esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in military operations to prevent pilot losses. Nowadays, the fast technological evolution has enabled the production of a class of cost-effective UAVs that can service a plethora of public and civilian applications, especially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is a challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, the elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.  
unmanned aerial vehicles; genetic algorithm; mesh networks; optimization; MOEA; NSGA-II +
Footnote †: journal:  
15 October 2018; Accepted: 8 December 2018; Published: 11 December 2018
## 4 Experiment Evaluation
### Dataset  
In this experiment, we used a corpus of emoticon images and another corpus of sentences with sentiment labels for pretraining, and a corpus of sentences with emoticon labels for the evaluations. The details regarding the data are summarized in Table 3.  
Because the quantity of evaluation data is small, we used five-fold cross-validation to predict and evaluate all examples. Furthermore, as the number of examples is uneven, the synthetic minority oversampling technique (SMOTE) [30] was adopted for oversampling.
## 4 Critical Cluster for Traffic Loading  
The load effect of cable axial force is calculated and plotted on probability paper for comparison of the four clusters. One thousand snapshots of traffic are extracted from each cluster. Each snapshot represents the full length of the FRB (1813 m) covered by regular traffic extracted from images. This is done by stitching together traffic data from images at the camera location, which covers about 50 m, and it is acknowledged that it does not take account of the difference between one large image covering the full bridge length and a series of images taken at sequential points in time. As explained in Section 2.1, the weights are inferred from vehicle lengths. An influence line for cable force is derived from a detailed finite element model--Figure 7--and the axial force in the main cable on the West side is calculated for each snapshot. Southbound traffic, i.e., traffic on the East carriage, is not considered. It is assumed that the West cable will carry a small part of that load but that this will be balanced by  
Figure 6: Data illustrated as four different clusters.  
the East cable carrying a small part of the West carriageway load. It is assumed that extreme loading on the two carriageways is unlikely to occur at the same time and that, while this is a potential source of inaccuracy, it is not expected to have a big influence on the findings.  
The cable forces are plotted on Gumbel probability paper [50] in Figure 8. Even though there is no theoretical basis for such an assumption, the data fits well to a Gumbel distribution [51] as evidenced by the approximate linearity of the data in each cluster. It can be seen that the HTLF cluster is to the left of the other three, despite a high % of trucks, evidently because of the low flows involved. The LTMF and LTHF clusters are more prominent, with higher flows but low percentages of trucks. The dominant cluster is MTHF, significantly right of all others, as it has quite high flows and a medium percentage of trucks. These trends are accentuated by the relative frequencies of the clusters. As can be seen in Table 1, there are considerably fewer hours of data for the HTLF cluster data (reducing its probability for a given return period) and considerably more hours of MTHF data (increasing its probability). Given its dominance over the other clusters, the following sections relate to the MTHF cluster only.  
Figure 8: Probability paper plots of axial force in main cable for low trucks/medium flow (LTMF), low trucks/high flow (LTHF), high trucks/low flow (HTLF) and medium trucks/high flow (MTHF).  
Figure 7: Influence lines for axial force in the main cable on the West side (northbound lanes).
# Application of FBG Based Sensor in Pipeline Safety Monitoring
## 2 Pipeline Safety Monitoring Theory
### Hoop Strain Theory  
Inner pressure causes the pipeline expansion in the radial direction. Based on the theory of Material Mechanics, the relationship between the hoop strain, the inner pressure and the wall thickness of the pipeline, is given directly:  
\[\varepsilon=P\times R/E\times t \tag{1}\]  
where \(\varepsilon\) is the hoop strain of the pipeline; \(P\) is the applied pressure; \(E\) is the Young's modulus and \(t\) is the wall thickness of the pipeline.
## 2 Background
### ZRAM Memory Compression System  
We revisit a popular Linux kernel software memory compression subsystem, namely the ZRAM module. As shown in Figure 1, ZRAM compresses the inactive virtual pages and transfers them to the physical memory, which can free a lot of physical memory. When the physical memory is almost used up, the ZRAM module transfers some compressed data to secondary storage until they are needed again.  
ZRAM can virtualize one or more compressed RAM disks, which can be used as swap devices.  
**Processing flow:** We depict the compression workflow to understand how ZRAM works and where ZRAM is located in Linux memory management, as shown in Figure 2. We divide the execution process of requests into three layers for clarity and intuition: the request process layer, the read/write page layer, and the compression library layer. When memory reclaims and swaps out inactive pages to ZRAM, the request process layer first iterates through the newly swapped-out request to parse it into a collection of pages and then transfers it to the read/write layer. Furthermore, the read/write layer calls the compression function of the compression library layer to compress the data and finally copies the compressed data to the zspages in Zpool [19], which is constructed of virtual devices. For swap-in requests, ZRAM calls the decompression function to decompress the compressed data and return them to the application layer. Note that the whole execution flow of ZRAM is a single-threaded operation. When the compression library layer calls algorithms implemented by software to (de)compress data, the entire execution process would be blocked until data (de)compression is complete. In other words, the next request can be processed only after the current request is completely processed.  
**Compression algorithms:** Compression algorithms reduce the given storage space of data, but this also results in significant performance degradation. Furthermore, a high compression ratio generally means low-speed compression for compression algorithms.  
We conducted an experiment to confirm the above and evaluate the algorithms to observe the reasons for ZRAM's bottlenecks. Our platform is equipped with Intel Xeon E5-2699 v4 processors, 64 G of RAM at 2133 MHz, and is running Linux kernel 4.8.12. ZRAM supports the LZO [20], LZ4 [21], and other compression algorithms [22; 23; 24]. This experiment begins with the FID micro-benchmark with different compression algorithm configurations. Table 1 shows the throughputs, CPU usage, and compression ratios of the different compression algorithms.  
**Observation 1:** From the experimental data, we can see that (1) The LZx algorithms have a low compression ratio, although they have a higher throughput. The purpose of memory compression is to increase memory capacity, so only a high enough compression ratio can make a significant difference. (2) The compression algorithms implemented in pure software consume a lot of CPU resources, which inevitably affects the performance of upper-layer applications.  
Figure 1: ZRAM module.
## 2 Method
### Feature Importance Calculation  
In the RF model, 90% of the original sample set data is randomly selected to form the training sets for model training. The remaining 10% of the data is referred to as out-of-bag data (OOB) [45], also known as the testing sets, which can be used to calculate feature importance. Different features constitute a feature set, and each feature set may have an impact on the prediction results of the model. The greater the importance of features, the greater the impact on the prediction results of the model, so it is particularly important to determine the feature set according to the importance of features.  
We select charge and discharge capacity, charge and discharge energy, median voltage, charge and discharge time, etc. as the characteristics in the process of LIB charge-discharge. To determine the significance of these features, the feature importance is calculated for an RF model consisting of \(n\) trees. The calculation rule for feature importance in an RF model with \(n\) trees is as follows.  
\[F=\frac{1}{n}{\sum_{i=1}^{n}}|e_{2}-e_{1}| \tag{2}\]  
where \(e_{1}\) is the error associated with out-of-bag data for decision tree \(i\), and \(e_{2}\) corresponds to the error generated when introducing noise interference to a certain feature for all out-of-bag data samples.  
After calculating the importance of each feature, the feature selection process is performed as follows:  
1. The feature set is formed from the features initially selected, the feature importance is calculated, and then the features are sorted according to the feature importance.
2. Remove the least important feature in the feature set to obtain a feature set with a smaller range.
3. Repeat Steps 1 and 2 until the importance of the features in the feature set is greater than a certain threshold.
## 1 Introduction
### Advances in Training Data Synthesis  
Synthetic data can be obtained in several ways. On the one hand, realistically rendered images can be produced from accurate geometrical and photo-metrical, i.e., physical, models of the real environment where the artificial vision system would be installed [18; 19]. Building such an accurate model requires a high amount of human involvement. However, the samples generated are usually very good in terms of visual fidelity. In addition, the annotation masks are automatically generated along with the images due to the rendering process; therefore, they are highly useful for machine learning model training.  
On the other hand, generative approaches can be applied to learn how to synthesize realistic images from data without an underlying detailed physical model. Recent works use Generative Adversarial Networks (GANs) [20] for this task, which demand less human involvement because both the discriminator learning process and the image generation are fully automatic. Contrary to rendering based methods, the image annotation task needs to be done manually in a separate process.  
For example, semantic networks for image segmentation improve their performance when data augmentation of the training dataset is carried out with synthetic images rendered from 3D models [21]. Specifically, authors report a significant increase in IoU from 52.80% to 55.47% when training the system over the PASCAL 2012 dataset enriched with 100 rendered artificial images.  
In [22], the authors propose a new GAN based architecture for evaluating the effectiveness of defective image generation using different public datasets for surface defect segmentation. Another study [23] on defect detection in steel plates using deep learning architectures also applies GANs for training set extension. They report improved detection results with Fast R-CNN [24] and YOLO neural networks as surface defect detectors.
# Components of the Fracture Response of Alkali-Activated Slag Composites with Steel Microfibers
## 1 Introduction
Only a few researchers' findings connected with the fracture behavior of this kind of material have been published. [PERSON] et al. [31] presented a study about the crack growth resistance of fiber-reinforced alkali-activated fly ash concrete exposed to extreme conditions. [PERSON] [32] investigated the effects of glass microfiber content on the mechanical properties of fly ash-based geopolymer. It was found that the optimal amount of glass microfibers is 2 mass% from the point of view of the enhancement of fracture toughness, compressive strength, Young's modulus and hardness. [PERSON] et al. [33; 34] examined the fracture properties of alkali-activated slag (AAS) and ordinary Portland cement (OPC) concrete and mortar. It was observed that the fracture energy value was lower in the case of AAS mortar, as compared to OPC mortar with the same compressive strength. [PERSON] et al. [35] investigated the effect of geopolymer binder on the fracture characteristics of concrete. The fracture energy of geopolymer determined by the work-of-fracture method was similar to that of the investigated OPC concrete. The critical stress intensity factor was higher in the case of geopolymer compared to OPC concrete with the same compressive strength. [PERSON] et al. [36] ascertained that the addition of polypropylene fibers to AAS mortar leads to an increase in fracture energy and fracture toughness compared to mortar without fibers. Because of the lack of information about the fracture properties of composites with alkali-activated matrix which could be used as relevant inputs for the material model, the main aim of the present work is to determine the fracture parameters of alkali-activated slag composites with steel microfibers and quantify the contribution of the matrix of AAS composites to their fracture response. Five AAS composites were investigated. The first was a reference version without fibers; the others contain steel microfibers amounting to 5, 10, 15 and 20% by weight of the slag. The AAS mixtures were cast into molds with dimensions of \(40\times 40\times 160\) mm so as to prepare prismatic specimens for use in fracture testing. The fracture characteristics were determined based on the results of three-point bending tests conducted on specimens which were provided with an initial central edge notch before testing. Load vs. displacement (deflection at midspan) and load vs. crack mouth opening displacement diagrams were recording during the fracture tests. Each diagram was processed in order to obtain the component that corresponds to the structural response of the matrix of the composite, which consists of AAS matrix and the steel microfibers reinforcing that matrix. The obtained diagrams were employed as inputs for parameter identification, the aim of which was to transfer the fracture test response data to the required material parameters. Values were also determined for the fracture parameters using the effective crack model [14], work-of-fracture method [16] and double-\(K\) fracture model [37].
# To Cache or Not to Cache
## 4 Evaluation
### Admission Policy Hit Rate Performance  
We have analyzed and compared the hit rate performances of the admission policies **FOMO** and TinyLFU, but primarily for each augmenting ARC. We observe **FOMO** and TinyLFU's performance in different workloads in comparison to the lone internal cache replacement algorithm (as seen in Figure 8). **FOMO** is seen to consistently improve the performance of LRU and ARC, except in CloudCache. For **FOMO**(LIRS), it is more appropriate to consider that **FOMO** is able to have similar performance to LIRS, if not slightly worse, for most cases, with the exception of CloudCache. TinyLFU is consistently introducing performance degradation when compared to the lone cache replacement policy, with the exception of LRU in CloudVPS.  
The CloudCache workload has the admission policies perform poorly. Of the two, we can see that **FOMO** is able to limit the degree of performance degradation it introduces considerably better than TinyLFU does. This appears to be primarily due to CloudCache's workloads having often changing behaviors whose benefits are primarily immediate to their appearance, leaving little benefit to filtering. The rate of change affected both **FOMO** and TinyLFU, but **FOMO** was better able to adapt rapidly and identify opportunities for hits that would otherwise incur an additional miss in the near future.  
Figure 8: Hit rate results for ARC, LRU, and LIRS, along with their **FOMO** and TinyLFU augmented counterparts, with their performances normalized to the best performing. Overall, we can see that **FOMO** is either able to improve the internal cache replacement algorithm, or at worst, as seen in CloudCache, preserve the internal’s performance as much as possible. When observing how TinyLFU fared by comparison, we see it primarily performing worse to much worse than its internal cache replacement’s lone performance. Such an example is observed with the CloudCache workload, but can be seen as well in LIRS and TinyLFU(LIRS)’s performance in FIU.
## 1 Introduction
ALCOAi is used for sophisticated data processing and management of continuously increasing raw data coming from the pharmaceutical manufacturing lines, in conjunction with the strict criteria and rules imposed by pharmaceutical regulatory frameworks, with the overall aim of maintaining ALCOA+ compliance. This model was tested on real pharmaceuticals manufacturing line datasets and also compared and evaluated, in terms of prediction efficiency, against other prominent state-of-the-art time-series forecasting methods. The main contributions of this work are the following:  
* it describes an approach to assess compliance with ALCOA+ principles;
* it describes a model to predict compliance with ALCOA+ principles;
* it evaluates the AI models on a real dataset consisting of data stemming from two different pharmaceuticals production lines. The rest of this paper is structured as follows: Section 2 is dedicated to exploring related work, and Section 3 to the transformer-based network solution employed for resolving the problem of ALCOA+ prediction. Performance evaluation of the proposed model is included in Section 4, along with a discussion and analysis of the obtained results. Finally, conclusions are drawn and future directions are given.
# Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
## 4 Discussion  
We note that parameter tests on other different hyperspectral image data sets may provide different parameter settings. Hence, we advise researchers to perform a parameter study for the initial values in the LRMR method setup for a new test set. A problem-specific parameter configuration is suggested, and it was a goal of this work to highlight and provide a sample analysis as a model for other tests.  
We computed the nonlinear optimization and the main analysis results in MATLAB. A comparison between the computational costs of MATLAB and Python was presented for the integer-valued parameters. The overall behavior of the algorithms is the same in MATLAB and Python. However, the computational times are significantly larger in Python. The reason is the efficient storing and processing of matrix-vector operations in MATLAB. The analysis of computational times is important for applications where the data budget is limited or where the data have to be processed in real-time (therefore, for real-time decision making). It was important to show the similar behavior in relative terms for both implementations--MATLAB and Python--and to discuss the implementation issues that may occur. We observed stable behavior for each implementation, and the relative behavior was the same.  
Regarding future work, other optimization methods can be applied in order to select the optimized initial values for \(p\). We have applied the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method [19; 20; 21; 22] as an alternative to nonlinear optimization. However, the method always stopped after two iteration steps. Alternatively, the use of a machine learning approach might provide additional parameter optimization results and a more efficient optimization approach to choosing \(p\). This would require access to or the creation of a large data set in order to secure efficient training and testing of the machine learning algorithm. However, this is beyond the scope of this article and is ongoing work of the author.  
Figure 18: LRMR result computed on the Pavia University data set for the optimized parameter settings.
# Auto-Encoder Learning-Based UAV Communications for Livestock Management
## 3 Proposed Methodology
### Data Generation, Training and Inference  
We have generated 50,000 message samples, from which 30,000 are the training samples, and 15,000 samples are used for validation and inference each. The auto-encoder is trained with a fixed signal-to-noise ratio (SNR) or energy per bit to noise power spectral density (\(\frac{E_{\lambda}}{N_{0}}\)) of 7 dB using an Adam optimizer [33] with a learning rate of 0.001. The training was performed with various training batch sizes to determine the appropriate size that gives the best performance. The performance of the trained auto-encoder was tested over different SNR values. We have implemented the auto-encoder model in Keras Tensorflow 2 DL framework and 3.8 Python.