esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Dispersal of Natural and Biomimetic Maple Samaras
## 3 Experimental Methods
### Field Experiments  
For a more realistic test of the samaras' descent performance, experiments were conducted in field conditions. Samaras were dropped from an aerial work platform over an asphalt airstrip located at the Kentland Experimental Aerial Systems Laboratory at Virginia Tech's Kentland Farm in Blacksburg, VA 24060, USA (Figure 4c). This site was selected because the paved area and the land in the immediate vicinity are flat, with no obstructions. As in the still air experiments, samaras were released from rest from a height of \(h=3.05\) m with the blade pointing downward and allowed to freely descend to the ground. The location of their first contact with the ground was marked with a colored metal disk so that no additional lateral sliding movement along the ground would occur.  
Each natural Norway maple seed was dropped 2 times and each 3D-printed Norway maple seed and 3D-printed silver maple seed was dropped 3 times from the platform. In this experiment, the repeated trials were conducted on different days to account for potential variability in wind conditions. Wind conditions were approximated by taking an average for the entire trial duration, which was typically 3 h per trial, on three different days. Wind speed and direction were measured at an approximate height of 3 m at a  
Figure 3: (a) Results from laboratory drop experiments in still air. The curve represents the descent path of the geometric centroid of the samara (not to be confused with the center of mass). From its rate of descent, the descent speed is calculated. From the oscillations, the rotational velocity is calculated. A natural (left) and artificial (right) Norway maple are shown. We have overlaid snapshots of the samaras. (b) The mean descent speed (\(v_{d}\)) and rotational velocity (\(\Omega\)) for each trial of still air drop experiments. Natural Norway maple seeds are represented by orange circles, artificial Norway mapsle are represented by green triangles, and artificial silver mapsle are represented by yellow inverted triangles, as shown in the legend. The overall mean and standard deviation of each samara type are shown by a mean point with red error bars, with shapes corresponding to each samara type. (c) We show the descent speed vs. samara tip velocity (\(R\Omega\)) for the natural and artificial Norway maple. Some curves of constant descent speed to tip velocity ratio, \(v_{d}/R\Omega\), are shown.  
weather station located approximately 100 m from the drop location. The wind conditions were variable. On the first day, artificial silver maple seeds were dropped in low wind conditions (\(\bar{u}_{w}=0.95\) m/s) and artificial Norway maple seeds were dropped in higher wind conditions (\(\bar{u}_{w}=3.69\) m/s). On the second and third days, all samaras were dropped together, with one low (\(\bar{u}_{w}=0.96\) m/s) and one higher (\(\bar{u}_{w}=3.47\) m/s) wind day. The wind conditions for each drop period are shown in Figure 4b.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 3 Issues Faced by RPL during Mobility
### Loops in the Network  
In RPL, the current rank of the mobile node is set to infinity when it disconnects from its parent. This allows the mobile nodes to attach with neighboring nodes even with those that have lower ranks (any rank is less than infinity). This mechanism of RPL results in a closed-loop if the detached mobile node selects the new parent that was previously its child [46].
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
## 3 Results
### Measurements under Rotation  
When the antennas are replaced by the RF couplers, a parasitic passive component (either inductor or capacitor) is introduced in the link budget. When the measurements are made under static conditions, the effect of this parasitic component is not noticed; however, if the measurements are made under rotation, the influence of this component in the link budget may cause introduce some frequency pulling. This effect cannot be accurately accounted for because it would require the knowledge of the couplers coupling parameters at the exact moment the interrogation unit is acquiring the data. Thus, in order to evaluate the impact of the rotation on the accuracy of the measurements, the statistical procedure described in Section 2.5 was applied.
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 4 Simulation and Discussion
### Fuzzy-PI Controller Driven Drive
#### 4.4.1 Starting Characteristics  
The characteristics of fuzzy-PI will be in between the PI controller and the fuzzy controller. The motor attains a steady state speed of 1400 rpm during starting without any load torque. The torque rises to 10 nm (7 nm for PI, 12 nm for Fuzzy Controller) and settles to its reference value close to 0.6 nm. The percentage overshoot at start with no load is 4.9%, (22.5% for PI, \(-\)0.07% for fuzzy controller); the respective peak time, rise time, and settling time are 0.1599 s, 0.0993 s, and 0.1778 s, respectively. The simulation performance at start is the same during step and linear loading conditions.
# Impact of COVID-19 Mobility Changes on Air Quality in Warsaw
## 3 Results  
### Changes in Mobility during COVID-19  
The analysis of the trip matrices confirmed the noticeable impact of the pandemic on the number of trips made during its different phases and in comparison to the reference period (spring 2019). According to the scope of the IDUB research project, comparisons of mobility data were carried out for trips generated in Warsaw and between Warsaw and its metropolitan area (WMA), the region of the country of which Warsaw is a part (Mazovia), the rest of Poland and abroad (Figure 2). Compared to the average daily number of trips in the individual pandemic phases of 2020 against 2019 (Table 1), it was noted that the first and second phases saw a 24-25% reduction in the number of trips made within Warsaw. In the third phase, the reduction in the number of trips compared to 2019 was already much smaller at 9%, which means that after a period of severe mobility restrictions, in May it began to gradually return to its pre-pandemic state. The number of trips from Warsaw to the area around Warsaw (WMA) decreased in relation to the pre-pandemic period by 32%. In the second phase (in April), it decreased compared to the pre-pandemic period by 28%. So, comparatively, it can be seen that the reduction in mobility in March (self-limitation of society) was stronger than in April (administrative restrictions). In May, during the exit from the pandemic, the reduction was definitely less significant-4%, and indicates a need for pre-pandemic behaviour. Figure 1: Traffic air quality monitoring station in Al. Niepodleglosci in Warsaw, located directly next to the roadway. Figure 2: Hierarchy of analysis of the trips related to Warsaw: (**A**)-from abroad, (**B**)-domestic, (**C**)-from the Mazovia region, (**D**)-from the Warsaw Metropolitan Area (WMA). In the first and second phases of the pandemic, a significant reduction in the number of long trips to Warsaw from the rest of the country (by 74-76%) and from abroad (by 60-61%) was observed. In the third phase, the reduction was smaller, at 53% for domestic trips and 56% for international trips, which also confirms the gradual return to pre-pandemic mobility. The data indicate a strong reduction on mobility in a large city, inside Warsaw and in trips related to Warsaw. A different situation occurred in the case of trips made outside a large city. Society has not reacted so strongly and has not restricted mobility in the same way. For example, in none of the phases, there was a decrease in the number of regional trips made from the Mazovia province area to the WMA zone. Moreover, the second and third phases even saw an increase in this type of trip, with the third phase reaching 38%. In the morning peak, in the WMA zone itself, slight differences in the number of any kind of trips generated in phases one and two were noted. It was only in the third phase that a clear increase in the number of trips was noticed compared to the earlier two pandemic phases (Figure 3).
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Receiver** & \(\Delta\mathbf{x}\) & \(\sigma_{\Delta\mathbf{x}}\) & \(\Delta\mathbf{y}\) & \(\sigma_{\Delta\mathbf{y}}\) & \(\Delta\mathbf{z}\) & \(\sigma_{\Delta\mathbf{z}}\) \\ \hline Trimble NETR9 & Nominal & \(-1.24\) & \(0.30\) & \(1.53\) & \(0.47\) & \(-1.12\) & \(0.31\) \\ Realistic & \(-0.84\) & \(0.19\) & \(1.16\) & \(0.3\) & \(-0.99\) & \(0.21\) \\ SEPT POLARX5 & Nominal & \(2.21\) & \(0.28\) & \(-3.44\) & \(0.45\) & \(2.34\) & \(0.29\) \\ Realistic & \(2.13\) & \(0.08\) & \(-3.43\) & \(0.13\) & \(2.16\) & \(0.08\) \\ LEICA GR30 & Nominal & \(-7.17\) & \(1.59\) & \(-1.18\) & \(0.90\) & \(-7.20\) & \(1.42\) \\ Realistic & \(-6.09\) & \(0.051\) & \(-0.97\) & \(0.28\) & \(-4.33\) & \(0.45\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The difference between calculated baselines and real values and standard deviations with nominal and realistic stochastic model in multi-GNSS (mm). improves the results' reliability. In the end, regardless of the system or receiver used, we can conclude that using a basic stochastic model leads to poorer precision of the predicted parameters. The results of LS-VCE for each receiver could be used in any other observation session. This procedure can also be thought of as the calibration of a specific receiver.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 4 New Approach: Parallel Vector Processing Based Algorithm  
Using a Vector DSP with a Very Large Instruction Word (VLIW) architecture requires a different approach compared to the original straightforward approach detailed in Section 3. The main idea is based on manipulating the data vector-wise instead of bit-by-bit and taking advantage of the modulo attributes originating from (2) and (3). Figure 2 indicates modulo-induced strides from one index to the next. There are four different groups of indexes derived from the four cases in (3). These strides constitute the basis of the developed vector-wise algorithm described in the following subsections. The algorithm is based on configurable parameters defined by a standard, ensuring a comprehensive and generic solution, thus enabling further research possibilities as elaborated in Section 7.  
Basing the algorithm on _Load-Execute-Store_ operations fitted with the CEVA-XC4500 DSP attributes creates phases. Each phase consists of iterations of execution operations that are performed in one work cycle of the DSP. The following subsections elaborate on these operations, which combined implement the permutation block of the TE. The presented algorithm was constructed for a specific VP and a specific TE but could be easily modified to various VPs and or different TEs. We chose to test and demonstrate the method on the CEVA-XC4500 and implement the TE described by the DVB-RCS2 standard.
## 4 Characterization Framework
### Communication Style  
Communication in a microservices architecture is one of the most challenging points due to its distributed nature. It directly affects the availability and resiliency of the systems. In the MSA, we can examine the communication in two headings, intra-microservice communication and inter-microservice communication.  
Figure 4: Feature diagram of data management and consistency.  
As shown in Figure 5, the first and most complex of the two is communication between services. Services can communicate with each other through a sync communication infrastructure, but, with this architecture, both the client and the server must be available to sustain the communication. Moreover, there is a tight runtime coupling among services. Communication can be sustained without the need for any message brokers, but services need to know each other's locations, which brings extra complexity. Furthermore, an external request generally needs collaboration between services, which might cause blocking of the system for a long time and some problems in availability and resource usage of the system. However, these concerns can be eliminated with async messaging. Availability and resource management improves, and runtime coupling becomes loose. The presence of a message broker can be counted as a challenging point. In addition, communication management is more complex, too. Sometimes, domain-specific protocol can be used in the communication between services; although this type of usage is limited, it can be preferred in an appropriate use case, such as SMTP or IMAP [23].  
As shown in Figure 5, some patterns are recommended to make the communication of microservices outside of them healthier. For example, with the API Gateway pattern, all requests coming from outside are transferred to the appropriate services inside through this structure, and the services respond to this request by communicating with each other [28]. Different APIs can be created for each type of client. It is called Backend for Frontend (BFF) by SoundCloud [29]. Moreover, it can translate external requests into protocols used across microservices. Since the location information of the services changes dynamically, the outside world does not need to know this location information thanks to API Gateway. This structure can be thought of as the only door opening to the outside world and isolates the system inside. Security concerns can be addressed here. For example, in a scenario where HTTPS is used when talking to the outside world, it will be sufficient for the services inside to talk with the HTTP protocol because the inside can be considered safe after the API Gateway. Some cross-cutting concerns, such as SSL, could be handled in API Gateway so internal microservices are lightweight and simplified [30]. Another solution is that each client communicates directly with microservices, but this method is a primitive method and its usage area is very limited. None of the benefits that come with API Gateway can be achieved with this pattern.
## 3 Results
### Comparison with Other Existing Works  
In this section, the results obtained from the proposed method are compared with existing work on VF detection found in the literature and are listed in Table 5. The study by [PERSON] et al. [14] shows the best result in terms of specificity; however, its sensitivity lags by a large margin of 19.29%, and the accuracy also falls behind by 2.62% compared to those of the proposed model. The work proposed by [PERSON] et al. [23] demonstrated excellent results and obtained performance measures near to our model, but it is lagging with a margin of 2.62%, 1.81% and 2.99% in terms of accuracy, specificity, and sensitivity, respectively. In another study [21], [PERSON] et al. showed the best result for VF detection; their result is very close to our study, with only slight differences of 1.62%, 0.51%, 1.29% in terms of accuracy, specificity, and sensitivity. However, their AUC score straggles by 5%, which is one of the critical measures of a model's classification capabilities. Moreover, the study was conducted using a small set of data with patients age over 50 years, which may affect the model's generalizability. The work by [PERSON] et al. [16] showed balanced results on all four performance metrics, but its accuracy lags by 7.95% compared to the proposed model. Overall, the proposed work demonstrates superior performance over all the methods compared in terms of accuracy, sensitivity, and F1-score, which indicates that our model is robust and accurate.  
Figure 13: ROC curve of base models and proposed model.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Existing Method** & _Acc (\%)_ & _Spec (\%)_ & _Sens (\%)_ & _F1 (\%)_ & _AUC (\%)_ \\ \hline [PERSON] et al. [15] & 80.4 & 81.6 & 78.7 & 83.5 & – \\ [PERSON] et al. [17] & 82.9 & 87.2 & 74.3 & 75.6 & 73.4 \\ [PERSON] et al. [16] & 86.67 & 84.2 & 88.13 & 87.04 & – \\ [PERSON] et al. [14] & 92 & **97** & 76 & – & – \\ [PERSON] et al. [18] & 73.59 & 73.02 & 73.81 & – & 72 \\ [PERSON] et al. [19] & 74 & 68 & 80 & – & 89 \\ [PERSON] et al. [20] & 89 & 92 & 83 & – & 87.6 \\ [PERSON] et al. [21] & 93 & 93 & 94 & – & 94 \\ [PERSON] et al. [22] & 89 & – & – & 90 & – \\ [PERSON] et al. [23] & 92 & 91.7 & 92.3 & – & – \\
**Proposed model** & **94.62** & 93.51 & **95.29** & **93.16** & **99** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the proposed method with the other vertebral fracture detection models.
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 5 Results and Case Studies
### Case Study: Redundancy to Improve Yield and Reduce Error Rate  
In this subsection, we demonstrate how combining the redundancy of the PUF cells along with the proposed testing technique can be used to improve reliability and yield of a chip using SRAM-based PUF. We also estimate how many extra redundant cells are required for guaranteeing high yield.  
Let us assume that, under a particular manufacturing process and SRAM design, \(X\%\) of cells are unreliable beyond correction by UP/DOWN voter scheme. We name such cells _bad cells_. Hence, we can assume that each cell has a probability of \(x\) (\(0\leq x=\frac{X}{100}\leq 1\)) of turning up as a bad cell after manufacturing. We also assume that the reliability for different cells is independent. We are interested in calculating, for a particular value of \(x\), how many redundant cells are required to create a stable  
Figure 8: Expected number of trials to reach saturation (decision) in a 4-bit UDC.  
Figure 9: Testing/DFT method for identifying high error rate cells.  
128 bit-key with high probability. We aim at reducing the probability of not finding 128 reliable bits cells to \(\leq\)10\({}^{-6}\). This can be calculated using a binomial counting process as given below:  
\[P_{yield\_loss}=\sum_{m=128}^{N}\binom{N}{128}x^{m}(1-x)^{n-m}, \tag{6}\]  
where \(P_{yield\_loss}\) is defined as the probability of not finding 128 stable cells in a design with \(N\) cells. For example, if a particular manufacturing process has \(x=0.05\), with just 151 cells, we can guarantee that 128 stable bits can be found with high probability (\(1-10^{-6}\)). This ensures that expected yield loss is around 1 in a million chips for the above example.
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 4 Discussion
The RF classification showed that 82% of the municipality is covered by different types of land occupation, which is in disagreement with the Brazilian Forest Code, which determines a maximum of 80% of land occupation (see more details in the Introduction Section). Our results of RF classification present a relatively good agreement with those from the MapBiomas initiative [6] (Figure 11), except for cultivated pasture. PS mosaics underestimated annual crops by 8%. This difference was somewhat expected because of differences in the image acquisition modes, matching mapping legends, and satellite overpasses. According to IBGE [30], the total harvested area with annual crops in the municipality of Goiatuba was 176,032 ha in 2021, that is, 71% of the municipality, a percentage much higher than those estimated by PS (37%) or MapBiomas (45%). This difference is because this institution (Brazilian Institute of Geography and Statistics--IBGE) counts the harvested area twice whenever the double cropping system is adopted. For example, Goiatuba harvested 27,400 ha of corn in 2021, but 94% of this area corresponded to the second crop [54]. Some authors have used Bayesian-based dropouts during the calibration phase of the methodology so that the model uncertainty can be statistically estimated [55; 56]. In practice, this method, known as Monte Carlo dropout, has become attractive since it deals well with large amounts of data [57]. We recommend testing this approach in order to investigate the radiometric consistency of PS monthly mosaics in terms of signal variations associated with varying image acquisition modes and multiple dates of individual satellites that are part of the PS constellation. Regarding the limitations of this study, we can point out the differences in the crop type (mostly for two types--maize and sorghum) and main differences in the crop planting dates that can cause large radiometric variations in the PS monthly mosaics, leading to misclassifications. As pointed out above, the radiometric variations due to different image acquisition modes and different dates of individual PS satellites that are considered in the production of monthly mosaics are another source of classification errors, although these mosaics are processed for normalization to reduce scene-to-scene variability and for seamline removal to minimize scene boundaries [38]. Figure 11: Comparison between the land use and land cover (LULC) data produced by the PlanetScope monthly mosaics and by the MapBiomas initiative for the municipality of Goiatuba, Goiás State, Brazil.
## 4 Design Scheme and VISSIM Simulation
### Calibration of the VISSIM Simulation Model  
To ensure the authenticity and accuracy of the simulation model, the model should first be calibrated to minimize the error between the simulation results and the measured data within an acceptable range. The capacity index is highly responsive to the path selection behavior in the model [13], necessitating the utilization of the \"capacity calibration method\" for model calibration. The mean absolute percentage error (MAPE) reflects the degree of deviation between the simulated value and the measured value. It is commonly used as an evaluation index for the simulation error. The calculation formula for the MAPE is as follows:  
\[MAPE=\frac{\sum\limits_{i=1}^{n}C_{p}^{i}-\sum\limits_{i=1}^{n}C_{f}^{i}}{\sum \limits_{i=1}^{n}C_{f}^{i}} \tag{6}\]  
where \(i\) denotes the traffic flow, \(n\) denotes the total number of traffic flows, \(C_{p}^{i}\) denotes the simulated capacity in VISSIM (veh/h), and \(C_{f}^{i}\) denotes the measured capacity (veh/h).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Maximum** & \(-1\) **m/s2** & **Acceptable** & **Coordinate Maximum** & **Safety Distance** \\
**Deceleration (m/s2)** & **Distance (m)** & **Deceleration (m/s2)** & **Deceleration of Brakes (m/s2)** & **Reduction Factor** \\ \hline \(-4\) & \(50\) & \(-2\) & \(-4\) & \(0.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Key driving behavior parameters in Simulation model.  
According to relevant studies, a simulation model is generally considered accurate in simulating the actual traffic situation when the MAPE value is less than 15% [50; 51].  
The MAPE calculation results are presented in Table 6. Based on the data in Table 6, the error is 0.28%, which is less than 15%, indicating that the accuracy of the simulation model meets the requirements.