esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Formation Size on Flocking Formation Performance for the Goal Reach Problem
## 4 Discussion
The results of reference [14] support our findings, in that each agent's final desired direction becomes more complicated and takes more time to determine as the size increases. From our findings we observed that as the flock size increases, the time required to complete the task increases as well, which does not adhere to reference [14]. This discordance is due to the fact that the flock sizes used in their work (with a maximum flock size of 20) were much smaller than the flock sizes used in this work. Moreover, the increase in the time required to complete the task is due to the formation maintenance and cohesion of flock members, which prevents the flock from exploring the environment properly (i.e., presenting stable flocking behavior). Interestingly, the average goal localization time remained unaffected by the flock size, further supporting that an increase in flock size requires higher formation maintenance behavior. To summarize, flock size does not significantly affect localization time and the number of alignments per flock member. Scatter is homogeneous for all flock sizes. An increase in flock size is positively associated with the number of collisions, separations, and cohesions per flock member. In addition, the localization window and task completion times both increase as flock size increases, and and increase in variability or scatter is seen as well. A decrease in flock size is associated with a larger distance to the center of mass of the flock, as well as larger and more variable speed matching times. Scatter increases with the decrease in flock size. The results we obtained highlight the critical factors that are affected by changing the flock size, and are expected to be helpful for future research in this field. The limitations of this work lie in the small number of experiments conducted for each flock size due to time constraints. In addition, a sample size of 20 makes it difficult to draw conclusions regarding the apparent outliers in the data, as well as the standard deviation for each flock size. The excessive runtime required for larger flock sizes further prohibits experiments beyond 100 flock members. Furthermore, the controller used in this work may not be fully generalizable to other flocking tasks.
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 2 Basic Link Budget
#### Noise Floor and Noise Margins  
The noise floor \(N_{i}\) represents the equivalent noise power referred at the receiver input. It is a function of the system noise temperature \(T_{sys}\) and of the minimum intermediate frequency (IF) bandwidth \(B_{IF}\) (i.e., of the signal bandwidth) according to:  
\[N_{i}=k_{B}\,T_{sys}\,B_{IF} \tag{5}\]  
where \(k_{B}=1.38\times 10^{-23}\,\mathrm{J/K}\) is the Boltzmann constant. The system noise temperature, in turn, is related to the antenna noise temperature \(T_{a}\) and to the receiver noise figure \(F_{RX}\) by:  
\[T_{sys}=T_{a}+\left(F_{RX}-1\right)T_{0} \tag{6}\]  
\(T_{0}=290\,\mathrm{K}\) being the standard IEEE temperature for noise. It has already been pointed out that, with a 50 cm parabolic dish as the receiver antenna, a half power beam width of \(1.7\,^{\circ}\) is obtained (see Table 1). This value is smaller than the \(1.9\,^{\circ}\) angular diameter of the Earth seen by the Moon (The angular diameter of the Earth seen by the Moon is \(\delta_{e,m}\approx 1.9\,^{\circ}\), that of the Moon seen by the Earth, instead, is \(\delta_{m,e}\approx 0.5\,^{\circ}\)). As a consequence the main beam of the receiver antenna is completely filled by the Earth and the corresponding antenna noise temperature is close to the brightness temperature of the Earth surface. A conservative estimation thus gives \(T_{a}\approx T_{0}=290\,\mathrm{K}\).  
Performing the computations in (6) for a receiver noise figure \(F_{RX}=3\,\mathrm{dB}\), a system noise temperature \(T_{sys}=580\,\mathrm{K}\) is obtained. For a maximum IF bandwidth of \(50\,\mathrm{MHz}\) (i.e., at the maximum data rate), a receiver noise floor \(N_{i}=-94\,\mathrm{dBm}\) is estimated. Reducing the IF bandwidth (and thus the data rate) to \(5\,\mathrm{MHz}\) and \(500\,\mathrm{kHz}\), the noise floor decreases to \(-104\) and \(-114\) dBm respectively. The latter estimations, however, hold only when the phase noise of the local oscillators (LO) contained in the receiver is negligible (see Section 4.2).  
Concluding this basic analysis one can observe that, in the K-band, the nominal \(+115.4\,\mathrm{dBm}\) EIRP at the ground station, is able to establish a \(-76.5\,\mathrm{dBm}\) power at the receiver input, and that this value is about \(17.5\,\mathrm{dB}\) above the noise floor for a \(50\,\mathrm{MHz}\) signal bandwidth. As already stated, however, such a margin represents a best case situation; lower values have to be expected in the presence of non zenithal links and atmospheric attenuation.
## 3 Circuit Implementation  
The architecture of the proposed MPPT circuit based on the time exponential rate P&O is shown in Figure 6, including a zero current detection (ZCD), a power modulator (PM), a time sensor, an oscillator (OSC), and power transistor drivers (Ndriver, Pdriver). The boost converter works in PWM mode. ZCD predicts when reverse current occurs on the inductor, then turns off MP by signaling GP_OFF and outputs a digital signal TP<8.0> to represent the length of time (\(t_{MP}\)). According to the time exponential rate P&O, the power modulator compares the TP<8:0> of two consecutive MPPT cycles to obtain the input power change and then generates a pulse width modulating signal (V\({}_{\text{PMO}}\)) to adjust \(t_{MN}\). The time sensor converts the MN on-time to voltage V\({}_{\text{ON}}\). Once V\({}_{\text{ON}}\) reaches V\({}_{\text{PMO}}\), the output of the comparator resets the following D flip-flop, thereby defining \(t_{MN}\).
# Numerical Analysis via Mixed Inverse Hydrodynamic
## 3 Results and Discussion
### Effect of Thermal Analysis
#### 3.2.3. Leakage  
Figure 10 shows the difference in flow rate depending on whether the effect of temperature increase due to frictional heat is considered according to the ambient temperature. All flow rates decreased with increasing ambient temperature because the fluid viscosity decreased with increasing temperature and a thinner oil film was formed. Considering the effect of temperature rise, it can be seen that the flow rate decreases in the outstroke; this is because the thickness of the oil film becomes thinner as the temperature of the sealing zone increases, similar to the effect caused by the increase in ambient temperature. In contrast, the flow rate for the instroke increases when the temperature rise is considered. In the outstroke, the leakage caused by Poiseuille and Couette flows is in the same direction. In contrast, in the instroke, the Couette flow introduces the external oil existing on the air side of the outstroke into the pump, but the Poiseuille flow acts in the opposite direction to the Couette flow and causes oil leakage. Considering the effect of temperature rise, the fluid lubrication pressure is reduced by the thinner oil film, which reduces the size of the Poiseuille flow and increases the instroke flow rate. Consequently, the effect of increasing the temperature resulted in reduced leakage in both instroke and outstroke. In the previous section, it was confirmed that the frictional force increased rapidly as the ambient temperature increased. An increase in frictional force means an increase in frictional heat and, thus, the temperature increase at the contact portion increases. Therefore, a higher ambient temperature corresponds to a greater difference in flow rate due to the temperature increase effect. This also suggests that, if the operating conditions are different, the effect of increasing the temperature may be large if the frictional force of the asperity is large, even in a section where the ambient temperature is relatively low. The relative differences in the  
Figure 8: Film thickness distribution according to different ambient temperatures and whether thermal effects are considered: (**a**) \(T_{a}=25\)\({}^{\circ}\)C, (**b**) \(T_{a}=55\)\({}^{\circ}\)C, (**c**) \(T_{a}=85\)\({}^{\circ}\)C, and (**d**) \(T_{a}=115\)\({}^{\circ}\)C.  
reverse pumping rate according to the temperature rise effect were 4.02%, 4.93%, 10.0%, and 20.9% at 25 \({}^{\circ}\)C, 55 \({}^{\circ}\)C, 85 \({}^{\circ}\)C, and 115 \({}^{\circ}\)C, respectively.  
Figure 9: Variation of friction force according to different ambient temperatures and whether thermal effects are considered: (**a**) outstroke and (**b**) instroke.
## 4 Uplink Reception With Proposed Cooperation
We aim at maximizing the sum-rate \(R_{\text{sum}}\triangleq\sum_{i\in\{1,2\}}R_{\text{sum},i}\) of all the users in the clusters while satisfying the fronthaul and backhaul capacity constraints. The problem can be mathematically formulated as\[\underset{\mathbf{\omega},\mathbf{R}_{\text{C},\mathbf{R}_{\text{sum}}}}{ \text{maximize}} \sum_{i\in\{1,2\}}R_{\text{sum},i}\] (18a) s.t. \[R_{\text{sum},i}\leq\log_{2}\det\left(\mathbf{I}+P\left(P\tilde{ \mathbf{G}}_{i}\tilde{\mathbf{G}}_{i}^{H}+\sigma_{z}^{2}\mathbf{I}+\tilde{ \mathbf{\Omega}}_{i}\right)^{-1}\tilde{\mathbf{H}}_{i}\tilde{\mathbf{H}}_{i}^{H} \right),\ \forall i\in\{1,2\}, \tag{18b}\] \[\log_{2}\left(1+\frac{\sigma_{y,i,r}^{2}}{\omega_{i,r,j}}\right) \leq\sum_{m=1}^{j}R_{\text{C},i,r,m},\ \forall i\in\{1,2\},\,r\in\mathcal{M},\,j\in\{1,2\},\] (18c) \[R_{\text{C},j,r,1}+R_{\text{C},j,r,2}\leq C_{F},\ \forall i\in\{1,2\},r\in \mathcal{M},\] (18d) \[\sum_{r\in\mathcal{M}}R_{\text{C},j,r,1}\leq C_{\text{B}},\ \forall i\in\{1,2\},\] (18e) \[R_{\text{C},j,r,j}\geq 0,\ \omega_{i,r,j}>0,\ \forall i\in\{1,2\},\,r\in \mathcal{M},\,j\in\{1,2\}, \tag{18f}\]  
where we define the notations \(\mathbf{\omega}\triangleq\{\omega_{i,r,j}\}_{i\in\{1,2\},r\in\mathcal{M},j\in\{1, 2\}}\), \(\mathbf{R}_{\text{C}}\triangleq\{R_{\text{C},j,r,j}\}_{i\in\{1,2\},r\in \mathcal{M},j\in\{1,2\}}\), and \(\mathbf{R}_{\text{sum}}\triangleq\{R_{\text{sum},i}\}_{i\in\{1,2\}}\). It is challenging to find an optimal solution of the problem in Equation (18), since it is a non-convex problem due to the constraints in Equations (18b) and (18c). However, we can obtain a difference-of-convex (DC) problem, whose objective and constraint functions can be expressed as differences of convex functions, by replacing Equations (18b) and (18c) with the following equivalent constraints  
\[R_{\text{sum},i} \leq\log_{2}\det\left(P\tilde{\mathbf{G}}_{i}\tilde{\mathbf{G}}_ {i}^{H}+\sigma_{z}^{2}\mathbf{I}+\tilde{\mathbf{\Omega}}_{i}+P\tilde{\mathbf{H}}_ {i}\tilde{\mathbf{H}}_{i}^{H}\right)\] \[-\log_{2}\det\left(P\tilde{\mathbf{G}}_{i}\tilde{\mathbf{G}}_{i}^ {H}+\sigma_{z}^{2}\mathbf{I}+\tilde{\mathbf{\Omega}}_{i}\right),\ \forall i\in\{1,2\}, \tag{19}\]  
and  
\[\sum_{m=1}^{j}R_{\text{C},j,r,m} \geq\log_{2}\left(\omega_{i,r,j}+\sigma_{y,i,r}^{2}\right)\] \[-\log_{2}\omega_{i,r,j},\ \forall i\in\{1,2\},\,r\in\mathcal{M},\,j \in\{1,2\}. \tag{20}\]  
A desirable property of the DC problems is that a locally optimal solution can be efficiently found via a Majorization Minimization (MM) based iterative algorithm (see, e.g., [19; 20]). The MM approach can be applied to tackle the problem at hand as follows: Suppose that the constraints in Equations (19) and (20), which are equivalent to Equations (18b) and (18c), are satisfied with setting \(\omega_{i,r,j}=\omega_{i,r,j}^{\prime},\ i\in\{1,2\},r\in\mathcal{M},j\in\{1,2\}\).
## 1 Introduction
This agile resource management capability of containers motivated us to explore the trade-offs of employing vertical elasticity for efficient resource management in container-based cloud environments. In this paper, we present our practice and experience of employing an adaptive resource management scheme for Big Data workloads in container-based cloud environments by leveraging the vertical elasticity of Docker [11], which is an open platform for managing and running containerized applications. Specifically, we employed two representative Big Data platforms including Apache Hadoop [1] and Spark [3] and performed extensive experiments based on several Big Data workloads from the HiBench Suite [15]. The initial implementation of Apache Hadoop [1] included the MapReduce programming model [16] with an underlying Hadoop Distributed File System (HDFS) [17], and it quickly became a de facto standard platform for effectively processing and storing \"Big Data\" with the advent of cluster-level operating system YARN [2,18]. Apache Spark [3,4,19] is a general-purpose, high-speed cluster computing platform designed for large-scale data processing. Spark is an open-source project and designed to be from 10 to 100 times faster than Hadoop's MapReduce by effectively employing _in-memory_ computing capability. HiBench [15] is a suite of Big Data benchmarks managed by Intel and widely used to evaluate different Big Data frameworks in terms of speed, throughput, and system resource utilization. During the workload execution, our proposed scheme periodically monitors the resource usage patterns of running containers and dynamically adjusts (scale up/down) allocated computing resources. Comprehensive evaluation results show that our adaptive container resource management scheme can effectively improve overall system utilization and user response time. To summarize, the contributions of our paper can be as follows:* Design and implementation of an adaptive resource management scheme that can employ dynamic vertical elasticity in container-based cloud environments;
* Devising fine-grained resource coordination and monitoring policies that can reflect the characteristics of Big Data workloads;
* Application of the proposed scheme and policies to representative Big Data platforms (Apache Hadoop and Spark) and workloads (HiBench Suite);
* Comprehensive evaluation results of the adaptive resource management scheme and policies for Big Data workloads. The rest of this paper is structured as follows. In Section 2, we discuss some of the related research work, and Section 3 presents the overall system architecture and implementation details of our adaptive container resource management scheme for Big Data workloads. Section 4 demonstrates comprehensive experimental results with the lessons learned, and finally, we conclude and discuss future work in Section 5.
## 5 Discussion
### Limitation and Future Works  
Even though an extensive work, this research still has some limitations, which can be categorized into following aspects:  
1. The data sample is not sufficient. Despite great benefits, industrialized construction technique is not the mainstream construction technique in both the United States and China. During the questionnaire survey distribution and collection process, it was found that a large proportion of practitioners have never participated in any industrialized construction projects, which made them refuse to answer the questionnaire survey. The number of respondents in some groups are not sufficient. For example, the number of Chinese practitioners whose company type is component manufacturer is only six, which is much lower than the number of respondents in other groups. Future research should enlarge the sample size, which is believed to further improve the accuracy of the prediction models.
2. Other variables may affect the results. In addition to these four variables, there could be other variables that can affect the practical technology utilization level in industrialized construction, such as construction culture and research investment. Future research will focus on collecting and quantifying these factors and thus improve the prediction performance.
3. This research is the first known research that focuses on the prediction of technology utilization levels in the industrialized construction, and ordinal logistic regression is used as the method considering the structure of the collected data. In future research, some other multi-class machine-learning algorithms (e.g., Naive Bayes, Decision tree, and support vector machine) might be used to improve the accuracy of prediction.
# Fine-Tuning BERT-Based Pre-Trained Models for Arabic Dependency Parsing
## 2 Background
### Sequence Labeling for Dependency Parsing  
Most NLP tasks can be directly considered multiclass classification problems (sequence tagging) because they are classified into pre-defined categories. For example, Name Entity Recognition (NER) identifies each entity as a person, organization, or place. Further, in part-of-speech (POS) tagging, the words are categorized as noun, verb, preposition, and so on. However, in the case of dependency syntactic parsing, the problem is more challenging because syntactic analysis is the task of mapping the sequence of words to its parse tree.  
The parse tree in dependency treebanks is a directed graph that shows each token in the sentence as dependent on only one other token (its head). Moreover, it specifies the syntactic relation between the token and its head. Thus, the dependency parsing task classes are defined depending on the length of sentences and the syntactic reactions used in the treebank annotation. Each token requires a label (class) containing information about the head token's position and the syntactic dependency relation. The head position indicates the index of the head (parent) token of the dependent token in the parse tree of a sentence. The second part is the syntactic dependency relation between the dependent token and its head. Thus, the concatenation of the head position and the dependency relation forms one class in the classification problem.  
[PERSON] et al. [20] encoded the dependency tree as a sequence of labels, where each token \(T_{i}\) in the sequence \(T_{0}\),..., \(T_{n}\) is associated with a particular label of the form (\(x_{i}\) @ \(1_{i}\)), where \(x_{i}\) encodes the position of \(T_{i}\)'s head, and \(l_{i}\) is the dependency relation label between \(T_{i}\) and its head. Three of the four encoding types for \(x_{i}\) that were applied by [PERSON] et al. [20] were evaluated in this study: naive positional encoding, relative positional encoding, and relative POS-based encoding. These three methods were chosen because they can represent non-projective dependencies that exist in Arabic sentences. Figure 1 shows the three applied encodings on an Arabic parse tree from the ArPoT treebank, whereas Table 1 lists them, along with the explanation of labels.
# Deep Directional Network for Object Tracking
## 3 Direction Deep Network Tracker
### Pretrained Direction Network  
This paper uses a deep convolutional neural network, as shown in Figure 2 and Table 1. At the end of this network, a directional information classification layer is added to obtain the information on target direction. The previous three convolutional layers of the direction network use VGG-M [26]. Numerous studies have proven that convolutional networks, such as VGG-Net, GoogLeNet [27], etc., trained with large-scale datasets achieve good generalization performance and transfer learning capabilities. Our network uses the network weights that outstanding scholars have trained as the initialization of the direction network, and greatly improves the ability of tracking learning on the network.  
The network framework finally adopts a dual-category network, whereby one classification network is trained to learn the direction information, and the other classification network is trained to learn the sample classification. As shown in Figure 2, FC6 is a direction network that can classify eleven types of direction. FC7 is a sample classification network that is used to classify target and background information. We use the softmax cross-entropy loss function, stochastic gradient descent and backpropagation to update the network weights. The number of network pretraining cycles is 100, and the training dataset consists of 58 video sequences from the VOT2013, VOT2014 and VOT2015 datasets. Using video sequences for network pretraining is different from using large-scale classified data in the past. This method is more suitable for the tracking network.  
The training samples were selected by the overlap rate strategy. To enable the network to learn more target orientation information during the training phase, the threshold value of the overlap rate is set to a smaller value of 0.6.  
\[l=\left\{\begin{array}{ll}1&if\ IoU(\mathrm{g}_{s},\mathrm{g}_{t})>0.6\\ 0&otherwise\end{array}\right. \tag{8}\]  
Positive and negative samples classification and threshold setting are shown in Equation (8), where \(l\) represents the label of positive and negative samples, and \(IoU\) denotes the calculation of the overlap rate. If the overlap rate of \(\mathrm{g}_{s}\) and \(\mathrm{g}_{t}\) is greater than 0.6, \(l\) is judged to be positive; otherwise, it is negative. The number of samples per frame is set to 200 positive samples and 50 negative samples. The convolutional layer learning rate is 0.0001, and the fully connected layer learning rate is 0.001. The positive samples obtained by Equation (8) are classified as various direction categories according to the decision criterion in the Equation (4).  
The direction network is trained by minimizing the multi-task loss function. The multi-task loss function is defined as Equation (9). \(m\) donates the batch size, \(L\) denotes the cross-entropy loss function. \(\hat{c}_{i}\) and \(\hat{l}_{i}\) represents the predicted direction and class respectively.  
\[L_{mt}=\frac{1}{m}\sum_{i=1}^{m}L(c_{i},\hat{c}_{i})+\frac{1}{m}\sum_{i=1}^{m}L (l_{i},\hat{l}_{i}) \tag{9}\]
# On the Trade-Off between the Main Parameters of Planar Antenna Arrays
## 5 Conclusions  
In this contribution, the trade-off between the main parameters of planar antenna arrays has been reviewed and a technique to overcome directivity issues in sparse arrays has been proposed.  
More in detail, the synthesis of pencil-beam planar arrays has been reviewed, and formulas relating the beam-width, the side-lobe level, the scanning range and the minimum number of radiators, have been provided.  
After focusing on the sparse array synthesis, the problem of directivity reduction has been analyzed. It has been shown that, without directivity constraints, it is possible to have a huge reduction in the number of radiators. This reduction is paid with a fall of the array directivity, which can be even 5-6 dB lower than that of equispaced lattices.  
It has also been shown that, using conventional optimization techniques, it is possible to synthesize array layouts that are able to radiate a pencil beam that has a good directivity in the broadside direction, with a limited number of antennas. Unfortunately, these layouts show a rapid fall of the performances when the beam is scanned far from broadside direction.  
Finally, a novel, yet simple, dummy directivity formula that also allows optimizing the scanned beams has been introduced. This new formula is convex as the conventional directivity constraint, so the overall synthesis algorithm remains convex and efficient.  
Using the proposed approach, we can synthesize arrays that have a minimum directivity reduction compared to an equispaced array but use a smaller number of radiators (a reduction of 30%-40%). It is also possible to exploit the aforementioned formula to synthesize arrays that exhibit an excellent directivity, significantly better than that of equispaced lattices employing a similar number of radiators.  
As a future development, the discussed approach will be applied to the synthesis of shaped beams, and the synthesis of isophoric arrays.  
This paper has been supported by the MIUR program \"Dipartimenti di Eccellenza 2018-2022\".  
The author declares no conflict of interest.