esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description  
In the Distributed Permutation Flow-shop Scheduling Problem (DPFSP), a set of \(n\) unrelated jobs are processed in \(F\) factories. A factory should be selected for processing each job. Each factory is equipped with a set of \(m\) machines. These machines are uniformly aligned across all factories, meaning the first machine in any factory has the same characteristics as the first machine in every other factory, and this uniformity applies to all subsequent machines in the series. Each job, when processed in a factory, must sequentially pass through all \(m\) machines under the flow-shop constraint. Every job is composed of \(m\) tasks, corresponding to each machine, and the processing time for each task is fixed, based on the specific job and machine. Also, the following assumptions hold:  
* All factories can process all jobs.
* All machines are available throughout the scheduling horizon.
* Each machine can process at most one job at a time.
* Machine setup times for executing tasks are considered to be 0 or are consolidated to the process time of each task.
* Jobs are independent from each other.
* All jobs are available to start at time 0.
* Each job is processed at one machine at a time.
* The route that each job should follow through the machines of a factory is the same for all jobs and known.
* Once a job is assigned to a factory, it must conclude its processing at this factory.
* Each task should finish processing, once it has started (i.e., preemption is not allowed).  
The solution to the problem is \(F\) sequences of jobs, with one for each factory indicating the production order of jobs for this factory. The desired solution is any solution that minimizes the maximum makespan across all factories. Using the three-field notation (machines/constraints/objective) introduced by [PERSON] et al. in [2], DPFSP is categorized as \(DF/prmu/C_{max}\), where \(DF\) stands for Distributed Flow-shop, \(prmu\) for permutation, and \(C_{max}\) for the maximum completion time among all jobs.  
In PFSP, the size of the solutions space is \(n!\) since a permutation of the \(n\) jobs has to be found. DPFSP is even harder, and in [1], it is proven to have \(\binom{n-1}{F-1}n!\) possible solutions when the optimality criterion is makespan. So, the search space of DPFSP is considerably larger than the search space of PFSP, and since DPFSP is reduced to PFSP when \(F=1\), it can be concluded that DPFSP is NP-complete as PFSP is proved to be NP-complete [3], provided that \(n>F\).
## 2 Research Area  
This section delineates four configurations of glass tube collectors (GTCs) (Figure 1); they are:  
* SGTC with air layer (collector type S1);
* SGTC with a vacuum layer (type S2 collector);
* DGTC with air layer (collector type S3);
* DGTC with a vacuum layer (collector type S4).  
The basic structural and functional unit of all GTCs is the aluminum flat absorber plate (ABS) with an integrated circular cross-section flow channel (Figure 1). A selective SnAl2O3 coating is used to enhance the optical characteristics (absorptivity and emissivity [19]) of the ABS. The internal glass tube (IGT) is a common component for both SGTCs and DGTCs, while the external glass tube (EGT) is characteristic only of DGTCs. The space between the ABS and the IGT is filled with an air layer for the S1 collector type, whereas a vacuum layer is used for the S2 collector type. In the S3 collector type, two air layers are present: one between the ABS and the IGT and another between the IGT and the EGT. In the S4 collector type, these two air layers are replaced with two vacuum layers.  
The geometric characteristics of the GTCs are shown in Figure 2. As illustrated, the width of the ABS is 100 mm, and its thickness is 2 mm. The diameter of the cross-section flow channel is \(\mathcal{O}\)15 mm. The thickness of both the IGT and EGT is 3 mm. The distance between them (in the case of DGTCs) is 6 mm. The lengths of all components (ABS, IGT, and EGT) are 800 mm. The optical-thermal characteristics of the GTCs are presented in Table 1.  
Figure 1: GTC construction types.
## 5 Energy Optimization Approach Based on MAC/Routing Cross-Layer (EOAMRCL)  
To minimize energy consumption in WSNs, we propose a centralized approach with a hierarchical architecture, where the network is partitioned into clusters, and all processes are managed at the BS. Our proposed protocol, EOAMRCL, focuses on both the MAC layer and the network layer, which are essential in self-organizing networks for enhancing performance and addressing scaling issues. This cross-layer protocol offers a comprehensive clustering solution by utilizing an objective function to identify the optimal CHs based on residual energy levels, intra-cluster distances, and inter-cluster distances. Additionally, during the transmission phase, each node creates an active mode/sleep mode schedule based on the NAV, leveraging the MAC layer's duty cycle schedule. This schedule is generated using inter-layer routing information, ensuring efficient and energy-saving communication within the network.
## 2 Data Acquisition and Preprocessing
### Data Preprocessing  
Segmenting plant leaves in the outdoors poses a unique challenge compared with the urban or interior environment commonly used for image segmentation [15]. Obtaining a strict leaf boundary is difficult because of the dynamic illumination conditions, leaf occlusion, and geometric variability between individual leaves [16]. In addition, the changing illumination conditions between images also increase the difficulty of segmentation.
## 4 Experimental Results  
### Application  
Convolutional neural networks consist of diverse parameters that profoundly affect the efficiency of the models. Every genre of the deep learning model receives an input, which in our study cases is the input time series, and by properly adjusting internal trainable parameters, such as the neuron weights and biases, generates an output. Nevertheless, training a deep learning model also entails parameters whose values need to be set a priori, called hyperparameters, whose configuration significantly affects the efficacy of the model. Deducing to the optimal hyperparameter configuration is not a straightforward endeavor, requiring experimentation through trial and error. The genre of inspected and tuned hyperparameter along with the audited values, which in essence are the candidate optimal values, are tabulated in Table 3. The multitude of combinations, as can be inferred by the data in Table 3, suggests that finding the optimal configuration with a manual investigation for the best configuration, where various arrangements are randomly selected, is implausible. Therefore, an automated algorithm is required for this endeavor. For that matter, the grid search is selected since it was deemed computationally affordable after experimentation. More specifically, this algorithm exhaustively searches for the optimum across the entire hyperparameter plane.  
In our study, a parametric inquiry is presented, aspiring to determine not only whether CNNs are applicable for CBM schemes in marine engineering, but also to examine the effect of significant factors, such as the degree of detected degradation and the input time frame, to the efficacy of the models developed [33].  
Figure 11: Signal of the average temperature among the main engine’s six cylinders for 8 h.
## 3 WBAN Requirements  
### Introduction  
WBANs are concerned with the remote monitoring of key physiological signals, therefore, systems need to maintain high levels of reliability and low levels of latency. Sensor devices have relatively low data rates but energy efficiency and node battery longevity are fundamental to enable devices to maintain connectivity for long periods of time. Patients are likely to be mobile and WBANs will be processing key sensitive data that must be securely processed. Nodes are likely to be worn continuously. Patients must have confidence in the technology and be comfortable with the device on a daily basis.
## 4 Synthesis of Seismic Waves  
To simulate earthquake action, real second and third site conditions are considered. This study employed the ground motion synthesis software EQsignal v1.2.1, taking GB50011-2010 as the target spectrum. The characteristic periods were varied (T\({}_{\text{g}}\) = 0.45 s; T\({}_{\text{g}}\) = 0.40 s; T\({}_{\text{g}}\) = 0.35 s), and three kinds of non-pulse ground motions and pulse ground motions were synthesized. The peak acceleration of non-pulse ground motions with different character periods were 1.0 m/s\({}^{2}\), and the peak accelerations of pulse ground motions were 1.39 m/s\({}^{2}\), 1.22 m/s\({}^{2}\), and 1.40 m/s\({}^{2}\), respectively, with a time interval of 0.01 s and a total time length of 30 s [27, 28, 29].  
This study considers varying damping ratios and examines the displacement and shear stress responses of a tall bridge pier subjected to one-, two-, and three-dimensional ground motions. The acceleration time history and spectrum curves of tall bridge piers under non-pulse and pulse ground motions are shown in Figures 2 and 3, respectively. This investigation focuses mainly on the effect of multidimensional ground motions and different damping ratios on tall bridge pier behaviour, and provides valuable insights into the design of seismic-resistant bridge structures.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Physical Parameters** & **Values** \\ \hline Density & 2430 kg/m\({}^{3}\) \\ Elastic modulus & \(3.2\times 10^{10}\) N/m\({}^{2}\) \\ Poisson’s ratio & 0.191 \\ Damping ratio & 3\%; 5\%; 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of the bridge pier material.  
Figure 1: Calculation model of tall bridge piers under multidimensional ground motion.  
Figure 2: Acceleration time histories and acceleration spectrum curves of non-pulse ground motions.  
A comparative analysis of Figures 2 and 3 was performed to study the significant differences between the acceleration time history and acceleration response spectrum curves for pulse and non-pulse ground motions. These differences underscore the contrasting spectral characteristics of the two types of seismic waves. Within 5 to 10 s, the acceleration time history curves for pulse and non-pulse ground motions exhibit distinct variations. This finding demonstrates that the spectral performance of earthquake waves undergoes alteration subsequent to the introduction of the pulse to the seismic wave time history. There are significant variations in these spectral characteristics of earthquake waves as a function of various characteristic periods.
## 5 Evaluation
### Evaluation Metrics  
In our experiments, we utilize a variety of evaluation metrics to monitor the model's performance throughout the training and testing phases. Our primary metrics include accuracy, F1-score, and loss. We also record the overall times for training, validation, and testing. In terms of privacy evaluation, we adopt qualitative metrics as proposed by [9].
# Integration of _Salmonella_ into Combination Cancer Therapy
### Broad Tumor Specificity  
Several pre-clinical studies conducted in the past decades have reported the effective therapeutic index of _Salmonella_. This success was achieved at the level of tumor growth, metastasis, and host survival. The effectiveness of _Salmonella_ treatment has been documented against a broad-spectrum of murine tumor models, including melanoma [35; 36], colon cancer [37], lung cancer [38], prostate cancer [39], cervical cancer [40], metastatic T cell lymphoma [41], and others. In particular, _Salmonella_-mediated inhibition of metastasis was observed in different tumor models such as breast cancer [42], osteosarcoma [43], and dorsal spinal cord gliomas [44]. Moreover, the anti-tumor effects of _Salmonella_ were demonstrated in metastatic patient-derived orthotopic xenograft (PDOX) murine models of osteosarcoma [45], melanoma [46], soft tissue sarcoma [47], and follicular dendritic cell sarcoma [48]. In addition to _Salmonella_'s efficacy against a wide range of tumor types, the different routes of _Salmonella_ administration, including intravenous, intra-arterial, intraperitoneal, and intratumoral routes, have shown success in inhibiting tumor growth.
# An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
###### Abstract
Although there is a lack of specific mathematics and a universally accepted definition of interpretability, this term typically pertains to the causal relationship between inputs and outputs [20]. [PERSON] et al. [21] provides a comprehensive review and detailed categorization of methods for explaining machine learning models, including various deep learning networks. Some new interpretability methods have been developed to explain the predictive patterns captured by the machine within the recurrent units of LSTM networks [22; 23; 24]. Most interpretability methods were initially designed for symbolic sequences in natural language processing, which is different from analyzing the relationships between oceanic variables with specific physical meaning and real-world context. Explainable artificial intelligence aims to build more understandable models while maintaining a high level of performance, making the decisions of deep neural network models more transparent, interpretable, and trustworthy for humans. However, few studies have explored the underlying principles behind LSTM network decisions, especially regarding the exact role of the well-known memory mechanism in predicting oceanic variables. Therefore, new methods are needed to address the heterogeneity of the data in multidimensional-level prediction and make these predictions interpretable. In this article, inspired by [PERSON] et al. [25], we introduce a novel interpretable artificial intelligence approach that focuses on the identification of marine heatwave patterns. Particularly, understanding the causes of marine heatwave generation in the context of climate change helps in identifying and mitigating the risks of flood disasters. We establish an LSTM-based model, selecting 13 coastal stations in China. Using two advanced interpretability techniques, namely Expected Gradients and Additive Decomposition, we analyze the variations of information hidden within the network to reveal the role of the model in identifying marine heatwaves.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 2 Materials and Methods
### Training Data  
The generation of training data was an iterative process (Figure 3). Initially a subset of the existing QLUMP data in the study area was edited to better represent the land use features within the  
Figure 2: This diagram shows the three-tiered hierarchical structure of the Australian Land Use and Management (ALUM) classification and an extract demonstrating bananas as a commodity within the “Tree fruits” tertiary class, “Perennial horticulture” secondary class, and “Production from irrigated agriculture and plantations” primary class.  
2015 imagery. This editing was required as the QLUMP data were compiled using lower resolution imagery (SPOT 6 with a resolution of 1.5 m) and mapped land use features at a scale of 1:50,000 (using a minimum mapping unit area of 2 ha and width of 50 m) [13; 49]. From these data, image and corresponding mask chips were randomly generated for model training. An initial inference was produced, converted to a polygon using a prediction probability threshold of 50%, and edited to fix any areas of omission and commission errors. This resulted in a more accurate and detailed training dataset compared to the QLUMP data. The image and mask chips were then regenerated for additional training.  
A total of 91,129 image chips with a size of \(256\times 256\) pixels were randomly generated from the 2015 and 2018 imagery. Of the total number of chips, 16,560 (18.2%) contained banana plantations, with the remainder located over a range of other land uses. No data were generated which intersected the Tully River catchment (Figure 1b) and this area was not included in the training stage of the study.
## 1 Introduction
Additionally, sliding mode control can precisely control the output voltage and current, making it well-suited for applications requiring high accuracy and efficiency. Several applications have been reported for converters controlled with sliding-mode-based controllers. For example, in [20], a high-gain SMC algorithm was designed for the  
Figure 1: (**a**) A boost inverter; (**b**) equivalent circuit with voltage sources; (**c**) voltage signals constituting the output voltage. power flow regulation of a bidirectional converter; in [21], a generalized super-twisting algorithm was designed to control a DAB converter. Overall, sliding mode control offers a promising approach to achieving accurate and efficient control of power boost converters in various power electronics applications [20; 21; 22; 23; 24; 25; 26]. In this work, a direct control approach is used to solve the problem of tracking a DC-biased sinusoidal signal for the capacitor voltage on a DC-DC boost converter. This approach is based on a robust higher-order sliding mode output regulation technique based on the super-twisting algorithm. The unstable zero dynamics related to the inductor current variable are stabilized with a properly selected sliding surface. The reference signal chosen for the current through the inductor is an approximate solution to the corresponding Francis-Isidori-Byrnes equation. The article is organized in the following manner: Section 1 presents the introduction, and Section 2 presents the model of the DC-Dc boost converter and the design of its output regulator based on a higher-order sliding mode controller. Then, Section 3 deals with the experiments performed to verify its performance. Lastly, Section 4 presents the conclusions.
# A Lightweight Efficient Person Re-Identification Method Based on Multi-Attribute Feature Generation
## 2 Related Work
### Deep-Learning Person Re-ID Methods  
Recently, _deep-learning_-based methods for person re-ID have become popular and offer high performance. These methods are inspired by the perception mechanism of humans, who are equipped with multiple neural nodes for this problem. [PERSON] et al. [8] designed a novel lightweight person ReID model, called the Multi-Scale Focusing Attention Network (MSFANet), to capture robust and elaborate multi-scale ReID features, which has a lower float-computing and a higher performance.  
[PERSON] et al. [18] proposed a non-local temporal pooling (NLTP) method used for temporal features' aggregation for person re-ID. Their method would effectively capture long-range and global dependencies among the frames of the video.  
[PERSON] et al. [8] proposed a lightweight model called the Multi-Scale Focusing Attention Network (MSFANet) for person re-ID. The proposed method had designed a multi-branch depth-wise separable convolution module to extract and fuse multi-scale features independently.  
[PERSON] et al. [19] have proposed a lightweight network, which mainly combined the global, part-based, and channel features in a unified multi-branch architecture for person re-ID. The network structure they designed to obtain the pedestrian feature was mainly exploited the pooling operations (MaxPool and AvgPool) to the initial feature maps. In our work, we have excavated three kinds of attributes (descriptors) equipped with the accessories feature, the body contour, and the appearance color to re-identify the specific persons, respectively. These descriptors all have specific meanings for each person via constructing a network architecture accordingly that is essentially different from it.  
[PERSON] et al. [9] had utilized the person attribute information into the classification framework with three sub-networks, called the Global Feature Network (GFN), the Part Feature Network (PFN), and the Attribute Feature Network (AFN), which is called AANet. This method was devoted to excavating multi-attributes for each person who was not a specific one.  
In this work, the multi-attribute has the same concept as the descriptor. Specifically, we exploited the accessory attribute network to grasp the person accessory descriptor. The body attribute network was engaged in learning the person region structure with a Spatial Pyramid operation to capture the body descriptor, and the color attribute network was devoted to maintaining the color consistency of each person for a color descriptor. These descriptors all have the specific meanings for each person in our work, and it is essentially different from the AANet [9].  
The present study differs from previous works in the following major ways: (1) it proposes a lightweight multi-attribute feature-generation method for person re-ID, and (2) it explores the applicability of the human visual perception mechanism in person re-ID technology.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### Distribution of Ranges
#### 4.1.3 Nakagami-\(m\) Distributed Ranges  
In Figure 4, Rayleigh fading distribution is plotted with a left skew around \(\alpha>2\) value seen. Similar to Figures 2 and 3, the range distributions are heavily impacted by the path-loss coefficient, \(\alpha\). This is in part due to the dominant NLOS signal between communicating vehicle nodes and the existing BVS nodes. As the fading worsens for inter-vehicle distances beyond 50 m or for \(m\leq 1\) values (i.e., \(m=1\) for Rayleigh distribution), the communication range is Weibull-distributed with parameter \(C\).  
Figure 4: Nakagami-\(m\) Range distributions for \(\alpha=1,2,3,4,5\). (**a**) Nakagami-\(m\) PDF, \(m=3\). (**b**) Nakagami-\(m\) CDF.  
Figure 3: Rayleigh range distribution. (**a**) Rayleigh PDF, \(\sigma=1\). (**b**) Rayleigh CDF.