esa-sceva/satcom-chunk-collection huggingface dataset:
# Carbon Dioxide Uptake by Cement-Based Materials: A Spanish Case Study
## 6 Results and Discussion
### Maximum Amount of Carbon Dioxide that Can Be Absorbed
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Main Types** & **Type Name** & **Type Notation** & **Clinker, K** & **Minor Additional** & **Factor** & **Factor** & **CO\({}_{2,\text{max}}\)** \\  & & & & **Consititents** & **(K\({}_{\text{remrem}}\)/K\({}_{\text{CEM}}\))** & **(K\({}_{\text{remrem}}\)/K\({}_{\text{CMM}}\))** & **kg CO\({}_{2}\)/K\({}_{\text{remrem}}\)** \\ \hline \multirow{2}{*}{CEM 1} & Portland cement & CEM I & 95-100 & 0-5 & 95\% = 1 & 1 & 490 \\ \cline{3-8}  & Portland slag cement & CEM II/A-S CEM II/B-S & 80-88 & 0-5 & 80\%-95-905 & 0.84-0.99 & 410-480 \\  & Portland slag cement & CEM II/A-D & 65-79 & 0-5 & 65\%-7905 & 0.68-0.83 & 340-410 \\ \cline{2-8}  & Portland/silica fume cement & CEM II/A-D & 90-94 & 0-5 & 90\%-94/95 & 0.95-0.99 & 460-480 \\ \cline{3-8}  & & CEM II/A-P & 80-94 & 0-5 & 80\%-94/95 & 0.84-0.99 & 410-480 \\ \cline{3-8}  & Portland pozzolama cement & CEM II/B-P & 65-79 & 0-5 & 65\%-79/95 & 0.68-0.83 & 340-410 \\ \cline{3-8}  & & CEM II/A-Q & 80-94 & 0-5 & 80\%-94/95 & 0.84-0.99 & 410-480 \\ \cline{3-8}  & & CEM II/B-Q & 65-79 & 0-5 & 65\%-79/95 & 0.68-0.83 & 340-410 \\ \cline{3-8}  & Portland-fly ash cement & CEM II/A-W & 80-94 & 0-5 & 80\%-94/95 & 0.84-0.99 & 410-480 \\ \cline{3-8}  & & CEM II/B-V & 65-79 & 0-5 & 65\%-79/95 & 0.68-0.83 & 340-410 \\ \cline{3-8}  & Portland-fly ash cement & CEM II/A-W & 80-94 & 0-5 & 80\%-94/95 & 0.84-0.99 & 410-480 \\ \cline{3-8}  & & CEM II/B-W & 65-79 & 0-5 & 65\%-79/95 & 0.68-0.83 & 340-410 \\ \cline{3-8}  & Portland-burnt shale cement & CEM II/A-T & 80-94 & 0-5 & 80\%-94/95 & 0.84-0.99 & 410-480 \\ \cline{3-8}  & & CEM II/B-T & 65-79 & 0-5 & 65\%-79/95 & 0.68-0.83 & 340-410 \\ \cline{3-8}  & & CEM II/A-L & 80-94 & 0-5 & 80\%-94/95 & 0.84-0.99 & 410-480 \\ \cline{3-8}  & Portland limestone cement & CEM II/B-L & 65-79 & 0-5 & 65\%-79/95 & 0.68-0.83 & 340-410 \\ \cline{3-8}  & CEM II/A-L & 80-94 & 0-5 & 80\%-94/95 & 0.84-0.99 & 410-480 \\ \cline{3-8}  & & CEM II/B-L & 65-79 & 0-5 & 65\%-79/95 & 0.68-0.83 & 340-410 \\ \cline{3-8}  & Portland composite cement & CEM II/A-M & 80-88 & 0-5 & 80\%-94/95 & 0.84-0.99 & 410-480 \\ \cline{3-8}  & & CEM II/B-M & 65-79 & 0-5 & 65\%-79/95 & 0.68-0.83 & 340-410 \\ \hline \multirow{3}{*}{CEM III} & \multirow{3}{*}{Blast furnace cement} & CEM III/A & 35-64 & 0-5 & 35\%-64/95 & 0.37-0.67 & 180-330 \\ \cline{3-8}  & & CEM III/B & 20-34 & 0-5 & 20\%-34/95 & 0.21-0.36 & 100-180 \\ \cline{3-8}  & & CEM IIUC & 5-19 & 0-5 & 59\%-19/95 & 0.05-0.20 & 30-100 \\ \cline{3-8}  & & CEM IV/A & 65-89 & 0-5 & 65\%-89/95 & 0.68-0.94 & 340-460 \\ \cline{3-8}  & & CEM IV/B & 45-64 & 0-5 & 45\%-64/95 & 0.47-0.67 & 220-330 \\ \hline \multirow{3}{*}{CEM V} & \multirow{3}{*}{Composite cement} & CEM V/A & 40-64 & 0-5 & 40\%-64/95 & 0.42-0.67 & 210-330 \\ \cline{3-8}  & & CEM V/B & 20-38 & 0-5 & 20\%-38/95 & 0.21-0.40 & 100-200 \\ \hline \end{tabular}
\end{table} 
Table 2: Maximum amount of carbon dioxide, CO\({}_{2\text{max}}\), that the different cements (EN 197-1:2011) can absorb.
While this approach allows the model to learn the specific characteristics of buildings in the target area, it does not effectively inherit the generalization knowledge learned from the pretrained model on public datasets. Knowledge distillation techniques [42; 43] offer a solution by pretraining a teacher neural network on a large dataset. During the training process, the knowledge learned by the teacher network is utilized to guide the student neural network, thereby improving the accuracy of the student network. For example, [PERSON] et al. addressed the problem of insufficient labels in remote sensing scene classification by employing self-distillation to learn complex scene knowledge in diverse backgrounds [44]. [PERSON] et al. used knowledge distillation to extract visual knowledge in complex urban scenes [45]. By applying knowledge distillation, the model not only retains the generalization knowledge learned from the public datasets but also leverages the supervision from the limited samples in the target area to enhance the model's adaptability to diverse types of buildings. Therefore, this paper proposes the Building Polygon Distillation Net (BPDNet) to accurately extract building polygons in the target area with limited samples. The network consists of two structurally identical sub-networks known as the teacher network and the student network. The teacher network is trained on a large building polygon dataset that contains structurally diverse building samples, while the student network is trained on a small number of available samples from the target area. During the training process of the student network, the teacher network provides guidance, allowing the student network to access generalization knowledge. Furthermore, to address the issue of fuzzy, irregular, and fragmented building extraction caused by complex urban backgrounds, we employed the Dice Loss [46] to encourage the model to focus more on building boundaries. This helps alleviate the problem of building fragmentation caused by blurry boundaries. The primary contributions of this work can be summarized as follows:  
1. We propose BPDNet for extracting building polygons in the area with limited high-resolution remote sensing image samples. This network enables the model to inherit the generalization knowledge from the large dataset via knowledge distillation;
2. BPDNet employs Dice Loss to improve the extraction of buildings against the backdrop of complex urban context, characterized by fuzziness, irregularity, and connectivity issues. The paper is structured as follows: Section 1 introduces the background to the methodology and the problem addressed in this paper; Section 2 describes the study area, the external dataset used, and the process and methods used to process the data; Section 3 presents the details of the methodology; Section 4 presents the experimental design, parameter settings, and analysis of the results; Section 5 provides a discussion of the methodology; and, finally, the conclusions are presented in Section 6.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 6 Key Revocation  
Key revocation is a vital, but often overlooked [26] aspect of any IoT system. It is however simple to implement in the context of a C2 server-based approach to an IoT system. There are two types of revocation that need to be considered.  
1. The removal of a device as a subordinate to a C2 server such that it is no-longer subject to control by that server, whilst retaining the device within the wider system (this is the opposite of a join operation);
2. The permanent withdrawal of the device from the system as a whole (the opposite of initial device registration).  
To remove a device from a particular C2 network within SRUP, the device may elect to resign (by sending a SRUP_MESSAGE_TYPE_RESIGN_REQ message), or it may be terminated by the server (using a SRUP_MESSAGE_TYPE_TERMINATE_COM message).  
If the device is being disconnected by the server, the server would simply remove the record pertaining to the device from its list of devices. If the device is requesting to resign, then the server should send a response message with a status to indicate whether or not that resignation is accepted; and (if it is) the key would be removed as before.  
For a permanent (system-wide) de-registration of a device, SRUP could also be used (despite the fact that the initial registration took place outside of the protocol). The data exchange is essentially the same as for the resign/terminate use-case, but with message types of SRUP_MESSAGE_TYPE_DEREGISTTER_REG, or SRUP_MESSAGE_TYPE_DEREGISTTER_COM; the result of which would be the device's key being permanently removed from the system's key store.
###### Abstract  
Recently, filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) has received increasing attention from researchers, owing to its merits and superior spectral efficiency. High peak-to-average power ratio (PAPR) occurs in approximately all multicarrier systems, including FBMC/OQAM, and may cause bit-error-rate (BER) degradation if not appropriately handled. Conventional PAPR reduction methods for orthogonal frequency division multiplexing (OFDM), such as partial transmit sequence (PTS), selective mapping (SLM), and discrete Fourier transform (DFT) spreading, are ineffective in FBMC/OQAM because of the different structure of the symbols. This study proposes a novel method combining DFT spreading and PTS methods to reduce the PAPR of FBMC/OQAM systems with reasonable computational complexity. Numerical results obtained from various computer simulations show that the proposed method achieves a noticeable enhancement in the PAPR performance of the FBMC/OQAM signal compared to other existing methods without affecting the BER performance. Further, the computational complexity analysis and BER performance of the proposed method are presented in comparison to typical existing methods. From our computer simulations, the proposed method reduces the PAPR by approximately 32.8% compared to that of the conventional methods, and the BER performance is improved by 25% with a high-power amplifier effect.  
Keywords:filter bank multicarrier with offset quadrature amplitude modulation; orthogonal frequency division multiplexing; peak-to-average power ratio; discrete Fourier transform; partial transmit sequence; bit-error-rate +
Footnote †: journal: Electronics
## 4 Classification Results and Discussion
### Comparison of Training Results with Different Data Volumes  
Using Python 3.9 scripts, the dataset was randomly split, ensuring a roughly equal distribution of samples for each class. Training and prediction were conducted using dataset sizes of 2500; 5000; 10,000; 20,000; and 35,000. The recognition results of each model without utilizing the improved CELMD preprocessing are presented in Figure 12, while those with improved CELMD preprocessing are depicted in Figure 13.  
The results indicate an overall upward trend in model performance with increasing dataset sizes. CNN and ViT network exhibit greater sensitivity to dataset size, showing significant increases in accuracy as the dataset grows. In contrast, Swin Transformer neural networks demonstrate a more gradual improvement trend, yet consistently outperform CNN and ViT network at each dataset size. However, this does not necessarily imply that the learning capacity of these two networks is stronger than that of the Swin Transformer. On the contrary, this phenomenon arises because the Swin Transformer network has a stronger ability to extract primary features than CNN and ViT neural networks. This enables it to learn more feature information from a smaller amount of data, thanks to its unique downsampling mechanism and SW-MSA mechanism, which outperform CNN's convolutional operations and ViT's W-MSA mechanism.  
Figure 11: Swin Transformer neural network confusion matrix: (**a**) Swin T model confusion matrix; (**b**) Swin S model confusion matrix; (**c**) Swin B model confusion matrix; (**d**) Swin L model confusion matrix.  
Figure 12: The trend of model accuracy changes with different volumes of unprocessed data.
## 4 Conclusions  
As suggested in our previous paper and as pointed out by this study, saturated bio-mixtures from biobed systems utilized for OMWW treatment not only do not require any special treatment before their final disposal but can also be exploited as a soil amendment. To this end, the effects of the used bio-mixture application in different proportions as a soil amendment on the physical and chemical properties of a medium-texture cultivated soil were investigated. The study demonstrated that soil supplementation with bio-mixture shows a high potential for the improvement of soil physical properties due to proper aeration and leaching, especially when used for heavy soils, as well as for the enhancement of soil fertility, regarding the rise of organic matter, cation exchange capacity, phosphorus and nitrogen. Furthermore, a sufficient amelioration of the second depth soil (20-40 cm) was observed due to soil particles movement downwards. Moreover, chemical analysis of the leachates intimated that the impact of selected amendments on groundwater quality was the minimum.  
The promising outcomes, combined with the fact that exhausted bio-mixtures' reuse follows the circular economy principles, may facilitate the arduous and intractable treatment of OMWW in Greece, given that its disposal would be an additional problem that an olive oil mill owner would have to deal with. Recycling bio-mixture is a win-win strategy to transform a potential environmental threat associated to its disposal in a valuable resource.  
Further, future research may be conducted on the treatment and utilization of exhausted bio-mixture by composting and vermicomposting to produce organic soil amendment as well so that it can be used in crop fertilization.  
Conceptualization: E.H. and V.K.; Methodology: E.H.; Formal analysis: V.K. and S.S.; Investigation: V.K. and E.H.; Resources: E.H., V.K. and A.G.; Data curation: V.K., S.S. and D.G.; Writing--original draft preparation: V.K. and E.H.; Writing--review and editing: V.K., E.H., A.G., E.K.O., S.S. and D.G.; Visualization: V.K. and E.H.; Supervision: E.H.; Project administration: E.H., A.G. and E.K.O.; Funding acquisition: E.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 8: Average B (mg kg\({}^{-1}\)), Fe (mg kg\({}^{-1}\)), Mn (mg kg\({}^{-1}\)), Zn (mg kg\({}^{-1}\)) and Cu (mg kg\({}^{-1}\)) concentrations and their standard deviations (SD) of samples of the four treatments (a) at 0–20 cm depth and (b) at 20–40 cm depth. Each value is the mean of three replicates. Different letters above each column within the same parameter evaluated, indicate significant differences.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors are thankful to [PERSON] and [PERSON] for their contribution to the preparation of the bio-mixture-soil mixtures and the construction and filling of the columns.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Detail-Preserving Shape Unfolding
## 5 Experimental Details
### Quantitative Metrics  
To demonstrate the effectiveness of the proposed mesh unfolding method, we quantitatively evaluate the quality of the resulting canonical poses. Specifically, the metrics are defined as  
\[E_{\text{r}ig}=\frac{1}{n}\sum_{i}\sum_{j\in\mathcal{N}_{i}}\frac{\|\mathbf{p} ^{\prime}_{i}-\mathbf{p}^{\prime}_{j}-\mathbf{R}_{i}(\mathbf{p}_{i}-\mathbf{ p}_{j})\|}{\|\mathbf{p}_{i}-\mathbf{p}_{j}\|}, \tag{8}\]  
and  
\[E_{\text{r}tr}=\frac{2}{n(n-1)}\sum_{i=1}^{n-1}\sum_{j>i}^{n}\frac{d^{\prime}_{ ij}-d_{ij}}{d_{ij}}, \tag{9}\]where, \(E_{rig}\) measures the accuracy of the local rigid approximation, \(E_{str}\) measures the mesh stretching between the initial mesh and the canonical pose, \(d^{{}^{\prime}}_{ij}\) and \(d_{ij}\) are the Euclidean distances between vertex pairs on the deformed and original mesh. From Equations (8) and (9) we can clearly see that an embedding with small \(E_{rig}\) and large \(E_{str}\) will have good rigidity and strong stretching ability, respectively.  
In addition, we also use the following metric to measure the similarity between the pairwise Euclidean distances and the corresponding geodesic distances at the deformed canonical mesh:  
\[E_{geo}=\|\frac{\mathbf{D}^{{}^{\prime}}}{\|\mathbf{D}^{{}^{\prime}}\|_{F}}- \frac{\mathbf{G}}{\|\mathbf{G}\|_{F}}\|_{F}, \tag{10}\]  
where, \(\mathbf{D}^{{}^{\prime}}_{ij}=d^{{}^{\prime}}_{ij}\) is the pairwise Euclidean distance and \(\mathbf{G}^{{}^{\prime}}_{ij}=g^{{}^{\prime}}_{ij}\) is the corresponding geodesic distance. For each mesh, its value close to 0 imply a good geodesic approximation as the difference is taken between unit matrices. Based on the Equations (8)-(10), we can use the empirical cumulative distribution function (CDF) to evaluate the performance of different algorithms on a whole dataset.
## 3 Quantification of Uncertain Information
### Motion Intention-Switching by Identifying the Actual Intention of the User
#### 3.3.3 Motion Intention Switcher (MIS)  
The desired position for the movement cannot be directly taken as the position referred from the gesture since the point referred from the gesture is not very accurate and typically it would not be the exact location that the user wants to navigate the robot. Moreover, the gesture instructions are often useful in enhancing the meaning of vocal instructions in human-robot interaction [22; 24]. Therefore, it is only used for altering the perceptive distance (\(D\)) to an alternative perceptive distance (indicated as \(D_{a}\) in Figure 4) from the default (i.e., \(D_{r}\)) by identifying the actual intention of the user. The assigning of alternative perceptive distance (\(D_{a}\)) for the perceptive distance (\(D\)) is done by MIS if required. The decision as to whether the perceptive distance has to be altered to an alternative (\(D_{a}\)) is decided based on a rule-based approach that evaluates \(D_{gesture}\) and \(D_{r}\).  
The procedure of assigning the perceptive distance \(D\), is given in Algorithm 1. \(\delta_{max}\) and \(\delta_{min}\) are scalar constants used in order to avoid the false triggering of the intention switching due to the less accurate \(D_{gesture}\). The alternative perceptive distance, \(D_{a}\), has two cases where the \(D_{a}>D_{r}\) and \(D_{a}<D_{r}\). If \(D_{a}>D_{r}\), then it is considered as \(D_{a,max}\) and if \(D_{a}<D_{r}\), then it is considered as \(D_{a,min}\). Moreover, the MIS shifts the perception of robot between the alternative and default hypotheses based on the defined thresholds that depend on the pointing gesture issued by the user and the layout of the surrounding environment.  
```
INPUT: \(D_{r}\), \(D_{gesture}\), \(D_{a}\) OUTPUT: \(D\) if\(D_{gesture}>\delta_{max}D_{r}\)then \(D=D_{a,max}\) elseif\(D_{gesture}<\delta_{min}D_{r}\)then \(D=D_{a,min}\) else \(D=D_{r}\) endif
```  
**Algorithm 1** Assigning perceptive distance (\(D\))
## 3 Experiment Setup and Results
### Power Splitting Ratio in the Si\({}_{3}\)N\({}_{4}\) Waveguide Based MMI Couplers  
We assess the power splitting ratio in the two output arms of the MMI couplers. Three test MMI couplers were designed such that all the MMI couplers have the same core waveguide width (\(W_{MMI}\)) of 30 \(\upmu\)m. The core waveguide lengths (\(L_{MMI}\)) of the three MMI couplers are varied from the first beat length (\(\sim\)1860 \(\upmu\)m found from simulation, Section 2.2). Figure 10 shows the normalized output transmission response of the MMI couplers. From this figure, it is seen that optical power splitting ratio within 0.3 dB power range is obtained for all the MMI couplers at both ports. The MMI coupler with \(L_{MMI}\) = 1860 \(\upmu\)m exhibits 0.4 dB loss per output channel at 1550 nm wavelength. The optical loss per channel increases to 0.8 dB and 1 dB for the MMI couplers with \(\pm\)10 \(\upmu\)m variation from the simulated optimum core waveguide length. The 0.4 dB to 1 dB insertion loss for the MMI coupler is still high, considering the low-loss Si\({}_{3}\)N\({}_{4}\) platform used to design the devices. This suggests that in future runs the MMI coupler should be designed more carefully by considering both the simulation and experimental result obtained from this run.  
Figure 9: **(a)** Optical transmission response of the test MZI filter at two different bias voltages to the heater, **(b)** Measured and calculated group index of the 72 nm thick Si\({}_{3}\)N\({}_{4}\) waveguide.
# Target Image Mask Correction Based on Skeleton Divergence
## 2 Materials and Methods
### Hole Filling  
An image that is merged using discrete pixel regions is affected by insufficient surface texture and noise. Image regions are prone to voids after mask correction. Hole filling presents great advantages in terms of post-image restoration accuracy and avoidance of accumulated errors [23]. The employed void-filling method is mainly based on structural similarity and the image hole filling of morphological features. The image is first subjected to regional morphological expansion processing, then connectivity analysis is conducted. Subsequently, the regularization constraints are combined to recover the cavity information. In addition, skeleton and boundary information increases the overall structural constraints for hole filling, improving the accuracy of hole filling.
## 3 Methodology
### NPP Estimation
#### 3.1.3 Model Validation  
To assess the performance and certify the improvement of the AVPM, this paper validated the AVPM and MOD17 per year from 2012 to 2019. Three indices were computed, including bias, mean absolute percent error (MAPE), and Pearson's correlation coefficient (\(r\)). The bias measures how a modeled value deviates from the observed value (Equation (25)). MAPE is the mean absolute percent error of the prediction errors, which explains how concentrated the data are around the observed data (Equation (26)). The \(r\) was bounded with its value ranging from \(-1.0\) to \(1.0\), which provides some information about the interannual variation or the consistency of variation between the simulated and observed NPP values (Equation (27)). The equations are as follows:  
\[\text{Bias}= \frac{1}{\text{n}}\sum_{i=1}^{\text{n}}(\text{y}_{\text{i}}-\text{x} _{\text{i}}) \tag{25}\] \[\text{MAPE}= \frac{1}{\text{n}}\sum_{i=1}^{\text{n}}|\,\text{y}_{\text{i}}- \text{x}_{\text{i}}|\times 100\] (26) \[\text{r}=\frac{\sum_{i=1}^{\text{n}}(\text{x}_{\text{i}}-\overline {\text{x}})(\text{y}_{\text{i}}-\overline{\text{y}})}{\sqrt{\sum_{i=1}^{\text {n}}(\text{x}_{\text{i}}-\overline{\text{x}})^{2}\sum_{i=1}^{\text{n}}(\text{ y}_{\text{i}}-\text{y})^{2}}} \tag{27}\]  
where n is the number of samples, x\({}_{\text{i}}\) and y\({}_{\text{i}}\) represents the observed and estimated values, and i is the serial number ranging from 1 to n.