esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification
### Vision-Based Detection
#### 2.3.1 Traditional Object Detection  
Viola Jones Detector  
The VJ detector [60] relies on a simple yet effective object detection technique known as sliding windows. As part of this method, the window's position and scale are scanned in order to determine if there is a human face in any of the windows. Although the sliding windows technique used by the VJ detector appears to be straightforward, the computational demands of this approach were beyond the capabilities of computers at the time of its development. This method combines three essential techniques: \"integral image\", \"feature selection\", and \"detection cascade\", which significantly enhance the speed of detection.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**mAp** & **VOC07 mAP** & **VOC12 mAP** & **COCO mAP@.5** & **COCO mAP@[.5, 95]** \\ \hline RCNN & 58.50\% & 53.70\% & - & - \\ SPPNet & 59.20\% & - & - & - \\ Fast RCNN & 70.00\% & 68.40\% & 35.90\% & 19.70\% \\ Faster RCNN & 73.20\% & 70.40\% & 42.70\% & 21.90\% \\ YOLOv3 & 63.40\% & 57.90\% & - & - \\ SSD & 76.80\% & 74.90\% & 46.50\% & 26.80\% \\ RetinaNet & - & - & 59.10\% & 39.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of object detection on VOC07, VOC12 and MS-COCO datasets. Detectors in the table: RCNN, SPPNet, Fast RCNN, Faster RCNN, YOLOv3, SSD, RetinaNet.  
HOG [61] can be regarded as a significant improvement over previous techniques such as scale-invariant feature transformations and shape contexts. It is designed to be computed on a dense grid of uniformly spaced cells in order to balance feature invariance, which includes translation, scaling, illumination, and other factors, as well as nonlinearity and the need to distinguish between different object classes. The classification accuracy is further improved by applying overlapping local contrast normalization to the \"blocks\". In spite of the fact that HOG is capable of detecting objects of different classes, it is primarily motivated by pedestrian detection problems. HOG detectors resize input images several times while maintaining the detection window size to detect objects of different sizes. In various computer vision applications for many years, the HOG detector has been a key component of many object detectors.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### AIDA Method
#### 4.4.3 Step 3--Candidate Positions Calculation  
The set of candidate positions (CPs) for installing key devices is established based on the positions of poles with and without DA devices. As mentioned before, the method AIDA uses a grid approach to make the optimized selection of a subset of poles to minimize the computational effort required to choose the most ideal positions for the placement of the routers and gateways. In addition, we opted for the use of a grid to establish the uniform coverage of the entire region. Usually, the territorial area of cities is irregular, presenting a varied concentration (density) of poles and meters, including very dense urban and sparse rural areas. Therefore, grid-based approaches presented good results in mesh network scenarios, such as those evaluated by the authors in [26].  
First, the region area is divided into a grid with the same horizontal and vertical spacing, considering a theoretical transmission range for the routers/gateways devices. Once the initial grid was established, the grid points are adjusted to the nearest positions of poles available in the region (Figure 4), prioritizing the use of poles with DA (objective function (3)).  
The method tries to keep the grid points within a minimum separation distance to minimize the allocation of candidate positions. Grid points that are too far from the poles are discarded. The grid resolution is adjusted at each AIDA iteration to decrease the distance between the points. Poles selected to be CP positions in previous iterations are ignored.  
Figure 2: AIDA method.
# IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security
## 1 Introduction
### Contribution
#### 1.3.1 Capacity Requirement  
We propose the IoT-Chain and Monitoring-Chain to lighten the blockchain. Blocks are created in the IoT-Chain through a consensus method in which all nodes using the Schnorr signature participate and verify the hash value of the last block to ensure the reliability between the two blockchains that can be operated on IoT devices. After distributing and storing the chain in a distributed file system, only the address value is embedded into the Monitoring-Chain, following which the blockchain of the IoT-Chain is initialized so that the blockchain capacity stored in the IoT-Chain does not exceed a specific size. The blockchain can be maintained even in IoT devices with limitations.
## 4 Results
### Comparing MSE Forecasting Accuracy between a Single Model and Combined Model with GA  
Using the results of genetic algorithm parameter selection, the parameters to be used for the demand forecast values to be optimized for the e-commerce sales dataset were decided. By using these parameters, a comparison was made between the single model MSE accuracy value and the combined model with genetic algorithm MSE accuracy values. For 22 randomly selected products, the MSE values are shown in Table 5.  
In Table 5, while, without clustering, the GA model shows the best forecasting performance in 16 out of 22 products, it plays the role of the second-best forecasting method in 3 products according to the single model performance.
# Machine Failure Prediction Using Survival Analysis
Overall, the flexibility and versatility of RSF make it a powerful tool for survival analysis, particularly in situations where there are many potential predictors and complex interactions among variables [13]. Our research makes the following contributions:  
* We propose a new machine failure prediction model aimed at increasing prediction accuracy using machine learning mechanisms and the method of survival analysis;
* The proposed model includes a data filtering/selection layer that is designed to choose the most suitable features for training the machine learning model. This is achieved by implementing various feature analysis/selection techniques. Each of the techniques employed targets a distinct aspect of comprehending the data;
* A random survival forest model is used to combine machine learning and statistical analysis, overcoming their limitations, that affect the prediction [12];
* We objectively examine the model on a collection of 10,000 machine instances, with experiments showing promising statistical accuracy;
* Our work contributes to the existing literature on the use of RSF for machine failure prediction, as well as the need for a strong feature extraction level for model training. The innovation presented in this paper centers around a novel approach to predict machine failure in large-scale systems, by incorporating a technique that prioritizes the suitability of the data. The proposed method utilizes the RSF model and an efficient feature filtering mechanism, incorporating a range of feature importance ranking techniques to eliminate irrelevant data and retain only pertinent information. This process effectively eliminates \"noise\" from the data, ultimately leading to increased accuracy and consistency in the prediction outcomes. The overall organization of the paper is as follows. Section 2 provides an overview of the related work that was reviewed for this research, including a comparison of our proposed approach to other proposals in the literature. In Section 3, we present the mathematical and theoretical background of the techniques utilized in this study. In Section 4, we describe the model created in our study as well as the dataset used for training and testing the model. Additionally, we discuss the different failure modes of the machines in the dataset. In Section 5, we present the results and findings of our study, which are later discussed in Section 6. Finally, Section 7 concludes and closes the paper.
###### Abstract  
Remotely-sensed Vegetation Indices (VIs) are often tightly correlated with terrestrial ecosystem CO\({}_{2}\) uptake (Gross Primary Production or GPP). These correlations have been exploited to infer GPP at local to global scales and over half-hour to decadal periods, though the underlying mechanisms remain incompletely understood. We used satellite remote sensing and eddy covariance observations at 10 sites across a California climate gradient to explore the relationships between GPP, the Enhanced Vegetation Index (EVI), the Normalized Difference Vegetation Index (NDVI), and the Near InfraRed Vegetation (NIR\({}_{v}\)) index. EVI and NIR\({}_{v}\) were linearly correlated with GPP across both space and time, whereas the relationship between NDVI and GPP was less general. We explored these interactions using radiative transfer and GPP models forced with in-situ plant trait and soil reflectance observations. GPP ultimately reflects the product of Leaf Area Index (LAI) and leaf level CO\({}_{2}\) uptake (A\({}_{\text{leaf}}\)); a VI that is sensitive mainly to LAI will lack generality across ecosystems that differ in A\({}_{\text{leaf}}\). EVI and NIR\({}_{v}\) showed a strong, multiplicative sensitivity to LAI and Leaf Mass per Area (LMA). LMA was correlated with A\({}_{\text{leaf}}\), and EVI and NIR\({}_{v}\) consequently mimic GPP's multiplicative sensitivity to LAI and A\({}_{\text{leaf}}\), as mediated by LMA. NDVI was most sensitive to LAI, and was relatively insensitive to leaf properties over realistic conditions; NDVI lacked EVI and NIR\({}_{v}\)'s sensitivity to both LAI and A\({}_{\text{leaf}}\). These findings carry implications for understanding the limitations of current VIs for predicting GPP, and also for devising strategies to improve predictions of GPP.  
Keywords:Gross Primary Production; vegetation indices; plant traits; radiative transfer model; NDVI; EVI; NIR\({}_{v}\) +
Footnote †: journal: Remote Sensing, 2020, 12, 1405; doi:10.3390/rs12091405
## 3 Results
### Brain Metabolism
#### 3.2.2. rhTSH Administration versus Replacement Condition  
Brain SUVR was not significantly higher under rhTSH administration compared to the replacement condition, except for the 30 min post-injection acquisitions in very small regions of possibly limited value, taking into account the small number of patients included in this study. No brain region showed statistically higher SUVR during replacement condition when compared to the rhTSH condition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Condition** & **TSH** & **FT4** & **T3** \\ \hline \multirow{2}{*}{Withdrawal} & Hypothy. & 133 (78.4) & 4.25 (2.59) & 0.61 (0.61) \\ \cline{2-5}  & Replace. & 0.02 (0.02) & 22.23 (2.92) & 2.17 (0.45) \\ \hline \multirow{2}{*}{rhTSH} & rhTSH & 20.0 (58.6) & 19.47 (6.65) & 1.78 (0.22) \\ \cline{2-5}  & Replace. & 0.19 (0.39) & 17.21 * & 1.70 (0.82) \\ \hline \hline \end{tabular}
\end{table}
Table 3: TSH, FT4, and T3 serum concentrations under the different conditions (median (IQR)). TSH is in mU/L, FT4 in pmol/dL and T3 in nmol/L * Three missing data, IQR was not computed.  
Figure 2: Voxelwise paired t-test, representing the brain regions where the SUVR was statistically lower in hypothyroidism than in the replacement condition. On the left, the results were obtained with the 30 min post-injection acquisitions, and on the right, the results were obtained from the 2 h post-injection acquisitions. Levothyroxine withdrawal saw a decrease in glucose metabolism (SUVR), especially based on the 2 h post-injection acquisitions. In particular, a dense area of hypometabolism in the hypothyroidism condition when compared to replacement condition (bilateral pre-frontal and temporal cortices, ACC, insula, striatal areas, primary motor cortex, and BA 10, 11, 22, 23, 31, 40, 44, 47). The color scale represents the \(t\)-value. Differences were considered significant for \(p<0.005\). The voxelwise \(t\)-value images were overlapped on the MNI MRI brain template. The white number at the bottom of each slice represents its position (mm) in the MNI space.  
3.2.3. [18]FFDG Brain Uptake over Time  
Mean brain SUV decreased significantly ([PERSON] signed ranks test 2-tailed exact \(p\) = 0.031) from 30 min to 2 h post-injection for all patients in all conditions (SUV2h/SUV30 min < 1, Figure 3). It seems there is a trend for a higher ratio under the hypothyroidism condition than under the replacement condition and a lower ratio under rhTSH stimulation than under the replacement condition; however, the differences are not statistically significant ([PERSON] signed ranks test 2-tailed exact \(p\) = 0.156 and \(p\) = 0.063, respectively).
## 2 Materials and Methods
### Preparation and Analysis of Wheat Straw Hydrolysate  
To generate hydrolysate, 10 g of 280 \({}^{\circ}\)C-torrefied and non-torrefied biomass were soaked at 10% (\(w/v\)) overnight in de-ionized water and ozone-lyzed in a metallic batch reactor as previously described [30], in 2.5 g-increments for 30 min with air/O\({}_{3}\) flow-rate of 5 L min\({}^{-1}\). Approximately 7.63 g of biomass was then used for subsequent pretreatment. This involved soaking at 10% (\(w/v\)) in an aqueous 28-30% (\(w/w\)) NH\({}_{4}\)OH solution (JTB-9721-03) and incubating at 50 \({}^{\circ}\)C for 12 h with no agitation in 1 L screw-cap Pyrex solution bottles. The biomass was then washed thoroughly with 0.8 L deionized water through a vacuum-pressurized Whatman filter until neutral pH 7.0 in the permeate was reached. The resulting filter-cake (g) was dried at 50 \({}^{\circ}\)C for 8 h. Then, 4 g of it was enzymatically hydrolyzed at 50 \({}^{\circ}\)C in a capped flask in an Gyromax 747 orbital incubator shaker (Amerex Instruments, Inc., Concord, CA, USA) for 72 h. This occurred at 4% (\(w/v\)) solid loading in 0.050 M sodium acetate buffer (pH 4.8) supplemented with 8 mL of solution containing 2% sodium azide with 30 FPU g\({}^{-1}\) of cellulase (Novozymes NS 50013) and 30 CBU g\({}^{-1}\) of \(\beta\)-glycosidase (Novozymes NS 50010). Enzymatic hydrolysis was terminated by boiling the hydrolysate in a hot water bath for 5 min and centrifuging for 10 min at 3000 rpm (\(\times\)\(g\)) to remove residual insoluble lignin particulates. About 48 mL of light-brown hydrolysate was then decolorized and detoxified by adding 2.4 g Darco 20-40 mesh activated charcoal (Sigma-Aldrich, St. Louis, MI, USA) at a 5% (\(w/v\)) loading, briefly vortexing, and incubating for 12 h at 4 \({}^{\circ}\)C. The activated charcoal used for de-to-toxifying non-torrefied hydrolysate was also vigorously vortexed and incubated for 16 h at 4 \({}^{\circ}\)C in acetone and methanol solvent in 1 mL Eppendorf tubes and then PTFE 0.2 um disc-filtered away to remove coloration compounds for analysis on GC-MS as previously described [31]. The resulting black slurry was centrifuged, and grey supernatant was vacuum-filtered with Whatman paper. The clear permeate was then neutralized to pH 7 via addition of 1 M NaOH, sterilized with 0.2 um disc-filter for subsequent mixotrophic microalgal cultivation, and analyzed for sugar content.  
Additional non-detoxified hydrolysates of both non-torrefied and torrefied biomass derived instead by 2% solid loading and using instead the 0.050 M sodium citrate buffer specified in an NREL standardized method [32] were also comparatively analyzed for sugar content and ability to be used for microalgal cultivation. The final acetate-buffering and de-to-toxification steps to generate the hydrolysate used for microalgal cultivation were devised after preliminarily observing that citrate buffer did not permit growth consistent with the literature and that 33% and 50% dilutions of the brown hydrolysate with deionized water allow progressively greater microalgal growth.
# Flattening the Curve of Flexible Space Robotics [PERSON]
## Appendix A Elaboration of Modal System Identification on the Flexible Robot System  
Substitute into Equation (19):  
\[\ddot{\theta}+\frac{\sum_{i=1}^{n}D_{i}(-2\zeta\omega_{i}l_{zz})}{l_{zz}(I_{ zz}-D_{i}\sum_{i=1}^{n}D_{i})}\dot{q}_{i}+\frac{\sum_{i=1}^{n}D_{i}(\omega_{i}^{2}l_{ zz})}{l_{zz}(I_{zz}-D_{i}\sum_{i=1}^{n}D_{i})}q_{i}+\frac{\sum_{i=1}^{n}D_{i}(-D_{i}T)}{ l_{zz}(I_{zz}-D_{i}\sum_{i=1}^{n}D_{i})}=\frac{T}{I_{zz}} \tag{18}\]  
\[\ddot{\theta}=\frac{2\zeta\omega_{i}\sum_{i=1}^{n}D_{i}}{l_{zz}-D_{i}\sum_{i=1 }^{n}D_{i}}\dot{q}_{i}+\frac{\omega_{i}^{2}\sum_{i=1}^{n}D_{i}}{l_{zz}-D_{i} \sum_{i=1}^{n}D_{i}}q_{i}+\frac{TD_{i}\sum_{i=1}^{n}D_{i}}{l_{zz}(I_{zz}-D_{i} \sum_{i=1}^{n}D_{i})}=\frac{T}{I_{zz}} \tag{19}\]  
\[\ddot{\theta}=\frac{2\zeta\omega_{i}\sum_{i=1}^{n}D_{i}}{l_{zz}-D_{i}\sum_{i=1 }^{n}D_{i}}\dot{q}_{i}+\frac{\omega_{i}^{2}\sum_{i=1}^{n}D_{i}}{l_{zz}-D_{i} \sum_{i=1}^{n}D_{i}}q_{i}+\frac{T(D_{i}\sum_{i=1}^{n}D_{i}+I_{zz}-D_{i}\sum_{ i=1}^{n}D_{i})}{l_{zz}(I_{zz}-D_{i}\sum_{i=1}^{n}D_{i})} \tag{20}\]  
\[\ddot{\theta}=\frac{2\zeta\omega_{i}\sum_{i=1}^{n}D_{i}}{l_{zz}-D_{i}\sum_{i= 1}^{n}D_{i}}\dot{q}_{i}+\frac{\omega_{i}^{2}\sum_{i=1}^{n}D_{i}}{l_{zz}-D_{i} \sum_{i=1}^{n}D_{i}}q_{i}+\frac{TL_{zz}}{l_{zz}(I_{zz}-D_{i}\sum_{i=1}^{n}D_{ i})} \tag{21}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System with** _Single-Sinusoidal_ & **System Order** & **Mean Error** &
\begin{tabular}{c} **Percent** \\ **Improvement** \\ \end{tabular} \\ \hline PID controlled with step input & & & \\ (benchmark) & 26 & 0.013984 & - - \\ \hline PID controlled & 26 & 0.000502 & 96.4 \\ \hline
**PID + mode 1** & **34** & **0.000199** & **98.6** \\ \hline PID + mode 1 + mode 2 & 42 & 0.00021 & 98.4 \\ \hline PID + mode 1 + mode 2 + mode 3 & 50 & \(-\)0.00029 & 98.0 \\ \hline \end{tabular}
\end{table}
Table 8: The mean tracking errors using the step responses with sinusoidal-shaped trajectories.  
\[\tilde{\theta}=\frac{2\xi\omega_{i}\sum_{i=1}^{n}D_{i}}{I_{zz}-D_{i}\sum_{i=1}^{n} D_{i}}\hat{q}_{i}+\frac{\omega_{i}^{2}\sum_{i=1}^{n}D_{i}}{I_{zz}-D_{i}\sum_{i=1}^{n} D_{i}}q_{i}+\frac{T}{(I_{zz}-D_{i}\sum_{i=1}^{n}D_{i})} \tag{10}\]  
Recall the expressions for the rigid elastic coupling using modal coordinates: \(D_{i}=\int_{F}\left(x_{F}\phi_{i}^{y}-y_{F}\phi_{i}^{x}\right)dm\), where \(\phi\)'s are the mode shapes from finite element analysis using the eigenvalues of K/m (stiffness/mass). The system stiffness matrix is included in Table 10 and the mass matrix in Table 10 shows the natural frequencies and mode shapes for the flexible system in Tables 11 and 12.
## 5 Results
For the purpose of this paper, we relied on the retransmission mechanism of the consumer application to overcome this issue with good enough results, although it is possible to devise a mechanism to overcome this problem as in [20].) The reason for the increase in the bandwidth usage with the number of consumers is caused by the sawtooth behavior of the TCP-like congestion avoidance algorithm. As the number of unsynchronized consumers increases, their aggregated demand grows smoother than their individual demands. This effect is also present when using regular TCP transmissions. To take a closer look into the effects of interest aggregation at the forwarders, we represent in Figure 6 the normalized total number of bytes exchanged in the network (the total number of bytes was normalized by the maximum value obtained in all the simulation experiments) in the previous experiments, both for the case of consumers requesting the _same_ named content and for _different_ contents. We can observe that more of the network capacity is used when consumers request the same content. This is expected since some traffic in shared links serves several consumers and, therefore, downstream links can carry more capacity. That is, a single packet at a shared link results in multiple copies when it reaches the consumers. We can also appreciate that the total traffic increases with the number of consumers in the _same_ content case, as the opportunities for aggregating interest packets at the forwarders increase. Finally, the effects of caching when requesting the same content can be seen in Figure 7.  
Figure 4: Percentage of bandwidth usage at the producer when consumers require the same data names. As before, as the number of consumers increases, the amount of total data transmitted also augments. Even with the link from the producer saturated, the consumers can still get high throughput because their data copies do not have to travel from the producer itself. With no caches, this sharing is only due to the coalescing of interest packets. However, with active caching, the sharing also comes from copies of data previously forwarded by individual satellite nodes. Figure 5: Percentage of bandwidth usage at the producer when consumers require different data names. Figure 6: Total amount of data exchanged (normalized) through the network when a set of consumers requests content from a producer at their maximum attainable rate.
# Sensorless Control for DC-DC Boost Converter via Generalized Parameter Estimation-Based Observer
## 3 Sensorless Controller Design
### Observer-Based PI-PBC  
In this section, we propose our sensorless PI-PBC, which is obtained by combining the PI-PBC of Proposition 1 with the state observer reported in the previous subsection.  
**Proposition 3**.: _Consider the system in Equation (5) with the measurable outputs of Equation (15) in a closed loop with the PI-PBC of Proposition 1, where the state \(x_{1}\) is replaced by an estimate \(\hat{x}_{1}\) generated via the FCT GPEBO of Proposition 2._  
_Then, for all initial conditions of the plant and the controller \((x(0),x_{c}(0))\in\mathbb{R}^{2}\times\mathbb{R}\) and all initial conditions of the observer \((\xi(0),Y(0),\Omega(0),\hat{\theta}(0))\in\mathbb{R}^{2}\times\mathbb{R}^{2} \times\mathbb{R}^{2\times 2}\times\mathbb{R}^{2}\), the trajectories of the closed-loop system are bounded and satisfy Equation (4)._  
Proof.: First, notice that the output signal of Equation (8), evaluated with \(\hat{x}\) instead of \(x\), may be written as  
\[(\hat{x}-x^{\star})^{T}g_{\star} =[x-x^{\star}+\Phi(\hat{\theta}_{\text{FCT}}-\theta)]^{T}g_{\star}\] \[=\hat{y}+(\hat{\theta}_{\text{FCT}}-\theta)^{T}\Phi^{T}g_{\star}.\]  
Consequently, the PI-PBC (Equation (7)) takes the form  
\[\hat{x}_{c} =\hat{y}+(\hat{\theta}_{\text{FCT}}-\theta)^{T}\[PERSON]^{T}g_{\star}\] \[\hat{u} =-k_{p}\hat{g}-k_{i}\hat{x}_{c}-k_{p}(\hat{\theta}_{\text{FCT}}- \theta)^{T}\Phi^{T}g_{\star}.\]  
Adding the control signal \(\hat{u}\) above in the system dynamics of Equation (10) yields  
\[\hat{x} =(\mathcal{J}u-\mathcal{R})Q\hat{x}-g_{\star}[k_{p}\hat{y}+k_{i} \hat{x}_{c}+k_{p}(\hat{\theta}_{\text{FCT}}-\theta)^{T}\Phi^{T}g_{\star}]\] \[=:F_{x}(x,x_{c})-g_{\star}k_{p}(\hat{\theta}_{\text{FCT}}-\theta) ^{T}\Phi^{T}g_{\star},\]  
where \(F_{x}(x,x_{c})\) corresponds to the system in a closed loop with the full-state measurable input signal \(\hat{u}\).  
Proposition 1 ensures that \((x^{\star},x_{c}^{\star})\) is a globally exponentially stable (GES) equilibrium of the unperturbed system. Now, as shown in Proposition 2, The assumption ensures that \(\hat{\theta}_{\text{FCT}}(t)=\theta\) for all \(t\geq t_{c}\). Invoking the absence of finite escape times and the aforementioned GES property allows us to conclude that Equation (4) holds globally.  
**Remark 3**.: _In this paper, the GPEBO is just designed for a boost converter. However, this technique can be extended to a large class of power converters described by port controlled Hamiltonian systems shown in [45]._