Statement: Informed oral consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data are contained within the article.  
The authors declare that they have no conflicts of interest.
## Materials and Methods
### Data Sources and Preprocessing
#### 2.2.2 GOSIF Data  
The SIF dataset is a key one used to construct the GPP\({}_{\text{SIF}}\) model. The GOSIF product used was a global SIF dataset produced with a \(0.05^{\circ}\times 0.05^{\circ}\) spatial resolution and an 8-day temporal resolution based on OCO-2, MODIS, and meteorological data [37] (available at [[http://globalecology.unh.edu/data/GOSIF.html](http://globalecology.unh.edu/data/GOSIF.html)]([http://globalecology.unh.edu/data/GOSIF.html](http://globalecology.unh.edu/data/GOSIF.html)), accessed on 10 April 2024). Compared with OCO-2 SIF, GOSIF offers superior spatial resolution and continuous and comprehensive global coverage. We used monthly GOSIF data for China from 2003 to 2010 and extracted the GOSIF values of the site locations using the latitude and longitude of the flux towers.
## 2 Results
### Performance of SPAD Prediction Regression Models  
The root mean square error (RMSE), the adjusted determination coefficient (R\({}^{2}\)), and the mean absolute error (MAE) were the metrics used to comparatively assess the performance of the SPAD regression models.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Full Communicating Sensor-Energy Consumption Results
#### 6.3.4 Comparison between Proposed Scenarios  
Table 7 presents comparison between the proposed scenarios. As we can see, the sensor node lifetime in Scenario 1 is higher than Scenarios 2 and 3. These results show the energy consumption cost of receiving downlink messages from the gateway.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Characteristics** & **RF Energy Consumption (mJ)** & **Sensor Autonomy (Months)** \\ \hline Scenario 1 & TX; RX1 and RX2 not done & \(E_{Tt}=0.59\); \(E_{R}=0\) & 61.8 \\ Scenario 2 & TX; RX1 done; RX2 not done & \(E_{Tt}=0.59\); \(E_{R}=0.27\) & 54.4 \\ Scenario 3 & TX; RX1 not done ; RX2 done & \(E_{Tt}=0.59\); \(E_{R}=0.54\) & 48.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between different scenarios  
Figure 19: Energy consumption of sensor node: Scenario 3.  
Figure 20: Sensor node autonomy: Scenario 3.  
Equation (31) depicts the total consumed energy \(E_{total}\) as a function of the probability of having Scenario 3 noted \(p\):  
\[E_{total}=(1-p).E_{scenario2}+p.E_{scenario3}, \tag{31}\]  
where \(E_{scenario2}\) and \(E_{scenario3}\) are the total consumed energies using Scenarios 2 and 3, respectively. Using results in Sections 6.3.2 and 6.3.3, the sensor node autonomy as a function of probability \(p\) is given in Figure 21. We remark that the sensor node lifetime decreases from 54.4 months when \(p=0\) (which is the sensor node lifetime using Scenario 2) to 48.7 months when \(p=\)1 (the probability that Scenario 3 occurs).  
In the ideal case (data transmission with acknowledgment reception and without transmission error), Scenario 2 is the most frequent one (meaning that the probability \(p\) is closed to 0). Thus, Scenario 2 is selected for the rest of this paper.
## 3 Results  
### Effectiveness Experiment Results  
In order to further analyze the effectiveness of the application methods proposed in this paper, we used the experimental setup in Section 2.5.1 and measured these methods through evaluation metrics.  
The experimental results of the effectiveness of YOLOv4 are shown in Table 2. Compared to mainstream object detection networks such as SSD and YOLOv5x, YOLOv4 performs very well on this dataset. The mAP (0.5) values for YOLOv4 are 31.27% and 20.11% higher than SSD and YOLOv5x, respectively. Moreover, the flops value of YOLOv4 is lower than both of them, indicating that the model is relatively less computationally intensive.  
Data augmentation experiment results are shown in Table 3. After data-enhancement experiments using yolov4_RMLight, it can be seen that the values of mAP (0.5) and mAP (0.75) have increased by 5.49% and 6.11%, respectively, after data enhancement. The effectiveness of the data-enhancement method is proved.  
The experimental results to improve the effectiveness of the enhanced feature extraction network are shown in Table 4. It is evident from the results that the improvements in the enhanced feature extraction network substantially reduce the number and size of parameters in the model. This makes the improved network much lighter.  
GhostNet was used as the backbone feature extraction network in yolov4_RMLight_ADD. To verify the effectiveness of GhostNet, the backbone feature network was replaced using an existing mainstream classification network. The experimental results are shown in Table 5. The experimental results clearly show that GhostNet has a clear advantage as a backbone feature extraction network in terms of both accuracy and performance. The non-lightweight Resnet50 and VGG16 far exceeded the lightweight networks in terms of computational effort and model size, which made deployment on mobile robotic devices more difficult, and several popular lightweight networks do not perform as well as GhostNet in terms of accuracy. GhostNet was therefore chosen as the backbone extraction network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **mAP (0.5)** & **Model Size/MB** & **Parameter** \\  & & **Quantity/Million** \\ \hline
**yolov4_RMLight\_ADD** & **86.84\%** & **42.50** & **10.80** \\ yolov4\_RMLight\_ADD\_0 & 70.31\% & 145 & 38.38 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The experimental results of improving the effectiveness of the enhanced feature extraction network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **mAP (0.5)** & **Model Size/MB** & **Flops** \\ \hline YOLOv4 & 88.63\% & 244 & 30.17 GB \\ SSD & 57.36\% & 92.6 & 31.35 GB \\ YOLOv5x & 68.52\% & 333 & 109 GB \\ \hline \hline \end{tabular}
\end{table}
Table 2: YOLOv4 validity experiment results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **mAP (0.5)** & **mAP (0.75)** \\ \hline
**yolov4_RMLight\_ADD** & **86.84\%** & **50.91\%** \\ yolov4\_RMLight & 81.35\% & 44.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data augmentation experiment results.
# Experimental Study on Flexural Capacity of Corroded RC Slabs Reinforced with Basalt Fiber Textile
## 2 Experimental Program  
### Test Specimens  
To investigate the flexural performance of the corroded slabs strengthened by BTRM and BFRP, 10 RC slabs were prefabricated in the laboratory. The specimens were designed according to the _Code for design of concrete structures (GB50010-2010)_[39], with a length of 2300 mm and a rectangular cross-section of \(b\times h\) = 700 mm \(\times\) 100 mm. The concrete cover thickness at the bottom was 30 mm. For each RC slab, 5 longitudinal HRB400 bars (diameter = 12 mm, and cross-sectional area = 113 mm\({}^{2}\)) were used for tensile reinforcing (\(\rho_{s}\) = 1.26%), and 12 transverse HRB400 bars (diameter = 6 mm, and cross-sectional area = 28.3 mm\({}^{2}\)) were used for the distributed reinforcement. The tensile reinforcing bars extended out of the ends of the slab to connect the wires and the surfaces of the distributed bars were wrapped up in an insulating coating to prevent rusting. The detailed dimensions of the specimens are shown in Figure 1. To depassivate the tensile reinforcing bars, the concrete was mixed with 4% NaCl by weight of cement. The measured cubic compressive strength of C40 concrete was 43.5 MPa, and the yield and tensile strengths of the reinforcing bars were 446.3 MPa and 594.9 MPa, respectively.
# A 32-GHz Eight-Way Power Amplifier MMIC in 150 nm GaN HEMT Technology
###### Abstract  
This paper presents a 32 GHz high-power amplifier (HPA) with a design strategy to achieve high-power output with reliable operation for Ka-band deep space satellite communication in 150 nm GaN HEMT technology. The presented Ka-band HPA employs a cascaded two-stage common source amplifier topology, and the output stage comprises an eight-way power combining network in the current mode. The interstage matching network is designed with the bandpass configuration utilizing capacitors and transmission lines to provide better stability at the low-frequency regime. The implemented Ka-band HPA achieved a power gain of 7.3 dB at the input level with the maximum PAE at 32 GHz, and the 3 dB gain bandwidth was 3.5 GHz (31.3-34.8 GHz). The saturated output power at the peak power-added efficiency (PAE) of 19.3% was 38.2 dBm, and the output 1 dB gain compression point (OP1 dB) was 27.4 dBm in the measurement. The designed HPA consumes an area of 19.35 mm\({}^{2}\) including RF pads and DC pads.  
mm-wave; deep space exploration; satellite communication; power amplifier; GaN; HEMT
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Unconventionnal Electrochemical Immunosensors Working in Organic Solvents  
As reviewed above, the most targeted analytes are organic compounds which are poorly soluble in water. Consequently, it is of interest to develop electrochemical immunosensors able to work directly in organic solvent or at least in a mixture of organic and aqueous solvents. Immunoassays in organic phase have been reported since at least two decades [177], investigated in optical [178] and gravimetric [179] devices recently, but stayed scarce for electrochemical immunoassays.  
In 2012, [PERSON] et al. [180] reported an immunosensor for the analysis of atrazine in olive oil, using HRP as antibody label, through a competitive immunoreaction. A LoD of about 50 nM was achieved. The immunore recognition was performed in oil, and the electrochemical detection in PBS. The same authors reported in 2015 detection of three kind of pesticides (triazinic, organophosphates and chlorurates) in sunflower oil, still through a competitive immunodetection process which took place in an \(n\)-hexane-chloroform mixture, followed by electrochemical detection (HRP-labelled Ab) performed in decane. The LoD was 10 nM. [181; 182].
## 2 Model Construction
### MPDIoU Loss Function  
Most state-of-the-art target detection models rely on a bounding box regression (BBR) module to determine the location of a target. Based on this model, a well-designed loss function is very important for the success of BBR. However, most of the existing BBR loss functions have the same value for different predictions, which reduces the convergence speed and accuracy of bounding box regression. In this paper, we use the MPDIoU [34] optimization model performance by directly minimizing the distance between the upper-left and lower-right corner points between the predicted bounding box and the real bounding box, rather than through _IoU_ (Intersection over Union), the details are shown in Figure 7. This helps to more directly measure the positional differences between bounding boxes. By optimizing the loss function, MPDIoU can make the predicted bounding box more accurately align with the actual target, thereby improving the accuracy of detection. By considering the width and height of the bounding box, MPDIoU can obtain more detailed position information, thereby better guiding the model for regression. MPDIoU adopts a maximization strategy in loss calculation, aiming to maximize accuracy. This method may make the model pay more attention to the accuracy of the bounding box during the learning process. The MPDIoU formula is as follows:  
\[d_{1}^{2}=\left(x_{1}^{B}-x_{1}^{A}\right)^{2}+\left(y_{1}^{B}-y_{1}^{A}\right) ^{2} \tag{3}\]  
\[d_{2}^{2}=\left(x_{2}^{B}-x_{2}^{A}\right)^{2}+\left(y_{2}^{B}-y_{2}^{A} \right)^{2} \tag{4}\]  
\[\text{MPDIoU}=\frac{A\cap B}{A\cup B}-\frac{d_{1}^{2}}{w^{2}+h^{2}}-\frac{d_{ 2}^{2}}{w^{2}+h^{2}} \tag{5}\]  
\[L_{\text{MPDIoU}}=1-\text{MPDIoU} \tag{6}\]  
Figure 6: Detection Head Structure.
## 4 Branch-and-Price-and-Cut Algorithm
### Initializing the RLMP  
Initializing the RLMP is a crucial and foundational step in the column generation algorithm. This significance stems from the fact that the RLMP is responsible for providing dual values that are utilized in the objective function of the subproblem. Therefore, we should initialize the RLMP with a feasible solution to ensure that the dual values for all constraints in the RLMP are obtained and then passed to the subproblem.  
In this paper, a simple greedy heuristic is employed to generate the initial columns for the RLMP, as detailed in Algorithm 2. This heuristic makes a series of decisions by selecting the best available option at each step, without accounting for the broader, global impact of these choices. Specifically, this heuristic operates as follows. We start it by generating a set of \(K_{\text{{truck}}}\) empty routes. Then, we consider each unfulfilled request and try to add it to the tail of each route. During this insertion process, we evaluate the feasibility of each potential insertion by taking into account constraints such as time windows and depot capacity. Afterwards, the trial with minimal travel cost increment is selected and the request associated with the selected trial is then assigned to the corresponding route. This iterative procedure continues until either all inland container transportation requests have been successfully inserted into routes, or the remaining requests cannot be accommodated within any of the existing routes due to feasibility constraints.  
If the heuristic approach fails to find a viable solution for the RLMP, a back-up plan is implemented to ensure that the RLMP consistently has a feasible solution. To this end, a column representing a fictitious route that visits all nodes is included, with an excessively high cost to render it impractical.  
```
Let \(\mathcal{S}_{U}\) be the set of unfulfilled inland container transportation requests;  Let \(flag\) denote the possibility of inserting an unfulfilled request into a route;  Initialize \(\mathcal{S}_{U}=\{C_{IF}\cup C_{OF}\cup C_{OE}\}\);  Initialize \(K_{truck}\) routes;  Initialize \(flag=1\); while\(\mathcal{S}_{U}\
eq\varnothing\) and \(flag=1\)do for\(r=1,2,...,K_{truck}\)do for\(i\in\mathcal{S}_{U}\)do ifrequest\(i\) can be inserted into truck route \(r\)then  Compute the travel cost increment\(\Delta_{ir}\) associated with request \(i\) and route \(r\); else \(\Delta_{ir}=+\infty\);  end if  end for \(\Delta=\min\limits_{(i,r)}\Delta_{ir}\); if\(\Delta<+\infty\)then \((i^{*},r^{*})=\arg\min\limits_{(i,r)}\Delta_{ir}\), and then insert request \(i^{*}\) to the tail of route \(r^{*}\); \(\mathcal{S}_{U}=S_{U}\backslash i\); else \(flag=0\);  end if  end for Output: The obtained routes.
```  
**Algorithm 2**Greedy heuristic.
## 4 Performance Analysis
### Diversity Order  
Diversity order provides how fast the outage curves fall as \(L\) increases in the probability of outage. In order to offer insights into the derived outage probability expressions, the diversity order for both the SUE and WUE is determined using [29] as follows:  
\[D_{s}=-\underset{\rho\rightarrow\infty}{lim}\left(\frac{\log_{2}P_{s}}{log_{ 2}\rho}\right) \tag{29}\]  
\[D_{w}=-\underset{\rho\rightarrow\infty}{lim}\left(\frac{\log_{2}P_{w}}{log_{ 2}\rho}\right) \tag{30}\]  
Substituting (27) in (29) and (26) in (30) gives  
\[D_{s}=\frac{L^{2}}{4}\left(\frac{\pi^{4}}{\left(16-\pi^{2}\right)^{2}}\right) \tag{31}\]  
\[D_{w}=min\left\{\frac{1}{2}\frac{\pi^{2}L}{16-\pi^{2}},\frac{1}{1.386}\frac{ \pi^{2}L}{16-\pi^{2}}\right\} \tag{32}\]