esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference
## 3 Methodology
### Step1: The Framework of CCL: C-YOLOv4
#### 3.2.4 Improvement of Loss Function  
The loss function of C-YOLO v4 is composed of confidence loss, prediction container regression loss, and classification loss, where the prediction container regression loss function is Complete Intersection-Over-Union (_CIOU_), and its specific formula is as follows:  
\[L_{CIOU}=1-IOU+\frac{\rho^{2}\left(b,b^{gt}\right)}{c^{2}}+\alpha\upsilon \tag{1}\]  
\[\alpha=\frac{\upsilon}{(1-IOU)+\upsilon} \tag{2}\]  
\[\upsilon=\frac{4}{n^{2}}\left(arctan\frac{\upsilon^{gt}}{\hbar\upsilon^{gt}}- arctan\frac{\upsilon}{\hbar}\right)^{2} \tag{3}\]  
Intersection-Over-Union (_IOU_) is the intersection ratio between the prediction bounding box and the ground truth. \(\rho^{2}\left(b,b^{gt}\right)\) is the center of the Euclidean distance. \(c\) is a true container that predicts the container and the diagonal distance of minimum closure area. \(\alpha\) is weight function; \(\upsilon\), width to height ratio, is used to measure the weight function; \(b\) is said to predict bounding box center coordinates; and \(w,h\) are the width and height of the prediction bounding box. \(b^{gt}\) is the center point coordinate of the ground truth, and \(w^{gt}\), \(h^{gt}\) are the width and height of the ground truth.  
Although _CIOU_ considers the overlapping area, aspect ratio, and center point distance of bounding box regression, its weight function only reflects the weight of aspect ratio and does not consider the difference between width and height, respectively, and its own confidence, which will hinder further optimization of the model. Therefore, we introduce a new loss function Efficient Intersection-Over-Union _EIOU_ as the prediction container regression loss function of C-YOLOv4, as shown in Equation (4):  
\[L_{EIOU}=1-IOU+\frac{\rho^{2}\left(b,b^{gt}\right)}{c^{2}}+\frac{\rho^{2} \left(w,b^{gt}\right)}{c_{w}^{2}}+\frac{\rho^{2}\left(h,b^{gt}\right)}{c_{h} ^{2}} \tag{4}\]  
where \(c_{w}\), \(c_{h}\) are the width and height of the minimum outer bounding boxes covering the two rectangular closures. _EIOU_ takes side length as the penalty term, which alleviates the problem that width and height cannot increase or decrease at the same time in _CIOU_ to some extent, and this improves the prediction accuracy of the model.
## 4 Discussion  
### Comparison with Other Regions  
The degree of SIUS can reflect the technological and economic development level of a region. Table 2 presents the results of SIUS studies in other areas, which can serve as benchmarks for comparative analysis. In 2016, Henan Province had a per capita SIUS of 3.31 t/cap, which is lower than that of Zhejiang and Fujian Provinces [10]. Based on previous analyses, the per capita stock is positively correlated with per capita GDP. This discrepancy may stem from differences in the economic development levels between the regions, as well as their varying population sizes. In 2016, Henan Province's GDP was  
Figure 10: Total available scrap steel resources from 2023 to 2060: (**a**) Total available scrap steel, (**b**) Depreciated scrap steel.  
40,160.01 billion yuan, ranking 4 th in China; however, its per capita GDP ranked only 20 th, well below that of Zhejiang and Fujian.  
This disparity in per capita SIUS reflects common features in the industrialization processes across different countries or regions. In developed countries, the extensive use of steel is typically driven by advanced ironmaking technologies and strong economic support. As such, the quantity of SIUS becomes an important indicator of a nation's industrialization and development level. Once the per capita SIUS reaches the 2 t threshold, a region enters the core stage of industrialization [3]. Henan Province officially reached this threshold in 2014, marking its transition into a phase of rapid industrial development.  
Furthermore, [PERSON] et al. (2013) calculated the per capita SIUS for 200 countries worldwide and found that the saturation point for all countries ranges from 10-16 t/ cap [38]. This saturation point is approximately 2-3 times higher than of Henan Province. Based on the saturation patterns observed in industrialized countries, it is anticipated that SIUS in Henan Province will continue to increase, particularly in end-use sectors with a large proportion of SIUS.
## 3 Proposed Methods
### Taillight Detection  
In the V2V positioning context, the taillight detection stage aims to obtain the pixel coordinates of each taillight in the image. Moreover, the regions of these taillights need to be preserved because they will be further used in stereo taillight matching and taillight pairing stages. To accomplish these requirements, we propose a taillight detection method that includes two phases: generation and verification. The generation phase is to generate the candidate taillight region from the image. This phase comprises of three steps: HSV color thresholding, interesting point enhancement, and candidate region clustering. The verification phase, which consists of one step that is taillight verification, is to verify whether each candidate region is a taillight or not. The verification phase is an additional phase when compared with many existing detection methods [12; 13; 15; 16; 17]. It should be noted that sometimes when taillights from different vehicles approach each other, taillight occlusion occasionally occur under the camera's viewpoint. To resolve this phenomenon completely, complicated research is required, which is beyond the scope of our study. Therefore, in this paper, we only focus on taillights that are already separated from each other in the image. Accordingly, we assume that each interesting point that is adjacent to another belongs to the same taillight. Figure 4 below shows the entire flow of the proposed taillight detection method. The details of each step are provided in the remaining part of this subsection.
# Non-Axiomatic Logic Modeling of English Texts for Knowledge Discovery and Commonsense Reasoning
## 3 Theoretical Foundations and Required Background Knowledge
### Linguistic Tools  
In order to properly define rules for NL-to-NAL translation, it is first necessary to obtain the linguistic structure of the NL text. Particularly, _dependency parsing, part of speech tagging, named entity recognition_, and _search for hypernyms_ are the more relevant linguistic analyses needed.  
Dependency parsing [36] is a linguistic analysis that identifies the grammatical structure of sentences and constructs a dependency tree that represents such a structure. This process finds sets of related words within a sentence, as well as the specific type of each one of those relations. Found relationships are called _dependencies_, and _universal dependencies_[37] is a representative set of dependencies designed to cover a great majority of use cases in all languages (see Tables 3 and 4).  
Part of Speech Tagging (PoS tagging) [38] is a process that marks each word in a sentence with a tag that indicates its grammatical role in that sentence. PoS tags include the eight classic categories (noun, verb, participle, article, pronoun, preposition, adverb, conjunction) as well as other related subcategories. Both dependency parsing and part of speech analysis are used to define the type of terms and the hierarchical structure of the NAL expressions defined, and the Stanford typed dependencies module of the Stanford Parser is used for this process [39].  
Named entity recognition [40] involves the identification and categorization of certain words or parts of sentences considered as key information or entities. An entity is basically anything that is consistently talked about or referred to in the text. Named entities include persons, geographic locations, dates, ages, addresses, phone numbers, organizations, companies, etc. The Stanford Named Entity Recognizer [41] is used for this process with only Person, Organization, and Location labels.  
Lastly, _hypernyms_ are words with a more general meaning than another word with a related but more specific meaning. We _search for hypernyms_ to establish and enrich the context of each concept used in the NL text being translated (i.e., each _term_ modeled in an NAL expression). Hypernyms and their opposite hyponyms define a hierarchy of concepts extremely similar to that defined by the _intension and extension calculus_ definitions, which are the basic ideas that conform the formal semantics in NAL expressions. WordNet [42] is used for finding chains of related hypernyms (i.e., the _intension_ of a term).  
A summary of the linguistic analysis performed with the software tools used is included in Table 5.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### DMR & CoDMR
However, this reliability comes at the cost of extra transmission, as there is one extra transmission at every hop by the cooperative relay nodes.
## 4 Feasibility of Agro-Waste Utilization in Reinforced Concrete (RC) Elements
### Limitations of Using Agro-Wastes in RC Elements  
Studies indicate that AWs, such as PO residues, used as aggregate supplements in concrete production, demonstrate structural behaviors comparable to or even superior to conventional concrete. The primary aim of incorporating AWs into RC structures is to reduce the consumption of natural resources and contribute to effective waste management processes. However, these materials are often combined with steel or adhesive reinforcements to enhance their performance. Further research is needed to evaluate the standalone efficacy of AWs as structural materials without supplementary reinforcements. Additionally, more comprehensive studies are required to assess their practical applicability on a larger scale.  
Materials like CSs have shown promising results as effective alternatives in RC elements. Specifically, they have exhibited typical structural behavior in flexural tests, with acceptable crack widths and good ductility. Despite these positive findings, the large-scale application of CSs as a replacement for conventional coarse aggregates necessitates more experimental data and validation [73].  
Several factors limiting the application of AWs in RC elements have been identified in the literature:  
* Material Homogeneity: Variations in the physical and chemical properties of AWs pose challenges in mix design, potentially affecting material homogeneity and performance consistency [6; 7].
* Long-Term Durability: The inclusion of AWs in concrete may influence long-term mechanical strength and environmental resistance. This highlights the need for considering unaddressed parameters in existing standards and conducting durability tests to evaluate their long-term effects [34].
* Lack of Standards: The absence of comprehensive international standards regulating the use of AWs in RC elements restricts their widespread adoption. Existing standards such as BS8110, ASTM C330, and ACI-318 need to be expanded to address the potential applications and performance of AWs [25; 37; 72].  
These limitations represent the primary challenges hindering the widespread use of AWs in RC elements. Addressing these issues will be a crucial step toward developing more sustainable construction materials.
# An FPGA Implementation of a Convolutional Auto-Encoder
## 2 FPGA Implementation of the CAE
### Design of PE  
The PEs employed by the periodic layer-multiplexing framework are used to implement the operation of multiplication and addition required by convolution calculation. To reduce the time consumption of convolution calculation, a parallel convolution method based on shift registers is presented for the design of PE.  
Figure 3: Parallel channels method by caching input data.  
There are four input channels in one PE, and the 16-bit unsigned numbers in the four channels can be inputted to the corresponding 32-bit shift registers per clock cycle. These 32-bit shift registers act as the convolution window, which can slide arbitrarily. We first assume that the data are inputted to these shift registers immediately after reset. Then, the convolution calculations begin in the second clock cycle. A \(4\times 4\) convolution window of the PE is shown as the colorful square in Figure 4.  
For storage convenience, a total of nine 8-bit signed numbers of each convolution kernel are combined in a row to form an integrated data with a width of 72 bits. When the convolution calculation starts, the convolution window begin to slide so that convolution operations can be performed in the four sub-windows simultaneously under one clock cycle. Then every 8-bit convolution kernel is multiplied by the data in the shift register, as shown in Figure 5. After that, the outputs of multipliers are obtained under the third clock cycle. All these outputs of multipliers are summed and exported under the fourth clock cycle. Some nonlinear factors are introduced into these results by operating the ReLU activation function. The outputs of all the ReLU functions are transmitted to the pooling module, and then three comparators in this module start to perform comparison operation among the four values and output the maximum, which consumes two clock cycles.  
Figure 4: The \(4\times 4\) convolution window (the red dotted box) contains four \(3\times 3\) sub-windows: Slide_1, Slide_2, Slide_3, and Slide_4, which are marked with different colors or streaks, respectively.  
Figure 5: Convolution operation process in PE.
## 4 Results and Discussion  
In this section, the effects of different vessel curvatures, burial depths, and epidermal melanin contents (i.e., human skin color) on the parameters of the PDL treatment of PWSs were systematically studied to determine the reasonable area ratio of the vascular purpura obtained from clinical observations. The depths of blood vessels in our simulation were set as 0.25, 0.375, and 0.5 mm corresponding to the superficially, mid-, and deeply buried blood vessels, respectively. Vessel models were selected as those with small, medium, and large StdGC, as shown in Figure 8. Laser pulse duration and laser spot diameter were fixed at \(t_{p}=6\) ms and \(D=1\) mm, respectively.  
The melanin volume fractions (\(f_{m}\)) in the epidermis were set as 1%, 3%, 5%, 7%, and 9% corresponding to people with white, light yellow, dark yellow, brown, and black skin, respectively [38]. Assuming that melanin is evenly distributed in the epidermis, we present the optical and thermal parameters of the epidermis containing melanin in Table 2. The effect of melanin content on the absorption coefficient of the epidermis was introduced in the form of volume fraction. The same optical and thermal properties of the vessel and the dermis in Table 1 were adopted.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Property** & **Epidermis (No Melanin)** & **Melanin** & **Epidermis with \(f_{m}\) Melanin** \\ \hline Absorption coefficient, \(\mu_{\text{a}}\)/cm\({}^{-1}\) & 0.371 & 402.63 & \(\mu_{\text{a,m}}\times f_{m}+\mu_{\text{a,e}}\times(1-f_{m})\) \\ Scattering coefficient, \(\mu_{\text{s}}\)/cm\({}^{-1}\) & 470 & — & 470 \\ Anisotropy index, \(g\) & 0.79 & — & 0.79 \\ Refractive index, \(n\) & 1.37 & — & 1.37 \\ Density, \(\rho\)/kg\(\cdot\)m\({}^{-3}\) & 1120 & — & 1120 \\ Thermal conductivity, \(k\)/kW\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) & 0.34 & — & 0.34 \\ Specific heat, \(c\)/J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\) & 3200 & — & 3200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optical and thermal properties of the epidermis irradiated by a 595 nm pulsed dye laser [39].  
Figure 13: Relationship between \(S_{br}\) predicted by three damage models and \(S_{pu}\) measured by the experiments under different incident energy densities.
## 3 Results and Discussion
### Electrochemical Study
The comparison of previously reported carbon materials for supercapacitor electrodes in terms of electrochemical capacitive performance is also described in Table 1. Obviously, the specific capacitance of NCFs exhibits a comparable capacitance value with the reported results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Precursor** & \(\mathbf{S_{BET}}\) (m\({}^{2}\) g\({}^{-1}\)) & **Nitrogen Content** & **Capacitance (F g\({}^{-1}\))** & **Ref.** \\ \hline PANI naboitabe & 46.4 & 7.4 wt \% & 133, 1 A g\({}^{-1}\) & [29] \\ PANI nanowires & 666 & 8.1 wt \% & 122, 1 A g\({}^{-1}\) & [31] \\ Hollow PANI nanowires & 213 & 6.7 wt \% & 176, 1 A g\({}^{-1}\) & [32] \\ Polyaniline with an F127 template & 721 & 5.9 at \% & 210, 1 A g\({}^{-1}\) & [59] \\ Lignocellulose & 180 & 0 & 45, 1 A g\({}^{-1}\) & [60] \\ MOF & 837.6 & 6.8 at \% & 325.3, 1 A g\({}^{-1}\) & [61] \\ Wood & 26.4 & 0 & 80, 1 A g\({}^{-1}\) & [62] \\ PANI nanofibers & 915 & 7.59 at \% & 139, 1 A g\({}^{-1}\) & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of specific capacitance values with recent reported literature in a three-electrode cell system. Figure 8: (**a**) CV curves at different scan rates; (**b**) a plot of specific capacitance vs scan rate\({}^{-1/2}\); (**c**) GCD curves at different current densities; (**d**) Nyquist plots of NCFs’ electrodes in the frequency range of 0.01 Hz–100 kHz; and (**e**) cycling performance of NCFs electrode.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 3 Problem Description and System Model  
In this section, we first describe the 5G core slice provisioning problem, and then present the system model. A summary of used notations is presented in Table 1.
# Self-Organized Conductive Gratings of Au Nanostripe Dimers Enable Tunable Plasmonic Activity
###### Abstract  
Plasmonic metasurfaces based on quasi-one-dimensional (1D) nanostripe arrays are homogeneously prepared over large-area substrates (cm\({}^{2}\)), exploiting a novel self-organized nanofabrication method. Glass templates are nanopatterned by ion beam-induced anisotropic nanoscale wrinkling, enabling the maskless confinement of quasi-1D arrays of out-of-plane tilted gold nanostripes, behaving as transparent wire-grid polarizer nanoelectrodes. These templates enable the dichroic excitation of localized surface plasmon resonances, easily tunable over a broadband spectrum from the visible to the near- and mid-infrared, by tailoring the nanostripes' shape and/or changing the illumination conditions. The controlled self-organized method allows the engineering of the nanoantennas' morphology in the form of Au-SiO\({}_{2}\)-Au nanostripe dimers, which show hybridized plasmonic resonances with enhanced tunability. Under this condition, superior near-field amplification is achievable for the excitation of the hybridized magnetic dipole mode, as pointed out by numerical simulations. The high efficiency of these plasmonic nanoantennas, combined with the controlled tuning of the resonant response, opens a variety of applications for these cost-effective templates, ranging from biosensing and optical spectroscopies to high-resolution molecular imaging and nonlinear optics.  
self-organized nanoantennas; large-area metasurfaces; transparent nanoelectrodes; plasmon hybridization; nanostripe dimers; wire-grid polarizers; biosensing +
Footnote †: journal: Applied Physics Letters
# Multimodal Diarization Systems by Training Enrollment Models as Identity Representations +
## 3 Face Enrollment Models
### Approximated Detection Cost Function (aDCF) Loss  
Most DNN systems are trained to generate representations using traditional loss functions as the objective loss for training. However, this strategy has a main drawback, as traditional loss functions are not oriented to the goal task. For this reason, different alternatives have been presented in the literature to design loss functions focused on the final evaluation metrics to train the DNN systems such as the approximated area under the ROC curve (aAUC) [16; 17], the partial and multiclass AUC loss (pAUC) [18; 19; 20], and the approximated detection cost function (aDCF) [14] which was used for this work. This aDCF is inspired by the DCF verification metric [21]. The use of this differentiable version of the DCF metric as objective loss function allows training DNN systems aimed at minimizing one of the main metrics employed in verification tasks. In addition, this function is based on measuring the two types of decision errors produced by verification systems using a threshold. These two errors are known as false alarms and misses which are also part of the diarization error rate (DER) used to evaluate diarization systems. The former errors occur when an identity is incorrectly assigned, while the latter refer to a correct identity not being detected by the system. Therefore, we seek to minimize the average number of times false alarms (\(P_{fa}\)) and misses (\(P_{miss}\)) occur, which can be approximated as  
\[\hat{P}_{fa}(\theta,\Omega)=\frac{\sum_{y_{i}\in y_{non}}\sigma_{a}(s_{\theta} (x_{i},y_{i})-\Omega)}{N_{non}}, \tag{1}\]  
\[\hat{P}_{miss}(\theta,\Omega)=\frac{\sum_{y_{i}\in y_{tar}}\sigma_{a}(\Omega-s _{\theta}(x_{i},y_{i}))}{N_{tar}}, \tag{2}\]  
where \(x_{i}\) is the input sample, \(y_{i}\) is the class label, \(s_{\theta}(x_{i},y_{i})\) is the score obtained from the last layer which is defined as a cosine distance layer, and \(\sigma_{a}(\cdot)\) is the sigmoid function, expressed as follows:  
\[\sigma_{a}(s)=\frac{1}{1+exp(-\alpha\cdot s)}, \tag{3}\]where \(\alpha\) is an adjustable parameter. The use of this sigmoid function allows making the original expressions of \(P_{fa}\) and \(P_{miss}\) differentiable and enables the backpropagation of gradients. Hence, the aDCF loss function to minimize is composed of a weighted sum of these approximated expressions, defined by  
\[aDCF(\theta,\Omega)=\gamma\cdot\hat{P}_{fa}(\theta,\Omega)+\beta\cdot\hat{P}_{ miss}(\theta,\Omega), \tag{4}\]  
where \(\gamma\) and \(\beta\) are configurable parameters to provide more cost relevance to one of the terms over the other.
## 1 Introduction
The system uses a reference material, which limits the accuracy of the method. An analysis of the system is carried out considering that there is axial and radial heat flow. In addition, the solid bar of material that can be evaluated, it has a hollow bar of insulating material. The problem to be solved is a bar composed of a reference conductor material at the longitudinal ends, and a test material depicted in the centre, the entire bar an element by an insulating material, it is considered that it is axial and radial flow and the physical dimensions of the problem are shown in Figures 1a, 2 and 3. Using the apparatus developed in the CENAM, two concentric cylinders are used, housing the material to be tested between them. Inside the smaller diameter cylinder is placed the heating resistance, which is covered with another cylinder to standardise the surface temperature. The temperature measurement is carried out on the outer and inner cylinders, using thermocouples for this. The method is used to measure thermal conductivity in materials solid conductors. To meet this need for measurement, the CENAM developed a system for measuring thermal conductivity in thermally conductive solid materials employing a secondary method. This work presents a comparison of certain experimental results using the cut bar and the finite element method (FEM), to obtain information that serves in the development of the new cut bar system, to extend its operating range up to 600 \({}^{\circ}\)C, under optimal operating conditions [2]. Figure 1: (**a**) Schematic of a Comparative-Guarded-Longitudinal Heat Flow System, indicating possible locations of temperature sensors (**b**) methodology for the experiment used in this work [1]. Figure 3: Diagram of the experimental model used. Figure 2: Cut bar method system [2].