esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Topology Control
### Post-Deployment
#### 4.3.1 Connectivity, Energy-Efficiency and Coverage
There are usually three statuses in each sensor node: active, idle and sleep. The node in sleep or idle mode consumes less energy than the active mode. Nodes are scheduled to keep some nodes alive or active if they are on duty, while letting other nodes be in sleep mode in order to save the energy of the whole network, but still keeping the network connected. The work in [88] points out that the idle status consumes almost the same power as the active status, but the sleep model consumes much less than the active status. For the case that every node is active with equal probability \(p\), the asymptotic \(k-coverage\) results for a grid, random uniform and Poisson deployment are studied in [89]. The work in [56] utilizes random scheduling for the required coverage, but perhaps the active nodes are not connected; therefore, other nodes are turned on to guarantee connectivity if necessary. Node A is called the upstream node of B if A is the neighbor of B and one hop closer to the sink; accordingly, B is called the downstream node of A. The status of Node A is not only decided by the random scheduling assigned by the coverage scheduling, but is also determined by its downstream nodes. For example, Node A may be in sleep mode assigned by coverage scheduling, but it will be forced to switch to active mode if its downstream node is active and there is no path for this downstream node for transmitting data to the sink. The model in game theory can be adapted to optimize the energy of WSNs, as well. For instance, the player in WSNs is the node. All nodes work in a non-cooperative manner. They selfishly conserve energy by refusing participants as relay nodes, but they can participant as a relay by offering some incentive to encourage cooperation. An incentive could be the token. The tax mechanism can be adopted, as well: a higher communication range introduces higher interferences to neighbor nodes; hence, they need to pay a high tax. The work in [90] surveys game approaches to formulate problems related to security and energy efficiency in WSNs.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 2 Communication Management in a Data Center
### Valiant Routing Algorithm  
For large DCNs with high-radix and low-diameter topologies, which are prone to congestion issues concentrating traffic exclusively on selected nodes is, in general, undesirable. The network becomes less resilient to load spikes. Additional traffic cannot be handled using the remaining devices because they are not on any of the shortest paths between the communication-intensive servers. To alleviate this, Valiant routing can be recommended for use. The aim of the Valiant routing algorithm, developed by [PERSON] and [PERSON] [19], is to divert traffic to a randomly selected intermediate switch (a pivot) to avoid pathological congestion issues. It is the basis of some adaptive routing algorithms.  
In general, the algorithm of finding a path between the pair of servers calculation consists of three main stages:  
1. Pivot node random selection.
2. Path calculation from the source server to the pivot.
3. Path calculation from the pivot to the destination server.  
At the cost of extending the path used to serve the transfer, the load present in the network can be balanced. Various modifications of the original version of the Valiant routing can  
Figure 1: The four-layer butterfly topology with two switch connections to the next layer.  
be found in the literature. [PERSON] et al., in [PERSON] et al. (2019), introduce restricted Valiant routing that randomizes traffic within local partitions generating shorter paths. Furthermore, in the case of ECMP routing, selecting a pivot located on any of the shortest paths between the chosen pair of servers does not change the path--as the shortest path between servers is also the sum of the shortest paths leading from the source to the pivot, and from the pivot to the destination node.
## 2 Satellite Imagery Applications
This is especially useful as forage quality can be tracked and/or modeled in near-real time to allow for timely management interventions that improve animal productivity. Laboratory based analyses of diet quality, either through processing of vegetation or fecal matter, provide data days after the fact, whereas remote sensing estimates can provide near-real time information on current conditions [29] that is non-invasive and nondestructive and is easily scalable. When paired with biomass estimates, this technology has a lot of potential for affecting management decisions in remote pastoral regions. This is especially true in areas where virtual fencing is used to automatically and remotely control herd dispersion and location [35; 36; 37]. Historical vegetation and long-term weather data with remotely sensed data processed with machine learning algorithms can be used to visualize past patterns and anomalies and produce short and long-range forecasts for biomass production and drought modeling [29; 38; 39]. Similar technologies are currently in use for US crop and rangeland insurance programs [38]. However, the spatial resolution of many remotely-sensed weather products are still quite coarse [38]. While this may be appropriate for analyzing conditions over a broader region, it is not always applicable to localized areas such as ecological sites or pasture paddocks. Fortunately, accurate and modestly priced consumer grade weather stations are becoming more common among private users, providing a new source of georeferenced climate data. Always-on data collection and processing technologies, known as the Internet of Things (IoT) [40], is a growing wellspring of spatially dispersed data. Recently, new modeling packages for the R programming language, such as NicheMapR and Microclima have allowed researchers to model microclimates at hourly intervals at less than 30 m resolution [41; 42] As private weather and climate data collection grows in popularity, modeling and ground truthing capabilities will benefit from the additional data as well.
## 2 Background
### Notation
The expression below provides a simple example:  
\[\underset{10\leqslant a<13}{\mathbb{S}} \begin{pmatrix}a\cdot\underset{1 \times 5}{\mathbb{S}}\end{pmatrix} = \begin{pmatrix}10&10&10&10&10\\ 11&11&11&11&11\\ 12&12&12&12&12\end{pmatrix} . \tag{3}\]  
As regards convolutions, there exist three modes according to which they can be computed, commonly named \"same\", \"full\", and \"valid\" (see, e.g., the documentation of scipy.signal.convolve2d). For simplicity, we choose to apply the mode \"same\" throughout the paper. This means the resulting convolution image is of the same size as the input, and it is achieved by aligning the top-left input pixel in every channel with the central weight of a filter. It also means that suitable image padding with zeros must be introduced. We use superscript \(P\) to denote a zero-padded tensor; e.g., for a tensor, \(X\), of shape \(H\times V\times C\), the elements of \(X^{P}\) ought to be understood (we use \(V\) for width since \(W\) is occupied by weights) as  
\[x_{j,k,q}^{P}= \begin{cases}x_{j,k,q},&\text{if }0\leqslant j<H\text{ and }0\leqslant k<V;\\ 0,&\text{otherwise},\end{cases}  \tag{4}\]  
regardless of the channel index, \(q\). Frequently, formulas related to convolutions and padding go together with extracting subtensors, i.e., taking subranges from original tensors. For that purpose, we shall use the \":\" symbol, working as the range operator in a way known from programming languages, where the index written before the colon is treated inclusively and the one afterwards exclusively. E.g., \(X_{j_{1};j_{2};k_{1};k_{2}}\) represents a subtensor consisting of the following elements: \(x_{j,k,q}\) for all \(j_{1}\leqslant j\)\(<\)\(j_{2}\), \(k_{1}\leqslant k\)\(<\)\(k_{2}\) and all \(q\) (if not restricted; otherwise, subtensors are taken along all the channels). Furthermore, \(\phi\) shall stand for activation functions, and we are going to use that same overloaded symbol for all layers. The reader will be able to understand it correctly from the context, based on the name of \(\phi(\cdot)\)'s argument. When no activation function for a layer is declared, one should simply treat \(\phi\) as an identity function and its derivative as a 1. Finally, we explain that, in actual DL implementations (also in ours based on NumPy and Numba), all tensors are typically stored with an extra dimension pertaining to a _batch_ of images (data examples) currently processed, rather than a single image. For example, a layer producing 16 convolutional images of size \(32\ \times\ 32\) each, for a batch of 100 input images, can be stored as a 4D tensor of the following shape: \(100\ \times\ 32\ \times\ 32\ \times\ 16\). In spite of this, in the mathematical and algorithmic sections, we choose to simplify the considerations (and notation) by not taking that extra dimension into account. In other words, we look at computations as if they were carried for a single data example.
## 5 Experiments and Results obtained  
After running the code on CNN with regularization and without regularization, our best results are as follows:  
1. For CNN without regularization, we got an accuracy of 94.51% on the train set and 85.41% on the test set.
2. For CNN with regularization, we achieved 96.06% and 86.63% accuracy on train and test sets, respectively.
## 3 Methodology
### 2 \(\times\) 2 Confusion Matrix and ROC Analysis  
In this study, a receiver operating characteristics (ROC) analysis was conducted using a 2 \(\times\) 2 confusion matrix to check the extent to which the areas with high flood vulnerability calculated using the logistic regression model were consistent with the inundation trace maps. The 2 \(\times\) 2 confusion matrix and ROC analysis have been mainly used in the medical field, including the performance evaluation of reagents that discriminate negative from positive patients in the diagnostic test of COVID-19, which has been spreading around the world in recent years. This technique has recently been extended and applied to the fields of machine learning and object recognition to evaluate the classification accuracy of artificial intelligence [42,43]. ROC analysis allows us to determine whether a test method is useful by showing a curve for the degree to which an event is detected for each test method [44,45]. To draw this curve, four components of a 2 \(\times\) 2 confusion matrix should be used.  
As shown in Table 3, the 2 \(\times\) 2 confusion matrix can be composed of 4 different combinations depending on whether the flood vulnerable area and inundation traces on the map coincide. If the flood vulnerable area and inundation traces coincide, it can be expressed as true positives (_TP_) or true negatives (_TN_); otherwise, it is expressed as false positives (_FP_) or false negatives (_FN_). For the plot of the ROC curve, the \(x\)-axis is calculated as 1-specificity, showing specificity which is the ratio of accurately predicted areas (TN) among the areas where no actual flooding occurred (FP + TN). The \(y\)-axis of the graph shows sensitivity, which is the ratio of the areas selected as flood vulnerable areas (TP) among the flooded areas (TP + FN). When expressed as an equation, specificity and sensitivity can be expressed as Equations (11) and (12), respectively, and the range of values is [0, 1][45].  
\[Specificity=\frac{TN}{FP+TN} \tag{11}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{12}\]  
In ROC analysis, the performance of a test method can be evaluated by calculating the area under the curve (AUC). It can be evaluated that the closer the AUC is to 1, the better the detection performance is, while the closer the AUC is to 0, the worse the detection performance is. According to [PERSON] et al. [46] and [PERSON] [47], the AUC can be evaluated as shown in Table 4. In addition, if the ROC curve is distributed below the diagonal with a slope of 1 and the AUC is calculated to be 0.5 or less, it means that the test method is not useful.
# Print Quality Analysis of Stone Paper and Coated Sticker Paper Used in Screen Printing
## 4 Results and Discussions
### Absorbance and Ink Consumption  
In the contact angle measurements, the water spread was greater on the coated sticker paper. These results showed that the coated sticker paper had a more absorbent texture than the stone paper (Figure 4).  
By measuring the mass of each paper before and after printing, the amount of ink consumed by these papers was determined. The ink consumption measurements revealed that the density values and ink consumption were greater on the coated sticker paper than on the stone paper. The density values of all the solvent ink pr  
Figure 4: Time-dependent changes in surface contact angles for unprinted papers.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh Count**} & \multirow{2}{*}{**Ink Type**} & \multicolumn{8}{c}{**Stone Paper**} & \multicolumn{8}{c}{**Coated Sticker Paper**} \\ \cline{3-14}  & & & & & & & & & & & & & & & & \\ \hline
90 tpc & Solvent & 2.76 & 0.01 & 30.41 & 0.07 & \(-\)7.57 & 0.04 & \(-\)60.01 & 0.06 & 2.83 & 0.01 & 27.90 & 0.05 & \(-\)6.02 & 0.02 & \(-\)58.38 & 0.02 \\
120 tpc & Solvent & 2.62 & 0.01 & 33.93 & 0.02 & \(-\)12.96 & 0.03 & \(-\)59.07 & 0.03 & 2.81 & 0.00 & 32.98 & 0.05 & \(-\)13.31 & 0.03 & \(-\)58.68 & 0.02 \\
140 tpc & Solvent & 2.60 & 0.02 & 35.11 & 0.01 & \(-\)13.86 & 0.04 & \(-\)59.76 & 0.03 & 2.80 & 0.00 & 34.32 & 0.02 & \(-\)14.60 & 0.02 & \(-\)59.55 & 0.02 \\
90 tpc & UV & 2.24 & 0.01 & 43.58 & 0.03 & \(-\)24.84 & 0.05 & \(-\)54.55 & 0.04 & 2.06 & 0.01 & 44.09 & 0.03 & \(-\)21.82 & 0.03 & \(-\)51.48 & 0.02 \\
120 tpc & UV & 1.88 & 0.02 & 47.29 & 0.06 & \(-\)28.88 & 0.02 & \(-\)52.20 & 0.05 & 1.93 & 0.01 & 46.04 & 0.03 & \(-\)24.38 & 0.02 & \(-\)50.89 & 0.01 \\
140 tpc & UV & 1.79 & 0.00 & 48.32 & 0.03 & \(-\)29.30 & 0.05 & \(-\)51.55 & 0.02 & 1.85 & 0.00 & 47.45 & 0.03 & \(-\)25.43 & 0.04 & \(-\)49.78 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Density and L*a*b* measurements before lightfastness test with standard error (SE) values.  
of the UV-based ink prints (Table 4). Especially in the prints made with solvent-based inks, the ink consumption on the coated sticker paper was twice as much as on the stone paper (Table 5).  
As the surface contact angle increases, the ink consumption decreases (Figure 4 and Table 5). Stone paper has a non-absorbent polymer surface; thus, a higher surface contact angle was measured than for the coated sticker paper (Figure 4). This reduces the absorbency of stone paper, causing ink consumption to remain low. Coated sticker paper, on the other hand, has an absorbent cellulosic surface, thus allowing the surface contact angle to decrease. The absorbent cellulosic structure of coated sticker paper is also an essential factor in the high ink consumption of coated sticker paper.
## 4 Redundant Transmission Control Clustering Approach
### Data Collection at Cluster Heads  
The CH each time selects member nodes from different directions in it's cluster and assigns the Time Division Multiple Access (TDMA) slots to the selected member nodes. The remainders of the member nodes which do not receive TDMA slots do not take part in the sensing activity and stay in sleep-mode to save energy resources of the network. Each of the data packets received from the member nodes contains a unique packet ID, if a data packet with the same ID or containing the same information received at the CH, the CH will discards this information. On fusing the gathered information, CH checks the similarity of IDs and data packets with the previously received data packets to take any decision. If any similarity is found, then the CH will discard all those similar packets. The similarity of the received data is checked through the following tests.
# Cell-Based Target Localization and Tracking with an Active Camera
## 2 Related Work  
### Image-Level Target Tracking  
Before 2010, researchers achieved fruitful results in the field of target tracking, Classic target tracking algorithms included Meanshift [10], Camshift [11], particle filter [12], and the Kalman filter algorithm, as well as the optical flow algorithm, and so on. However, these classic target tracking algorithms also have some problems, including limitations in feature representation. When the target is deformed, partially occluded, and there is similar target interference in the background, it will cause the tracking target to be lost, or cause a large error in the target trajectory.  
The Siamese network trackers and correlation filter trackers have better performance, with the appearance of many new target tracking algorithms. Compared with the traditional correlation filtering algorithm, when the SiamFC algorithm [13] is used to track the targets, the targets in the first frame are used as the templates for subsequent frame tracking, and the templates will not change. This also causes the tracking template not to change with the change of the target. The disadvantage is that it cannot perceive the change of the target. When the parameters of the target change, the tracked target is likely to be lost. Classical Siamese network tracking algorithms include SiamFC [13], SiamRPN [14], SiamRPN++ [15], etc.  
The essence of the correlation filtering algorithm and its improved algorithm [16; 17; 18; 19; 20; 21] is to find an area with the greatest correlation with the template in the current frame of the video sequence. The tracking template comes from a filter of online learning. In tracking the target, the correlation filtering algorithm continuously updates the template. The performance of the updated template directly determines the effect of target tracking. The classic correlation filtering algorithms include MOSSE [22], CSK [23], KCF [24], DSST [25], SAMF [26], SRDCF [27], Staple [28], C-COT [29], ECO [30], and ATOM [31].  
Many excellent deep learning-based target tracking algorithms have emerged. They are divided into two categories. One is based on region proposals, and the main representatives are R-CNN, SPP-net, Fast R-CNN, Faster R-CNN, R-FCN, etc. The others are based on the end-to-end algorithm, mainly represented by SSD [32] and the YOLO algorithm [33; 34; 35; 36].
## 4 Extensive Computer Simulations  
To verify the applicability of the proposed selective FMS filtering with two kinds of measurement windows and to compare it with the standard FMS filter with one measurement window as well as the Kalman filter, extensive computer simulations using the well-known commercial software Matlab are performed for a couple of discrete-time dynamic systems such as a F404 gas turbine aircraft engine and electric motor.
## 3 Payload Design
### Electrical  
Figure 3 portrays the harness diagram for the RSONAR II payload. The power budget measurements for the payload and subpayloads are outlined in Table 2. Each subpayload has its own onboard computer (OBC), so OBC power consumption is calculated for each subpayload. RSONAR II uses a single PDU for all subpayloads but the PDU is physically located within subpayload 2, so the PDU power consumption is calculated with subpayload 2 in Table 2. The PDU of RSONAR II comprised an input power harness, two custom Printed Circuit Boards (PCB), and six output lines. The DC voltage was supplied by the CSA batteries (Saint-Hubert, QC, Canada), which started at around 36 V and dropped to 24 V throughout the planned mission duration. The power harness was connected to the CSA batteries and PCBs via a PT06E-12-3P(SR) connector, a 16 AWG dual conductor wire, PT06E-12-3S(SR) connector, and a PT02E-12-3P connector in series. The selected connectors conform to the MIL-DTL-26482 standard [15] and feature a higher wire ampacity to accommodate the expected power draw. The primary function of the PDU was to regulate the input voltage, producing a single 12 V output line for subpayload 1 and a 5 V output line for subpayload 2 with four redundant lines. These output lines were achieved through the use of a PYBE30-Q24-S12-T DC-to-DC converter and two PYBE30-Q24-S5-T converters. All three regulators were placed in the application and electromagnetic compatibility (EMC) circuits recommended by the manufacturer [16]. The input of the regulators utilized a 5 A fuse, while the 12 V and 5 V regulator lines had additional fuses rated at 2.5 A and 2 A, respectively. The output rails each had a barrel jack connector, and an 18 AWG power cable connected them to the subpayload's primary boards.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Mass (Grams)** \\ \hline Payload components & 2236.3 \\ Fasteners (screws, nuts, and washers) & 150.0 \\ Aluminum Parts and Frames (main body) & 1428.9 \\ Interface Plate & 1488.0 \\ \hline Total Mass & 5303.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RSONAR II Mass Budget.  
Figure 2: RSONAR II Model; (**a**) RSONAR II CAD Model, (**b**) RSONAR II payload integrated on the gondola.
# Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm
## 4 Results
### Estimation Stages
#### 4.2.2 Estimation of the Bishop Model Parameters
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**Cell 1 (mc-SI)**} & \multicolumn{2}{c}{**Cell 2 (pc-SI)**} \\  & **Best** & **Worst** & **Best** & **Worst** \\ \hline \(I_{ph}(A)\) & 0.41 & 0.42 & 0.32 & 0.32 \\ \(I_{sat}(A)\) & \(9\times 10^{-8}\) & \(9\times 10^{-8}\) & \(2\times 10^{-9}\) & \(9\times 10^{-8}\) \\ \(n\) & 1.1 & 1.2 & 1 & 1.3 \\ \(R_{s}(\Omega)\) & 0.13 & 0.14 & 0.1 & 0.09 \\ \(R_{sh}(\Omega)\) & 52.5 & 53.6 & 155.4 & 179.9 \\ \(V_{br}(V)\) & \(-28.1\) & \(-18.3\) & \(-25.9\) & \(-40.9\) \\ \(m\) & 7.5 & 4.5 & 4.4 & 6.1 \\ \(a\) & 0.029 & 0.028 & 0.005 & 0.024 \\ O.F. & 0.004 & 0.005 & 0.006 & 0.008 \\ Time (s) & 632.6 & 162.6 & 580.8 & 56.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Best and worst parameter solutions for the Bishop model of each PV cell. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Accuracy Measures** & **Cell 1 (mc-SI)** & **Cell 2 (pc-SI)** \\ \hline RMSE & \(0.004\pm 3.9\%\) & \(0.007\pm 2.9\%\) \\ MAPE & \(0.018\pm 25.9\%\) & \(0.004\pm 222.5\%\) \\ MBE & \(-7.3\times 10^{-5}\pm 185.4\%\) & \(-6.4\times 10^{-5}\pm 196.18\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Accuracy measures for estimation with the Bishop parameters (mean \(\pm\) standard deviation). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Cell 1 (mc-SI)** & **Cell 2 (pc-SI)** \\ \hline \(I_{ph}(A)\) & \(0.4\pm 0.24\%\) & \(0.32\pm 0.25\%\) \\ \(I_{sat}(A)\) & \(9\times 10^{-8}\pm 13.3\%\) & \(9\times 10^{-8}\pm 24.4\%\) \\ \(n\) & \(1.1\pm 1.1\%\) & \(1.2\pm 2.5\%\) \\ \(R_{s}(\Omega)\) &
## 3 Modeling of SIC and Time Series Analysis  
The historical mean SICs of each NSR segment have been determined by the methods given in Section 2, forming a series of data points indexed in time order. This type of data can be described as a time series. In this section, we use the ARIMA model to analyze the SIC series.