esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Activation Pooling for Blind Image Quality Assessment
## 1 Introduction
In [17], the quality score is described as a simple distance metric between the distorted image and the model statistic. [PERSON] et al. [18] proposed the quality-aware clustering scheme to assess the quality levels of image patches. [PERSON] et al. [19] proposed the integrated local natural image quality evaluator (IL-NIQE) to predict image quality for opinion-unaware BIQA tasks. Firstly, they utilize five types of features to obtain the feature vectors of pristine naturalistic images, that is, the mean-subtracted contrast normalized (MSCN)-based features, MSCN product-based features, gradient-based features, log-Gabor response-based features and color-based features. Secondly, they use the feature vectors of pristine images to train a MVG model. Then, each patch in the test image is utilized to train a MVG model, and the parameters of the MVG model are compared with the trained MVG model to generate a quality score for the local patch. Finally, the overall quality score is computed by using the average pooling strategy. However, the average pooling lacks discrimination because the contributions of the patches are equally treated, whatever their importances are. In this paper, we propose a novel approach for opinion-unaware BIQA. Figure 1 illustrates the procedure of the proposed approach. Firstly, we utilize the convolutional maps of a pretrained convolutional neural network (CNN) for high-contrast patch selection. The convolutional activations represent the structural and textural features, and therefore the selection process could choose the salient patches and drop the meaningless ones for the subsequent assessment. Then, we utilize these selected patches of pristine images to train a pristine multivariate Gaussian (PMVG) model. In the test stage, each high-contrast patch in the test image is fitted by a test MVG (TMVG) model, and the local quality score is obtained by comparing with the PMVG. Finally, the deep activation pooling (DAP) is proposed to aggregate these local quality scores into an overall image quality score. We employ the convolutional summing map (CSM) computed from the convolutional layer to learn the aggregating weights. Specifically, each selected high-contrast patch is mapped to the corresponding activation-response region, and all the activation responses in the corresponding region are added as the aggregating weight. Hence, the proposed DAP could automatically obtain the aggregating weights by learning the importance of local quality scores from the CSM. In summary, the major contributions of this paper are: (1) We use the high-contrast patches chosen based on convolutional activations to train the PMVG and TMVG models, which captures the primary structural and textural information of images and obtains more accurate PMVG and TMVG models; (2) we compute local quality scores based on the PMVG and TMVG models, and propose the DAP to automatically learn the aggregating weights of local quality scores from CSM, which produces more accurate overall quality score.
## 2 Materials and Methods
### Laboratory Procedures
The BMP180 sensor is regularly used to measure atmospheric pressure. It is manufactured by Bosch Sensortec company. The BMP180 sensor is a piezoresistive sensor that detects pressure. Piezoresistive sensors are made up of a semiconducting material (usually silicon) that alters resistance when a mechanical force, such as atmospheric pressure, is applied [32]. The manufacturer provides datasheets and libraries to help with a BMP180 sensor integration. The BMP180 can deliver exact pressure measurements with a resolution of up to 0.01 hPa (hectopascals) [32]. The BMP180 sensor has a measuring range from 300 to 1100 hPa [32]. The DHT11 comprises two sensors: a temperature sensor and a humidity sensor. Its temperature readings range from 0 to 50 \({}^{\circ}\)C, and its humidity readings range from 20 to 90%. The sensor for measuring air temperature and air relative humidity has a simple 3-pin connection, enabling programming and connectivity with other sensors. It has two power supply pins and one pin for data decoding between the sensor and the Arduino board [33]. DHT11 sensor is small and has an operating voltage of 3 to 5 V.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## 4 A Threshold Determination Method
Table 1 shows example values for the threshold calculated using Equation (7) with \(p_{th}=0.7\) and codeword length \(N=64\).
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 2 Design Issues and Routing Challenges in Green Industrial IoT Networks
### Resource Constraints and Interoperability  
IoT devices have limited resources in terms of energy supply, storage capacity, processing power, and transmission capacity due to their physical design conditions. Because of the hardware constraints, the routing approach should be implemented with minimal code and require a low routing state to be suitable for the device capacity. As a result, the routing design and implementation of IIoT concept are subject to some restrictions. In this regard, 6 LoWPAN technology enables nodes to reduce power consumption and improve robustness due to its low complexity. Overall, these limitations are reflected in the protocol performance, so a lightweight routing protocol with low computational and energy requirements are needed. Moreover, some use cases in IIoT require users to deploy a large number of sensing and monitoring devices to be effective. Node placements range from hundreds to thousands, and the minimality should be considered for maximum benefit and utilization of resources. Therefore, seamless interaction with external networks (i.e., cloud or Internet) infrastructure is essential for making the IEMS more intelligent and pragmatic. Different techniques, such as in [20; 21], can be adopted to overcome the energy challenges of routing algorithms.
## 2 Related Work
With the integration of edge learning in the IIoT network to resolve resource-constrained IIoT devices, securing data transmission with the blockchain becomes crucial, along with the transfer learning model for the federated learning environment, to increase the versatility and efficiency of the training model. As a collaborative learning strategy to handle IoT device training issues with a large dataset, deep transferring models were used in [29] for industrial IoT environments. The authors of [30] presented a deep-transfer-learning-based traffic classification model for the 5G IoT environment. Such a model finetunes the classification model with a small set of labelled data by extracting the abundant labels of multiple domains. To denoise the raw ECG signal of patients in IoT healthcare, the model proposed in [31] utilises a federated-learning-assisted CNN-based autoencoder. Similarly, the authors of [32] proposed a federated-transfer-learning-based IDS model for the Internet of Medical Things environment. Such a model performs unique reconstruction with raw input and provides unique data to the federation process, resulting in improved healthcare IoT performance. It runs the classification algorithm on edge devices to handle the issue of network resource scarcity. However, most federated-learning-assisted IDS models solve the intrusion detection issues of heterogeneous IoT environments, whereas there are no specified FTL-based IDS models for heterogeneous RPL-IoT environments.
## 5 Conclusions
; resources, J.A.-N.; validation, F.J.L. and F.L.B. ; writing--original draft, F.J.L. ; writing--review and editing, F.J.L., F.L.B., G.R.-T. and J.A.-N. All authors have read and agreed to the published version of the manuscript. This research was funded by a Russel E. Train Fellowship from World Wildlife Fund, as well as from off-campus research fellowships from the Graduate School at the University of North Carolina at Chapel Hill. The publication of this article was funded by the Universidad San Francisco de Quito USFQ Research Publication Fund--granted to [PERSON], and the Galapagos Science Center. We would like to thank staff of UNC's Galapagos Science Center (GSC), USFQ's Instituto de Geografia, Ministerio de Agricultura del Ecuador (MAG), Instituto de Investigaciones Agropecuarias (INIAP), and Charles Darwin Foundation (CDF) for providing administrative, technical, and logistical support for this project. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Floreana**} \\ \cline{2-7}
**Land Cover** & \multicolumn{2}{c}{**Agricultural Area**} & \multicolumn{2}{c}{**Buffer Zone**} & \multicolumn{2}{c}{**Total**} \\ \cline{2-7}  & **Ha** & \%** & **Ha** & \%** & **Ha** & \%** \\ \hline Bare Ground & 1.96 & 0.72 & 5.25 & 0.36 & 7.20 & 0.42 \\ Built Environment & 5.63 & 2.08 & 3.93 & 0.27 & 9.56 & 0.55 \\ _Cedrela_—Cedar & 20.96 & 7.73 & 13.73 & 0.94 & 34.70 & 2.00 \\ _Cinchona_—Quinine & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ _Coffea_—Coffee & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ Cultivated Grass & 6.14 & 2.27 & 0.10 & 0.01 & 6.24 & 0.36 \\ Deciduous Forest & 2.30 & 0.85 & 95.84 & 6.55 & 98.14 & 5.66 \\ Evergreen Forest and Shrubland & 0.74 & 0.27 & 278.18 & 19.02 & 278.92 & 16.09 \\ Evergreen Seasonal Forest & 12.77 & 4.71 & 670.68 & 45.85 & 683.45 & 39.42 \\ Freshwater & 1.10 & 0.40 & 0.17 & 0.01 & 1.26 & 0.07 \\ Humid Tallgrass & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ Mixed Forest & 36.70 & 13.54 & 174.97 & 11.96 & 211.67 & 12.21 \\ _Pennisetum_—Elephant Grass & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ Permanent Crops & 23.62 & 8.72 & 4.30 & 0.29 & 27.92 & 1.61 \\ Pioneer & 2.48 & 0.91 & 6.46 & 0.44 & 8.94 & 0.52 \\ _Psidium_—Guava & 55.67 & 20.54 & 190.28 & 13.01 & 245.95 & 14.19 \\ _Rubus_—BlackBerry & 0.00 & 0.00 & 0.67 & 0.05 & 0.67 & 0.04 \\ Silvopasture & 50.34 & 18.58 & 12.84 & 0.88 & 63.18 & 3.64 \\ _Syzygium_—Ponnarosa & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ Transitory Crops & 50.60 & 18.67 & 5.22 & 0.36 & 55.82 & 3.22 \\ \hline Total & 271.01 & 100.00 & 1462.62 & 100.00 & 1733.63 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Land cover surface area and percentages of agricultural area and buffer zone occupied per category for Floreana island. *
## 3 Shading Dependence of Output Characteristics of a PV Array  
A single-peaked P-V characteristic curve of a PV array happens to contain multiple peaks if the array is partially shaded in different series and parallel combinations. In this work, SW20W PV modules, manufactured by MPPTSUN Co. Ltd., China, were used to build a PV array under test. Specifications of an SWM20W PV module are listed in Table 2. Using the Matlab software, a family of I-V and P-V characteristic curves of a 4 series-3 parallel (4S-3P) PV array, as shown in Figure 6, were simulated under Standard Test Conditions (STCs), that is, with the solar irradiance = 1 kW/m\({}^{2}\), T = 25 \({}^{\circ}\)C and air mass (AM) = 1.5. As illustrated in Figure 7, the improved version of the firefly algorithm was applied to track down the GMPP of a P-V array by controlling the gate of the main switch in the converter.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Specification** \\ \hline Maximum output power (\(P_{\text{max}}\)) & 20 W \\ Current of MPP (\(I_{mpp}\)) & 1.10 A \\ Voltage of MPP (\(V_{mpp}\)) & 18.18 V \\ Short current (\(I_{sc}\)) & 1.15 A \\ Open voltage (\(V_{oc}\)) & 22.32 V \\ Size of module & \(395\times 345\times 17\) mm \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of an SWM20W PV module.  
Figure 6: A family of (**a**) I-V and (**b**) P-V curves for a 4S-3P configuration with shading as a parameter.
## 1 Introduction
MCDCNet fully combines and utilizes the advantages of CNNs and Transformer [12]. It first employs a Transformer-based backbone to extract semantic information. Then, combined with the Spatial Extraction Module (SEM) proposed in this article, a novel dilated convolution-based encoder is introduced to capture and preserve spatial context at multiple scales. Subsequently, the traditional semantic segmentation decoder is replaced with a customized mask classification decoder to more effectively utilize the extracted semantic features for precise pixel-level segmentation. We conducted experiments on several challenging datasets to validate the effectiveness of our method. Figure 1 shows that our approach achieves a good trade-off between the Params and mIoU. The contributions of this paper are summarized as follows:  
* We construct the Spatial Extraction Module (SEM) focusing on the pixel-level feature correlation in the spatial dimension. It addresses the issue of insufficient spatial feature extraction in the network. * We design an efficient inference strategy to ensure that the IAM-based decoder's outputs meet the requirements of the semantic segmentation task. * The experimental results from the Cityscapes, ADE20K, and COCO-Stuff datasets demonstrate the effectiveness of our network. MCDCNet provides a new perspective for semantic segmentation.
# Collaborative Beamforming with DQN for Interference Mitigation in 5G and Beyond Networks
###### Abstract
The proposed DQN agent can decide a specific shift angle (i.e., right or left shift angle) depending on the time delay in beam-steering to reduce the interference's power and increase the serving power and SINR. The remainder of this paper is organized as follows. The details of the related work are provided in Section 2. Section 3 presents our proposed DQN-based 5G beamforming architecture. We show the performance evaluation and comparison in Section 4 and draw the conclusion in Section 5. Figure 1: (a) Radiation pattern with antenna spacing \(d=43\) mm. (b) Radiation pattern with antenna spacing \(d=150\) mm. (c) Radiation pattern with antenna spacing \(d=300\) mm.
# Pyrometallurgical Technology for Extracting Iron and Zinc from Electric Arc Furnace Dust
## 1 Introduction  
Steel production occupies a leading position in the metallurgical sector worldwide [1, 2, 3, 4, 5]. The rapid growth of steel production on a global scale over the past 20-30 years has attracted the attention of experts to the increasingly harmful environmental impact of ferrous metallurgy enterprises [6, 7, 8], including the generation of hard-to-recycle human-made waste [9, 10, 11, 12].  
Researchers and manufacturers are engaged in the recycling of metallurgical waste in accordance with waste management laws adopted in their countries [5, 6, 13, 14]. In Russia, the development of domestic technology and organization of iron-containing waste recycling into a product with 100% metallization and Fe content > 96% comprises an important R&D direction in the field of ferrous metallurgy, recommended for implementation in the period up to 2030 [15].  
Despite the growing cost of the storage of this raw material at landfills and slag fields, the multi-million-ton dumps from the ferrous metallurgy production activity (oily scale, gas-cleaning dust, sludge, slag) are continuously increasing [16, 17, 18]. Such hard-to-enrich and low-grade materials include electric arc furnace dust, which can be not only an iron-containing raw material for iron and steel production but allows for extracting zinc into a commercial product since the dust contains zinc compounds in sufficient quantities [19, 20, 21]. The growing global demand for steel and zinc makes scale processing and EAFD a very urgent problem. In this regard, developing technologies for processing EAFD to extract valuable components (in particular, Fe and Zn) while obtaining commercial products is relevant [12, 18, 22, 23, 24, 25].  
This study is aimed at developing a technology for processing electric arc furnace dust into granulated cast iron and a zinc-containing product.
## 2 Fundamental Models and Literature Search
### Planning Model
#### 2.2.2 Constraints  
The constraints in the marine data collection path-planning problem arise from a combination of factors, with the task model's requirements playing a significant role. To begin with, the problem must adhere to constraints related to the starting and ending locations of the data collection mission [32; 42]. Moreover, there are constraints on how frequently each node can be visited during the path-planning process [35; 26]. Additionally, a constraint governs the total number of nodes that can be visited along the path, with certain literature suggesting that nodes with lower data value might be excluded from the visitation plan, further optimizing the data collection process [43].  
The physical characteristics of sensors include constraints that must be accounted for in the path-planning problem. Sensor communication range determines the distance over which sensors can effectively transmit and receive data [32]. This range influences the layout of the path and the arrangement of nodes to ensure adequate coverage. Also, there are constraints on both the maximum energy that can be stored in each sensor node and the energy required to transmit or receive a data packet [41]. These energy-related limitations impact the lifetime and operational capabilities of the sensors. Moreover, signal-to-interference-plus-noise ratios (SINRs) and outage probabilities, as highlighted in the work [34], play a critical role in maintaining reliable communication within the sensor network.  
AMVs' physical system poses distinct constraints to the path-planning problem. AMVs are subject to constraints in energy supply [44], which directly affects their operational duration and the distance they can cover during the data collection mission. Communication distance is a vital constraint that dictates the range over which AMVs can effectively communicate with other nodes or the central control station [45]. Also, the constraint on maximum diving depth and the number of permissible turning points was introduced in [34; 35], respectively. The constraint of energy consumption during data packet transmission or reception is also considered [41]. Finally, behavioral constraints specific to AMVs [39] add complexity, requiring careful consideration of how AUVs interact with and approach the nodes during the data collection process.
# Cellular and Virtualization Technologies for UAVs:
## 2 Related Work and Background
### Communications
Another of their benefits is providing feasible communications between UAVs without any additional infrastructure, similarly to the well-known Mobile Ad hoc Networks (MANETs) or Vehicular Ad hoc Networks (VANETs). For this reason, FANETs have been the most considered solution by the research community to enable UAV communications during the last years. For example, Ref. [21] presents a FANET scenario where we evaluate a routing scheme using an emulation platform to characterize the communication channel (WiFi) and the network mobility models. Authors in [22] describe an innovative routing process based on ad hoc networks with better efficiency than traditional FANET algorithms. Another example is [23], where position-based routing algorithms are compared over a common scenario through a comprehensive comparative analysis.
## 3 Materials and Methods
### The EduDCM Framework
#### 3.1.2 LLM-Driven Context-Aware Label Generation  
EduDCM employs advanced LLMs, such as GPT-3.5-turbo [22], to generate preliminary labels for unannotated dialogues based on their conversational context. The dialogue is segmented into context windows, each containing the target utterance and its surrounding utterances, ensuring that the labels generated reflect the utterance's role within the broader conversational flow. Tailored prompts are dynamically crafted for the educational domain, instructing the LLM to classify utterances into predefined pedagogical categories while providing a rationale for each choice. For ambiguous utterances, the LLM generates multiple candidate labels, each with an associated confidence score, which are subsequently refined through alignment and validation stages. The preliminary labels generated form the input for alignment processes to ensure consistency and precision.
## 2 Theory
### Scattering Model for Microrough Surfaces  
The microroughness of the surface of a precision optical element causes the surface profile to be smaller than the visible wavelength by an order of magnitude. The scattering properties of this surface topography can be described using the Rayleigh-Rice theory. In this theory, the calculation of the pBRDF depends on the power spectral density (PSD) of the surface. As shown in Equation (2), the PSD is a function of the height profile \(z(x,y)\) of the scatterer surface, and since the microscopic morphology structure of the microrough surface is determined through stochastic processes with a certain average nature, the height profiles of the different regions of the surface are not the same, and therefore the scattering characteristics will fluctuate with a certain statistical law. Therefore, in this study,  
Figure 2: Schematic diagram of dig scattering.  
the K-correlation model function [23] is considered to fit the overall average PSD of the microrough surface as follows:  
\[\left\{\begin{array}{l}S_{PSD}(f)=\frac{A}{\left[1+\left(B\cdot f\right)^{2} \right]^{C/2}},\\ f=\sqrt{f_{x}^{2}+f_{y}^{2}}.\end{array}\right. \tag{6}\]  
This equation contains three adjustable parameters \(A\), \(B\), and \(C\), which are related to the statistical properties of the surface roughness \(\sigma\), as shown in the following equation:  
\[\left\{\begin{array}{l}A=\frac{B^{2}(C-2)\times\sigma^{2}}{2\pi},\\ B=2\pi l.\end{array}\right. \tag{7}\]  
where \(l\) denotes the correlation length of the microrough surface and the value of the parameter \(C\) is 3. After determining the PSD of the surface roughness from the K-correlation model function, the pBRDF of the surface microroughness of the optical element can be calculated.
## 4 Experiment and Discussion
### Test  
Five-fold cross-validation was used to test the proposed method. Since our dataset included a total of 645 tongue images obtained at three different times, the tongue images were randomly divided into five groups, each group containing 129 tongue images. Each time, four groups were used for training and the other one was used for testing. Table 1 shows the results of each cross-validation experiment. The proposed method was relatively stable and the average classification accuracy reached 78.6%.