esa-sceva/satcom-chunk-collection huggingface dataset:
# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 2 Literature Review  
### Scheduling in Semiconductor Manufacturing  
Manufacturing systems are affected by real-time events, which, in turn, impact manufacturing schedules. Mathematical programming models have been used to solve semiconductor scheduling problems. [PERSON] et al. [13] introduced a dynamic scheduling approach to prioritize the release policies of the hybrid make-to-stock/make-to-order (MTS/MTO) issue. By using heuristic algorithms, [PERSON] et al. [14] evaluated dispatching rules in two low-mix, high-volume wafer fabrication facilities. Based on rigorous modeling and statistical analysis, [PERSON] [15] proposed a hybrid intelligent algorithm coalesced with a GA (genetic algorithm) and a novel encoding method named bi-vector to solve scheduling problems with multi-mode resource constraints. [PERSON] et al. [16] created a bi-objective mixed integer linear programming (MILP) model to shorten the cycle time and boost resilience. A Pareto-optimal solution can be obtained by solving the single-objective MILP model to reduce the cycle time with fixed robustness or to maximize robustness for a given cycle time. The suggested method's efficacy was confirmed by simulation studies conducted on benchmarks and a small number of other randomly generated projects. [PERSON] et al. [17] created a MIP (mixed integer programming) issue to reduce makespan based on machine eligibility requirements, release time, and parallel batch and then used a heuristic approach to find the optimal solution. [PERSON] [18] proposed a mathematical model to determine the number of wafers to be processed in the photolithography area, in which the setup time is incurred when the type of wafers was changed on the equipment to maintain the target work-in-process level, minimize cycle time, and to maximize throughput. [PERSON] et al. [19] developed a mini-max regret approach for the improvement of equipment utilization in semiconductor manufacturing. Based on the binary integer programming method, [PERSON] et al. [20] proposed a heuristic scheduling method to reduce the cycle time of the parallel-batch processing in flow shops. They obtained optimal real-time scheduling performance by accounting for necessary batch processing. Although many studies have addressed performance optimization, most employed open-loop ideology and were not performance-driven. In other words, they optimized performances aimlessly and could not form a closed-loop system, which is self-adaptive to the capricious manufacturing environment.
## 4 Attacks and Misbehaviors
### Protocol Specific Attacks  
There are several main types of attacks or misbehaviors directly related to CENTERA and its functions. As discussed in Section 3, the BS in CENTERA sets and distributes the routing paths from every node to itself. Thus, the first type that the BS detects is the receipt of a packet on a different path than designated. Knowing that such misbehavior requires the collusion of two nodes, the BS performs the proper checks, and distinguishes a couple of two candidate colluding nodes. The BS puts these nodes on probation and keeps them under surveillance; and isolates whichever set repeating the error.  
Another type of protocol specific attacks is a node sending a wrong message format; thus disregarding the rule that in a report the DL should be the first node, followed by the UL (if any) followed by the rest of the neighbors (if any). So any node sending not conforming to this rule is detected as a bad node by the BS. Note that the node may be a malfunctioning node just misplacing its neighbors or a bad node deliberately changing the positions to decrease the trust of its neighbors. It may even be actually trying to send its packets not through its DL and forwarding the packets of a non-UL neighbor. In any case, this node is considered bad and negatively affecting the proper functioning of CENTERA, and thus, it is directly banned by the BS.  
A similar kind of misbehavior is the manipulation of counters that results in a negative number of packets initiated by the node. As described in Section 3, the number of packets initiated by a node is calculated as the difference between the pcounter to the DL and the sum of the pcounters to the UL neighbors. If this difference results in a negative value, the node may be malfunctioning or deliberately manipulating its counters and should be banned from the system.
## 3 Sub-Problem 1
### Two Examples  
In this section, two examples are shown to demonstrate the feasibility of autonomous orbit determination using only the inter-satellite range data. In the tests, only two spacecrafts and one link are considered. The initial orbital elements are \(\sigma_{1}=[3,0,90^{\circ},60^{\circ},60^{\circ},0^{\circ}]\) and \(\sigma_{2}=[3,0,0^{\circ},0^{\circ},0^{\circ},0^{\circ}]\). For the initial estimation, a random error (with a threshold of 100 m) along all three directions is added to the position vector of both the chief and the deputy. The measurement interval is 0.05(T). The only difference between the two tests is the arc length used for orbit determination. It is 50(T) for test 1 and 100(T) for test 2. The result is shown in Figure 2. The left frame shows the residual in the observation data, and the right frame shows the position errors of the determined orbit with respect to the true one. The blue line is the position error of satellite 1 and the red line is the position error of satellite 2.  
For test 1, the residual of the observation reaches the accuracy level of the observation data, and the position errors are smaller than 0.4 m. For test 2, where the arc length is longer, both the residual in the observation data and the position errors increase when compared with test 1. The reason for this phenomenon is that the force model for the orbit determination is different from the'real' force model which we use to generate the simulated observation data. When the length is too long, the difference between the two force models will cause the decrease of accuracy. To find an appropriate arc length, we conducted some further tests and displayed the relationship of the \(RMS\) with respect to the arc length in Figure 3. The abscissa is the arc length, and the ordinate is the \(RMS\) of the residuals. \(RMS\) is calculated by the following equation  
\[RMS=\sqrt{\frac{\sum(\rho_{i}^{\prime}-\rho_{i})^{2}}{n}} \tag{12}\]  
Different from Equation (5), here \(\rho^{\prime}\) is the calculated value and \(\rho\) is the observed value.  
From Figure 3, the accuracy of \(RMS\) is the best with 40(T) to 80(T) arc length. When the arc length is larger than 80(T), the accuracy decreases quickly. By our calculation, for most cases, the arc of 50(T) is sufficient.  
Figure 2: Residual in the observation data (**left**) and norm of the position error of the determined orbit from the true one (**right**).
## 4 Materials and Methods
### Biocontrol on Phytopathogenic Fungi under Salinity Conditions  
_Bacterial suspension:_ Cultures of _P. putida_ strains were diluted in a sterile physiological solution to a final concentration of 10\({}^{9}\) CFU/mL. Then, 100 uL of the resulting suspension was spread on Hoagland medium modified with 3% sucrose and 1.5% agar and allowed to grow for 24 h at 30 \({}^{\circ}\)C. The same procedure was carried out for control (with only 100 uL of sterile physiological solution, but without adding the strains). Fungal inoculum _Alternaria alternata_ and _Sclerotium rolfsii_ were used as fungal pathogens in the genus _Mentha_. These pathogens are known to cause economic losses by inflicting a heavy defoliation of the host, which affects both the yield and overall quality of mint oils ([PERSON] _et al._, 2011; [PERSON] _et al._, 2011). _S. rolfsii_ and _A. alternata_ were grown on PDA medium (Britania(r), Kolkata, India) in dishes for 5 and 7 days respectively. After this period, a disk of 5 mm diameter was obtained from the dishes and inoculated on sterile PDA dishes.  
_Exposure to volatiles:_ The bases of the Petri dishes, with 2 days of growth of bacteria and fungi, were inoculated and placed one against the other and sealed. By this method, _S. rolfsii_ and _A. alternata_ were exposed at 28 \({}^{\circ}\)C to bacterial VOCs without having any physical contact between them, and the diameter of the fungal colony was measured every day until days 4 and 6 of growth, respectively [94]. These experiments were repeated in triplicate, with five replicates being made for each treatment and control.  
Hoagland medium (bacterial media) was supplemented with salt concentrations (a) 0 mM NaCl, (b) 75 mM NaCl, and (c) 100 mM NaCl and PDA media (fungal media), which in all cases were used as control conditions (0 mM NaCl). The fungal media were not supplemented with salt because fungi develop naturally on vegetal tissue.
## 3 Results
### Noise Equivalent Power  
In this work, we present our measurements of the total NEP, the bolometer NEP and the amplifier NEP. The amplifier NEP is determined by dividing the measured voltage noise of the amplifier and the responsivity. The total NEP is obtained by measuring the voltage noise at the output of the preamplifier, which was then divided by the measured responsivity of the device. The bolometer NEP was then estimated from Equation (2), by subtracting the amplifier NEP from the total NEP. The total NEP, bolometer NEP and NEP of the amplifier from 35 Hz to 2 kHz is shown in Figure 3. The theoretical predictions for the total NEP, bolometer NEP, and NEP due to the amplifier are shown as dashed lines. At low frequencies, we include the 1/f noise due to the amplifier in our theoretical fit which agrees quite well with the experimental results at lower frequencies. As seen in Figure 3, the total NEP for all modulation frequencies is in the order of \(10^{-18}\) W/Hz\({}^{1/2}\). The maximum measured bolometer NEP is \(5\times 10^{-18}\) W/Hz\({}^{1/2}\) at 35 Hz, while the minimum measured bolometer NEP is \(10^{-18}\) W/Hz\({}^{1/4}\) at 2 kHz. Photon noise at the level of \(10^{-18}\) W/Hz\({}^{1/2}\) is for a signal of 10 fW at a frequency of 80 GHz. That means that for the power and frequency of the signal higher than these values, photon noise will dominate over the noise of CEB.
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Providing Multiple QoS Constraints
#### 2.2.1. Constructed Metric Technique  
With this technique, the protocols use accumulated constructed metric, which is a function of other metrics such as bandwidth and delay, to prioritize the various path segments.  
The protocol LACMQR [28] is an example. The constructed metric is a function of several metrics (e.g., delay and cost). When an intermediate node receives a probe packet, it calculates the value of constructed metric of this probe packet. If the value of constructed metric of the new probe is better than that of the previous probe, the node changes its predecessor to the node that forwarded the new probe packet. As a better path has been found, the probe will be forwarded; otherwise, the probe packet is discarded.
# A Broadband Analog Predistortion Linearizer Based on GaAs MMIC for Ka-Band TWTAs
## 2 Analog Predistortion MMIC Topology
### Nonlinear Frequency Adjustment Module Design for Broadband Application
#### 3.2.2 Simulation of the NFAM Effect  
The influence of the capacitance value on the phase expansion difference is shown in Figure 6a. A capacitance value of 65 fF was used in the APD MMIC. The gain and phase conversion in the operating band are shown in Figure 6b. The gain and phase under small-signal excitation are normalized to 0 dB and 0\({}^{\circ}\), respectively. The gain and phase conversion differences were about 1 dB and 13\({}^{\circ}\), respectively, which is consistent with the corresponding differences of the TWTA in the operating band.
## Appendix A  
Accurate FEM stress analysis is required for calculation of the ILT strength and inverse characterization using the FEMU approach. In particular, the FE mesh used in this study must be refined such as stress concentrations around the hole in the OHC specimens are correctly captured. Appendix A provides results of a mesh sensitivity study demonstrating convergence of the interlaminar normal stress field as the mesh is refined.  
An example of unidirectional OHC coupon 2D plane-stress FE mesh and contour plot of the interlaminar normal stress component \(\mathsf{S}_{33}\) normalized by the far field longitudinal normal compression stress is shown in Figure 11. In this example, orthotropic linear elastic properties for the generic carbon/epoxy tape material considered in this work are used. The mesh is refined as illustrated in Figure 12, where mesh density is defined as a function of the number of elements along the hole radius. Figure 12 shows details of the resulting FE mesh for a circumferential mesh density of 12, 16, 80 and 160 elements. The full mesh shown in Figure 11 corresponds to a circumferential mesh density of 80.  
The normalized interlaminar normal stress is plotted along the path illustrated on Figure 17 for verification of stress convergence. Figure 18(a) shows the stress plots along the selected path for 12, 16, 40, 80 and 240 circumferential element densities. Figure 18(b) shows convergence of the interlaminar tensile stress concentration factor \(K_{t}^{max}\), which is equal to the maximum value of the normalized interlaminar tensile stress at the hole boundary, as a function of circumferential mesh density.  
As shown in Figure 18, convergence of the stress field around the hole is rapidly achieved. In particular, the relative absolute error between \(K_{t}^{max}\) obtained using a circumferential mesh density of 40 and \(K_{t}^{max}\) for a mesh density of 240 is only about 0.4%. For a circumferential mesh density of 80, this error drops below 0.1%.  
For all the OHC coupons FE meshes used in this work, a circumferential mesh density of 80 elements was used. A stress field convergence similar to the results shown in Figure 18 was observed using homogenized orthotropic material properties representative of the fabric material considered in this work.
# Establishing Two-Dimensional Dependencies for Multi-Label Image Classification
## 4 Experiments
### Experimental Results
#### 4.4.1 Comparisons with State-of-the-Art Methods  
**Comparisons on PASCAL VOC 2007**. The VOC 2007 dataset serves as a standard benchmark for evaluating the capability of image classification and recognition. The dataset consists of a training set (5011 images) and a test set (4952 images), comprising a total of 9963 images across 20 categories. To ensure a fair comparison with other state-of-the-art methods, we report the results in terms of average precision (AP) and mean average precision (mAP). The experimental results on the VOC 2007 dataset are presented in Table 1, where we compared our method with 13 other state-of-the-art approaches. Thanks to the establishment of our two-dimensional dependencies, our approach yielded significantly better results compared to methods that solely rely on image feature extraction or those that build one-dimensional dependencies. Our proposed model achieved an mAP of 96.5%, indicating superior performance compared to the other methods. **Comparisons on PASCAL VOC 2012. ** The PASCAL VOC2012 dataset is a challenging and widely recognized benchmark in the field of computer vision, and it is commonly used for tasks such as object detection, image classification, and semantic segmentation. It consists of a diverse collection of images, each annotated with detailed labels that specify object boundaries, categories, and other relevant information. Our experimental results, as presented in Table 2, demonstrate that our network achieved an impressive 96.0% on the comprehensive evaluation metric mAP. This performance surpasses the current state-of-the-art by 1.2%. This improvement can be attributed to the effective two-dimensional dependencies we established, which simultaneously consider both image feature dependencies and label semantic dependencies. Additionally, global relationships were also taken into account when constructing these dependencies.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 2 Background
### Time Series Transformer Models  
Transformer-based time series models have emerged as a promising approach for analyzing and extracting patterns from sequential data, drawing inspiration from the success of foundation models in natural language processing. These models are pre-trained on massive datasets, enabling them to capture complex temporal dependencies and generalize to various downstream tasks. The architectures employed in time series data analysis are diverse and rapidly evolving; transformer-based models are the most common. They leverage a self-attention mechanism to capture long-range dependencies and have shown remarkable success in time series forecasting. Examples include Informer [22], which addresses efficiency in processing long sequences; Autoformer [26], designed to improve model interpretability and adaptability; and PatchTST [30], which optimizes performance for high-dimensional time series data. Alongside transformers, architectures like the temporal fusion transformer (TFT) [31] have emerged, combining elements of recurrent and convolutional layers to handle both temporal dynamics and static covariates. Additionally, simpler architectures like the DLinear [32] model employ linear transformations, providing computational efficiency at the expense of expressive power. These models are often trained on large amounts of prepared data using powerful computational hardware such as GPUs to accelerate the training process. We will describe the process of time series data preparation and the data pipeline required to facilitate the training process in the next subsections.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 4 Discussion
### Analysis of the Influence Factors
Input data transformation has been recognized as an impactful pre-processing task when using machine learning algorithms such as ANN models. Accordingly, the remote sensing data of this study were transformed using the histogram threshold scaler and the maximum-minimum scaler approach. [PERSON] et al. [74] proved the capability of the Adam learning algorithm in handling untransformed input data, which were also employed in WaterUNet. Although these factors affect the classification accuracy, WaterUNet still extracts water body data with a high accuracy, which also proves the robustness of WaterUNet.
## 1 Introduction  
Hyperspectral remote sensing obtains information from the visible and the near-infrared spectrum of reflected light. Agriculture, mineralogy, physics, surveillance, and so on are just some examples of applications of the hyperspectral images.  
This technology is continually in evolution and it is gradually becoming available to the public.  
In military and civilian applications the remote acquisition of high definition electro-optic images has been increasingly used. Moreover, these air-borne and space-borne acquired data are used to recognize objects and to classify materials on the surface of the earth.  
It is possible to recognize the materials pictured in the acquired three-dimensional data by analysis of the spectrum of the reflected light.  
NASA and other organizations have catalogues of various minerals and their spectral signatures. The new technologies for hyperspectral remote sensing acquisition allow the recording of a large number of spectral bands, over the visible and reflected infrared region. The obtained spectral resolution is sufficient for an accurate characterization of the spectral reflectance curve of a given spatial area.  
Higher resolution sensors will be available in the near future that will permit to increase the number of spectral bands. In fact, the increase in the number of bands, _i.e._, spectral resolution, shall make possible a more sophisticated analysis of the target region.  
The volume of data generated daily by each sensor is in the order of many gigabytes and this brings an important need for efficient compression.  
Figure 1 shows a sample of data cube for a hyperspectral image. The X-axis indicates the columns, the Y-axis indicates the rows and the Z-axis indicates the bands.  
We focus our attention on visualization, band ordering and compression of hyperspectral imagery.  
Section 2 discusses the visualization of hyperspectral images. We introduce a tool for hyperspectral image visualization and present an implementation on Google Android OS.  
Section 3 addresses the compression problem by considering an efficient and low complexity algorithm for lossless compression of three-dimensional hyperspectral data, called the Spectral oriented Least SQuares (SLSQ) algorithm.  
Section 4 presents an efficient band ordering based on two metrics: [PERSON]'s correlation and [PERSON] distances, which can lead to improvements to the SLSQ algorithm. Section 5 reports the experimental results achieved by a Java-based implementation respectively without band ordering, with [PERSON]'s correlation-based and with [PERSON] distance-based band ordering. Section 6 presents our conclusions and discusses future works.  
Figure 1: Example of data cube for a hyperspectral image.
# StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks
## 3 The Proposed Method
### Discussion  
In this section, we analyze the computational complexity, characteristics and limitation of the proposed method. The time complexity mainly lies in matrix multiplication. What is more, the dimension and sparsity of the matrix also have a certain impact on efficiency. The complexity is \(\boldsymbol{O}\left(m^{3}(d_{S}-1)\right)\) for an instance of a meta structure \(S\) with \(d_{S}\) depth, while \(m\) is the average matrix dimension. In addition, the Node-Composition Operator may also cause an increase in matrix dimensions, especially in a large-scale information network.  
In order to better analyze the characteristics of the proposed method, we have concluded the overall relations between different measures and the computation formulas in Table 1. They are based on some basic strategies including path/graph count or walking/expansion probability. Although these methods can also capture semantics between objects, it is not sufficient for the utilization of the structure information of a network. Therefore, we present the strategy of expansion with bias in this paper in order to make full use of network information. Moreover, for the complex structure expansion, one must also face the problem that multiple nodes expand at the same time. To solve this, some strategies need to be devised. In this paper, we design the operator of Node-Composition. Based on the operation, we can make the subgraph expansion much easier.  
These above strategies reveal the difference between StructSim and BSCSE. BSCSE simulates the process of structure-constrained subgraph expansion. StructSim further introduces the bias and operator of Node-Composition based on BSCSE, which can well capture comprehensive information between objects with complex semantic meanings. This property also implies that StructSim is more effective than BSCSE, since StructSim,meanwhile, considers both kinds of information for probability computation, not solely out-neighbor information. The limitation is that the efficiency of matrix multiplication is a vital issue that needs to be addressed. Besides this, by adopting the Node-Composition Operator in some layer of the meta structure, the number of composition nodes with the same type could be large, which is inefficient both in terms of space to store them and in terms of the operation of matrix multiplication. Fortunately, there have been several quick computation solutions, including dynamic programming and Monte Carlo [11].