esa-sceva/satcom-chunk-collection huggingface dataset:
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
## 4 Evaluation  
This section describes our experiments' results designed to evaluate our approach. For this evaluation, we established the three research questions below and carried out three experiments in response to research questions.  
* _RQ1: How much can the scalability of PBFT be increased through our proposed approach?_
* _RQ2: What is the correlation between the trouble transactions and performance?_
* _RQ3: How much can our approach improve the scalability of IBFT of Hyperledger Besu?_
## 4 Empirical Assessment Using Real-World Data
### Experimental Design
#### 4.2.2 Regression Stage
For the ZTNB model, the probability mass function (PMF) is adapted to account for the absence of zero counts, and it is defined as follows:  
\[\mathrm{P}(Y=y|Y>0)=\frac{P(Y=y)}{1-P(Y=0)} \tag{27}\]  
Incorporating the condition that \(y>0\) into the negative binomial PMF and substituting the expression for \(\mathrm{P}(\mathrm{Y}=0)\) from the NB distribution, we can obtain the PMF of the ZTNB model:  
\[\mathrm{P}(Y=y|Y>0)=\frac{\frac{\Gamma(y+k)}{\Gamma(k)y!}\left(\frac{\lambda}{ \lambda+k}\right)^{y}\left(\frac{k}{\lambda+k}\right)^{k}}{1-\left(\frac{k}{ \lambda+k}\right)^{k}}\quad for\ y=1,2,3,\ldots \tag{28}\]  
This adjusted PMF for the ZTNB model removes the probability weight of \(y=0\) and reallocates it across the positive counts, effectively accommodating the overdispersion and absence of zeroes in the crash data. The evaluation of the predictive models in the regression stage is conducted using two statistical metrics: Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE). These metrics are widely used for comparing the accuracy of models by quantifying the differences between the values predicted by the models and the observed values from the generated data. RMSE is a measure of the average magnitude of the errors between the predicted and actual values, giving higher weight to larger errors. It is defined mathematically as the square root of the average of squared differences between prediction and actual observation. The RMSE for \(n\) predictions is computed as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}} \tag{29}\]  
where \(y_{i}\) represents the actual crash counts, and \(\hat{y}_{i}\) represents the predicted crash counts for the \(i\)-th observation out of \(n\) total observations. MAE, on the other hand, measures the average of the absolute errors between the predicted and the actual values. Unlike RMSE, MAE treats all errors equally, providing a straightforward measure of prediction accuracy. It is expressed as follows:  
\[\text{MAE}=\frac{1}{n}{\sum_{i=1}^{n}}|y_{i}-\hat{y}_{i}| \tag{30}\]  
MAE is particularly insightful because it is in the same unit as the data being predicted--in this context, the number of crashes. Both RMSE and MAE are valuable metrics for model evaluation in this study. RMSE is sensitive to outliers, as it tends to penalize larger errors more severely, while MAE provides a simple average error magnitude. Together, they offer a comprehensive assessment of the model's prediction performance, with RMSE emphasizing the model's accuracy across the entire range of data, and MAE providing a measure of central tendency in the prediction errors.
## Appendix A Cellular Network Technologies
### 2G Global System for Mobile Communications (GSM)  
In the early 90s, the Global System for Mobile Communications (GSM), also referred to as the second generation (2G) of cellular networks, significantly changed the way people communicated, as proven by the billions of subscribers. GSM was first designed and developed in the 80s for voice and data communication using circuit switching only, and then extended with packet-switched functionalities to better fit discontinuous data mobile applications (such as Internet browsing). This was done by introducing the General Packet Radio Service (GPRS), and then the improved Enhanced Data rates for GSM Evolution (EDGE), which exhibited faster transfer rates.
## 4 Methodology
### Methodological Significance and Implications  
The methodology demonstrates statistical robustness through  
* Utilization of quartile-based boundaries, which are resistant to outliers;
* Transformation of continuous variables into categorical classifications, reducing sensitivity to measurement noise;
* Employment of non-parametric distance metrics, avoiding assumptions about data distribution.  
The approach enhances interpretative accessibility by
* Converting abstract numerical values into semantically meaningful categories;
* Providing visual representations of categorical trajectories;
* Quantifying similarities and differences through intuitive distance metrics.  
The prototype implementation reveals several conceptual insights:  
* Pattern Recognition: The methodology effectively distinguishes between different evolutionary patterns (monotonic increase, monotonic decrease, and random fluctuation).
* Temporal Coherence: The approach identifies temporal coherence (or lack thereof) between different entities' trajectories.
* Dimensional Reduction: By transforming continuous variables into categorical classifications, the methodology achieves dimensional reduction while preserving essential pattern information.
* System Dynamics: The aggregate distance measures provide insights into overall system dynamics and the degree of heterogeneity in evolutionary trajectories.  
**Country-Specific Weight Implementation:** For each country pair \((i,j)\), the weighted distance becomes  
\[d_{WH}(T_{i},T_{j})=\sum_{t=1963}^{2022}w_{t}\cdot|c_{i,t}-c_{j,t}|\]  
where \(c_{i,t}\) represents the categorical debt level for country \(i\) in year \(t\), and \(w_{t}\) represents the temporal weight assigned to year \(t\). This formulation ensures that the weighting scheme is applied consistently across all country comparisons while allowing for different temporal emphasis based on analytical objectives.
# Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering
## 3 Modular Software Architecture Definition
### Class Structures
#### 3.2.3 Device Level Class Structure for Firmware  
Firmware development followed the same principles of modularity and concurrent development as the higher levels. Previous successful experiences from the hardware development team in modular, OOP-based architectures running on microcontrollers [61] supported this approach, and established the starting point for the device level class structure design. Figure 7 presents a simplified class diagram that implements an adapted version of the modular class models developed for upper levels of the architecture. Here, an abstract class Device serves a similar purpose to Instrument class of Figure 6, as functionality is implemented by deriving devices. In this case, calling of devices is in charge of the class Device Manager, which instantiates every device and handles execution of its inherited methods. Such methods are device_setup, similar to Instruments Init, this is meant to be executed once at startup and contains routines to initialize devices, and device_loop, which contains device routines that have to execute periodically.  
In this design, external communication with the main vehicle processor is handled by Command class, which is instantiated once in global scope, and used by Device Manager to enable interaction of devices with the rest of the ROV. As an internal communication channel between devices, Command uses the Internal Command class, which is a simplified and lighter version of the former.  
Regarding specific devices, the ones displayed in Figure 7 actually represent a collection of devices. SC_Devices is a derived class which implements functionality of internal SmartCard devices such as temperature, humidity and pressure sensor, status indicator, voltage, and current sensor. POWER, CPU, and SAMPLER group distinguishdevices according to each box, e.g., thruster signal generation is performed in a device class that is only implemented in POWER Box. Detailed firmware class structure exceeds the scope of this document.
## 4 Results and Analysis
### Weight Variation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Effect** & **Coeff.** & **SE Coeff.** & **f** & **\(p\)** \\ \hline Const. & - & 0.05403 & 0.008133 & 6.64 & 0.000 \\ A & \(-\)0.04508 & \(-\)0.02254 & 0.008133 & \(-\)2.77 & 0.013 \\ C & 0.04427 & 0.02213 & 0.008133 & 2.72 & 0.019 \\ AC & \(-\)0.06796 & \(-\)0.03398 & 0.008133 & \(-\)4.18 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Effects and estimated coefficients for the weight variation with the meaningful parameters. Figure 12: Optimal parameters for the jetting dispenser. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Effect** & **Coeff.** & **SE Coeff.** & **\(t\)** & **\(p\)** \\ \hline Const. & - & 0.05403 & 0.005927 & 9.12 & 0.000 \\ \hline A & \(-\)0.004508 & \(-\)0.002254 & 0.0005927 & \(-\)3.80 & 0.013 \\ B & \(-\)0.01808 & \(-\)0.00904 & 0.005927 & \(-\)1.53 & 0.188 \\
**C** & **0.04427** & **0.02213** & **0.005927** & **3.73** & **0.014** \\ \hline D & \(-\)0.01883 & \(-\)0.00941 & 0.005927 & \(-\)1.59 & 0.173 \\ AB & \(-\)0.01528 & \(-\)0.00764 & 0.005927 & \(-\)1.29 & 0.254 \\ AC & \(-\)0.06796 & \(-\)0.03398 & 0.005927 & \(-\)5.73 & 0.002 \\ AD & \(-\)0.01509 & \(-\)0.00755 & 0.005927 & \(-\)1.27 & 0.259 \\ BC & 0.02088 & 0.01044 & 0.005927 & 1.76 & 0.138 \\ BD & 0.02119 & 0.01059 & 0.005927 & 1.79 & 0.134 \\ CD & 0.02110 & 0.01055 & 0.005927 & 1.48 & 0.135 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Effects and estimated coefficients for the weight variation.
# Effectiveness of Collagen Membrane in the Treatment of Schneiderian Membrane Perforation
## 1 Introduction  
With the increased popularity of dental implant therapy to replace missing teeth, the need has arisen for a reliable method to provide patients with bony support for these implants in cases where the alveolar ridge volume is insufficient for implant placement. In particular, bone remodeling following tooth extraction or trauma and maxillary sinus pneumatication often create a clinical challenge for dental implant placement in the posterior maxilla. First introduced by [PERSON] [1] and then published by [PERSON] and [PERSON] [2], sinus lifting has become a predictable procedure in the rehabilitation of vertical bone deficiency in the posterior maxillary region [3].  
Although the maxillary sinus lift procedure prior to dental implant placement is a predictable and safe procedure, complications such as Schneiderian membrane perforation (SMP), excessive bleeding, and infection have been reported during the procedure and may influence the outcome of treatment [4]. The most common complication is SMP. According to previous studies, the weighted average for the incidence rate of SMP was 23.5%, ranging from 3.6% to 41.8% [5]. [PERSON] and [PERSON] [6] indicated that SMPs are not considered a reason to discontinue sinus lift procedures but should be addressed by properly isolating and repairing the SMP. Repair could include folding of the sinus membrane itself, covering the SMP with an absorbable membrane, or careful suturing. As an alternative, the use of fibrin adhesive (FA) for repair of perforations has been advocated [4; 7].  
Among these repair methods, the collagen membrane (CM) is the most widely used, but there is little research on the effectiveness of CMs in the management of SMPs during maxillary sinus lift procedures [8]. This study aimed to evaluate the clinical outcomes of SMP repair using a CM when such an SMP had occurred during a sinus lifting procedure.
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 1 Introduction
### Objective and Organization of the Work  
In this study, a multi-frequency low-cost receiver ZED-F9P and u-blox GNSS antenna ANN-MB-00 were analyzed. GNSS data were obtained in favorable conditions in open-sky environment where reduced impact of multipath is expected. The main objective is to evaluate the positional quality of the low-cost receivers and to identify the minimum level of displacements that can be detected with such inexpensive instruments.  
The paper is structured as follows: firstly, an overview of the usage of single- and double-frequency, low-cost receivers for geodetic monitoring is described (Section 1). Then, the configuration and connection of the simpleRTK2B board with OpenLog are shown. Basic statistics are adopted to evaluate the positional quality of ZED-F9P, hypotheses are tested to define the range of displacements that can be detected (Section 2). Obtained results are presented and discussed (Section 3). Lastly, conclusions and remarks are presented (Section 4).
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 3 Experimental Results
### Impact of Segmentation Scale  
In order to divide HSI into appropriate superpixels by using the improved SLIC algorithm, the segmentation scale needs to be pre-specified. The rise of segmentation scale means the increase of superpixel volume. Figure 4 shows that the classification accuracies of three datasets vary with the increase of segmentation scale \(s\). For Indian Pines dataset, the best OA reaches 97.52% when \(s=5\); then the OA values drop sharply when \(s\) is greater than 7. In this dataset, the small volume of several classes, the spectral similarity between classes, and the spatial proximity of classes lead to the fact that a large superpixel likely contains different classes. As a result, the probability of misclassification for these superpixels increases greatly in the process of classification. The OA value on Salinas dataset shows a slow upward trend with the segmentation scale \(s\) varying from 3 to 13, and then decreases slowly. The reason is that superpixels with the right size can play a better role in denoising. Furthermore, high classification accuracy also explains the rationality of defined similarity between two superpixels. When the segmentation scale \(s\) is less than 13, the OA value of University of Pavia dataset is almost 100%. The reasons could be: (i) This dataset has a high spatial resolution; (ii) classes with similar spectral feature are not adjacent in space, for example, class Broccoligreenweed1 and class Broccoligreenweed2, as shown in Figure 3b. Superpixel technology is just good at dealing with this situation. For this dataset, we take s = 7 in our experiment. According to the above analysis, the classification results reported in the following subsection are obtained with a choice of the scales that is based on the reference ground truth that is not available in general.
# ReZNS: Energy and Performance-Optimal Mapping Mechanism for ZNS SSD
## 3 Design and Implementation
### Example  
Now, we explain how ReZNS works by showing an example (see Figure 2). In this example, we suppose that a zone on ZNS SSD consists of ten flash blocks, which include hundreds of flash pages, and ReZNS is enabled (square means one flash block in Figure 2). As shown in Figure 2), three zones, Zone0, Zone1, and Zone2, were already assigned to App1, App2, and App3, respectively. After that, App1 was terminated without triggering a zone-reset command after writing its data, which is enough to fill about four flash blocks of Zone0 (Step ). Then, App3 called upon the zone-reset command for Zone2 after using just three flash blocks with write operations (Step ). In this case, ReZNS inserts Zone2 into the _spare-list_ to reuse it in the future as its remaining capacity is 70% (Step ). After 30 min, App2 starts to work and it consumes nine flash blocks to store its data (Step ). Next, the daemon of ReZNS inserts Zone0, which has not used for one hour, into the _spare-list_ by identifying it as a _zombie zone_(Step ).  
Now, let us consider that App2 is going to consume nine flash blocks again (Step ). In this case, App2 first uses the last flash block belonging to Zone1 because it is available. After that, it tries to create a new zone because more flash blocks are needed in practice. At this time, since the _spare-list_ includes two Rzones, ReZNS assigns Zone2 to App2 to recycle its remaining capacity again instead of making a new zone. Note that Zone2 has a high priority in the list because the remaining capacity for Zone2 and Zone0 is 70% and 60%, respectively. Then, App2 resumes a series of write operations from the second block to the end block (Step ). Meanwhile, App2 never recognizes that it uses the remaining space of Zone2 (i.e., 70%); thus, ReZNS should add three flash blocks to provide ten flash blocks per zone, consistently. To do so, ReZNS assigns Zone0 to App2 once again when free space in Zone2 is insufficient and then records data belonging to the following 9 blocks in Zone0 (Step 1).  
In summary, ReZNS can prevent the use of the costly zone-reset command caused by Step 1 and improve space efficiency by reusing eight flash blocks across Zone2 and Zone0 (Step 1-Step 2). We believe this is very meaningful in that ReZNS reduces the overall energy consumption in storage devices.
## 4 Discussion
### Impact of Fragmentation on Edge and Core Forests  
In this study, differences in aboveground biomass, net primary productivity, gross primary productivity and leaf area index were analyzed between the edge and core areas (forest stand level). Except for net primary productivity, higher values were found in the core than in the edge forest areas. We assume that most of these observations result from edge effects. Edge effects occur due to an altered microclimate (e.g., wind or temperature), altered species composition or increased mortality in forests [10; 11; 12]. This leads to a reduction of biomass in forest edge areas. It is known that forest fragmentation also alters the dynamics in the edge area of forests [5; 11; 22; 35; 36; 37; 38]. [PERSON] et al. [1] found that the aboveground biomass in large fragments was reduced by up to 14.7%. For small fragments, the biomass was even reduced by 68.5%. In our study, the fragment sizes were not considered, but we obtained an average of 27% lower values for biomass in the edge areas than in the core areas. [PERSON] et al. [13] found that the biomass in the first 500 m of a forest fragment is on average 25% lower than in the forest core using optical satellite measurements (with limitation due to saturation effects).  
Surprisingly, in our analysis, the net primary productivity in the edge area is 13% higher than in the core area, although the biomass is lower at the edge [39; 40]. This can be explained, for example, by a change in species composition: productive pioneer species can dominate in the forest edge.  
Please note that reductions in biomass and productivity depend not only on edge effects, but that there is also a history of fragmentation. It is often not known how long ago a deforestation event occurred. Depending on how long the edge effects have been acting, the differences in biomass can be larger or smaller. A decrease in biomass after deforestation can take up to 40-60 years [10]. The lidar measurements used in this study give us just one snapshot in time. In addition, other factors can influence the biomass or productivity at the edge of forests. These include, for example, site factors (such as climate and soil properties) or elevation (topology).
## 4 Results and Discussions
### Reliability Analysis  
The grouped TBF data of self-starting, fuel supply and cooling subsystems are identified as not independently and identified distributed. The TBF data of the air supply and the lubrication subsystems were distributed independently and evenly. The MLE method is used to estimate the reliability and MTBF. PLP model is used for reliability estimation of subsystems having non-IID data. The reliability is estimated at an arbitrary value after 1000 h (for comparison) and also Mean Time Between Failure (MTBF) is calculated. Table A1 shows the values of reliability and MTBF for all five subsystems (See Appendix A).  
Figure 4: (**a**). Trend test of TBF data for air supply subsystem; (**b**). Trend test of TBF data for self-stating subsystem; (**c**). Trend test of TBF data for fuel supply subsystem; (**d**). Trend test of TBF data for lubrication subsystem and (**e**). Trend test of TBF data for cooling subsystem.  
Figures 6 and 7 show the value of reliability and MTBF for all five subsystems. The value of reliability is highest for fuel supply subsystems and lowest for self-starting subsystems. The lowest MTBF value is of Self-starting subsystem which is 1186.47 h and the highest is of the air supply subsystem which is 1525.50.  
Figure 5: (**a**). Serial correlation test of TBF data for air supply subsystem; (**b**). Serial correlation test of TBF data for self-starting subsystem; (**c**). Serial correlation test of TBF data for fuel supply subsystem; (**d**). Serial correlation test of TBF data for lubrication subsystem and (**e**). Serial correlation test of TBF data for cooling subsystem.
# Locating the Epidemic Source in Complex Networks with Sparse Observers
## 3 Experiments and Analysis
### Source Localization Accuracy in Synthetic Networks
The recovery probability used in the simulation is \(\gamma=0.1\) and the number of observers is \(K=10\), which are same in Figure 3.  
The relationship between source localization performance and reporting probability of different source localization algorithms in two kinds of tree networks is shown in Figure 3. It is easy to see that PESL algorithm is still the most competitive source localization algorithm. Especially when the infection probability \(\beta\) is very small, the performance of PESL algorithm is significantly better than other comparison algorithms, indicating that PESL algorithm can well deal with the influence of randomness in the spread process. As can be seen from Figure 3c and Figure 3f, when the infection probability \(\beta\) is large, the randomness in the spread process decreases significantly and at this time, PESL algorithm cannot well reflect its advantages. On the other hand, with the same infection probability, the larger the reporting probability \(\delta\) is, the closer the observers will be to the epidemic source, the less randomness brought by the observer will be and the gap between PESL algorithm and other algorithms will be further narrowed. In general, the results in Figure 3 indirectly indicate the applicability of PESL algorithm: as reporting randomness of source localization problem increases, the advantages of PESL algorithm will be more obvious compared with other algorithms. The relationship between the performance of source localization algorithm and the number of observers is shown in Figure 4. In the visual impression of people, the more the number of observers is, the more accurate the source localization accuracy will be. However, the results in the figure are not like this: there is no obvious tendency between source localization accuracy of GAU, TRBS, PESL, and the number of observers; The localization accuracy of DC and JC declines as the number of observers increases. This means the information provided by observers in network has a lot of redundancy, as shown in Figure 1. Because node \(e\) reports the infected time after node \(i\), and node \(g\) only can be infected by node \(e\), even if we have known the infected time of node \(g\), it has no ability to provide any useful information for source localization, which is called information redundancy. In a tree network, as the number of observers increases, a great source localization algorithm should perform as stably as possible. From this point of view, PESL algorithm has the strongest stability, i.e., it behaves best. Figure 4: The relationship between source localization accuracy and the number of observers in tree networks. (**a**,**b**) represent the relationship between average error distance and the number of observers in ERT under \(\beta=0.2\), \(\delta=0.6\) and \(\beta=0.5\), \(\delta=0.3\) respectively. (**c**,**d**) represent the same results in BAT. The abscissa represents the number of observers \(K\).