esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The ANN models employed for this purpose have been denoted by various terms such as connectionist models, parallel distributed processing models and neuromorphic systems [3]. These models function by means of the dense interconnection of simple computational units and are specified by the network topology, computational unit characteristics and training or learning algorithms employed [1]. The advantages of ANNs have facilitated numerous successful applications [22; 23]. In addition to our previously proposed regression models [5], we have also applied ANNs [24; 25; 26] to explore the impact of machining parameters on USM process responses. Employing ANNs for predicting the MMR in USM offers a significant advantage over regression techniques because ANNs do not require explicit mathematical functions. However, we did not consider whether the prediction precision provided by ANN modelling approaches is greater than that of our regression models. Therefore, the primary objective of this study is to propose a back propagation ANN (BPANN) for dealing with the complex input-output relationships in USM in the absence of an explicit mathematical model. We then compare the prediction precision of the BPANN proposed in this study with the corresponding precisions obtained using the previously proposed regression models [5] with equivalent experimental parameters on an experimental apparatus. As such, the neurons of the input layer were selected as the abrasive granularity (G), feed pressure (P) and feed speed (FS) and included a single neuron in the output layer representative of the predicted MRR, which is consistent with past studies [5]. According to Table 1, we note that the establishment of a suitable nonlinear regression equation is crucial to use in the regression technique for predicting the _MRR_ in the USM technique. However, establishing a suitable nonlinear regression equation is difficult for most complex input-output problems of general machining. In contrast, the input values of ANNs can theoretically be arbitrary because the output values of ANNs are obtained by simulating the human learning process. Therefore, we consider an ANN as a more powerful modelling method for mapping complex nonlinear input-output problems.
## 2 Methods
### Feature Extraction Network Construction
#### 2.1.3 Feature Extraction Enhanced Network with Improved DRE-Net  
DRE-Net (Density Reception Expanded) is a deep learning network whose core idea is to use a recursive structure to capture complex patterns and hierarchies in images. In the recursive structure, the network processes data through multiple recursive layers, each of which can be considered as a deep learning model, and these layers are repeatedly applied to the input data, each time based on the previous output, and the network extracts and refines the features during the recursive process. The DRE-Net network extracts the semantic information within different receptive fields through convolution at different expansion scales, while spaced convolution operations can increase the receptive fields without affecting the resolution.  
In this paper, three main improvements are made to the DRE-Net network:  
1. The semantic features with expansion convolutional layers 2 and 3 are enhanced by the self-attention mechanism;
2. The convolutional layer with expansion scale four is deleted;
3. A symmetric coding and decoding network layer are added. The improved DRE-Net network structure is shown in Figure 4.  
The process of the improved DRE-Net algorithm is as follows: firstly, select an uncomputed view in the dataset and downsample it twice with a step size of 2; secondly, convolve the downsampled results with an expansion scale of 2 and an expansion scale of 3, respectively; then, enhance the features by applying the self-attention mechanism and integrate the features by connecting them across the layers; then, upsample it twice with a step size of 2; and finally, store the computed feature maps.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Gelatin Films
#### 3.5.4 Composite Films  
Composite films are complex systems that consist of multiple components and combine several hydrophobic and hydrophilic compounds to improve functional qualities [20]. In these kinds of edible packaging, the approach is to use a combination of at least two constituents, wherein the weakness of an individual substance is compensated for by the presence of the other component. For example, the water vapor permeability of polysaccharides and proteins can be enhanced by incorporating lipids, resulting in an edible composite that possesses both hydrophilic and hydrophobic properties [6]. A study by [PERSON] et al. [90] presented an edible coating with hydroxypropylmethylcellulose and chitosan with and without bergamot essential oil (EO) for the cold storage of grapes. They suggested that HPMC coating with bergamot EO was the most effective formulation, in comparison to chitosan alone with bergamot EO, in displaying effective an timicrobial action and great control over respiration rates, with reduced weight loss during postharvest cold storage. The extension of ivy gourd (_Cocinia India_) shelf life was studied with composite films with a blend of gum acacia pectin and pullulan using plasticizers like sorbitol and glycerol. To evaluate the efficiency of the coating solution, titrable acidity (TA), antioxidant, weight loss, total soluble solids (TSS), total phenolic content (TPC), and storage time measurements were taken. According to the study's results, the ivy gourd's shelf life increased by 35% throughout the 23-day storage period. The anti-microbial test suggested that the prepared film/coating is more resistant to _Pseudomonas aeruginosa_ because of the presence of pullulan [91].  
Composite edible films can be classified as binary or ternary depending on the number of biopolymers. Combining pectin and zeolite Y results in a classic example of a binary edible film [91]. Such systems allow for a variety of protein-protein, carbohydrate-carbohydrate, and protein-carbohydrate combinations. There are a lot of studies on composite coatings and films created by combining two hydrocolloids; however, edible films or coatings created by combining three hydrocolloids are rare [92].  
The field of composites has seen significant advancements closely related to that of the progress in nanotechnology. Bio-nanocomposite materials are renowned for their promising features that help to enhance the mechanical and barrier properties of edible packaging materials [20].
## 2 Results and Discussion
### LiARG In Silico Binding Mode Analysis
Regarding the turmerone complexes **4-6**, all ligands assumed similar orientations among them and deeper positions in the _Li_ARG active site when compared to curcuminoids. Here, van der Waals interactions contributed to the lower estimated binding energy values (**4**, \(-\)5.90 kcal/mol, **5**, \(-\)6.20 kcal/mol, **6**, \(-\)6.22 kcal/mol), and no electrostatic interactions were observed. Despite the modification of the oxidative pattern of the ring moiety in their chemical structure, no significant changes in binding mode were observed. The \(\alpha,\beta\)-unsaturated carbonyl moiety was placed between His139 and His154 while the alkene extremity was placed among Asp137, Asp141, Asp243, Glu288, and Mn2+ ions (Figure 3a-c). These residues are conserved among all arginases and have been described as key residues for potential inhibitors binding with other _Leishmania_ species [15; 35; 36; 37; 38; 39]. His114, Ala140, Ile142, Asn143, Gly155, Ala192, Asp194, Glu197, and Thr257 residues also participate in complexes' formation, increasing the affinity of turmerones to the enzyme. These results indicate the potential of turmerones **4-6** to act as inhibitors against arginase of _L. infantum_. Figure 2: Binding mode of curcuminoids with _Li_ARG obtained by molecular docking. The superposition of the final docking pose of each inhibitor docked into the active site of _Li_ARG is shown in the center. The three-dimensional model of _Li_ARG is displayed in cartoon representation and colored green. The molecular structures of (**a**) curcumin (**1**) (pink), (**c**) bisdemethoxys Curcumin (**3**) (yellow), and (**e**) demethoxys Curcumin (**2**) (light pink) are shown in stick representation. Residues that participate in interactions are colored in light green. The 2D diagrams show the interactions between (**b**) 1, (**d**) 3, and (**f**) 2. Hydrogen bonds and ionic interactions are depicted as yellow and blue dashed lines, respectively.
# Microwave SINIS Detectors
## 6 Experimental Studies  
The fabricated samples were measured in two cryostats: the Heliox AC-V from Oxford Instruments (Abingdon, Oxfordshire, England) with an operation temperature as low as 273 mK, and a cryostat insert designed by [PERSON] at the Kapitza Institute for Physical Problems (Moscow, Russia) [42], with an operation temperature as low as 50 mK.  
Sensitive experiments were performed in the dilution cryostat, which was inserted into the transport He dewar [42]. The lowest achieved temperature was 50 mK. Such a cryostat does not require external pumps that significantly reduce vibrations and electric jamming. By using a cryostat equipped with three sample holders, in one cooling cycle, it is possible to measure and compare the characteristics of three samples. The cryostat was equipped with a blackbody radiation source, and we studied the responsivity and dynamic range of samples at different radiation temperatures (the schematic of this experiment is presented in Figure 12) [14].  
The optical response of the SINIS detectors with integrated twin-slot antennas, designed for the 350 GHz band, was measured at a phonon temperature around 100 mK using a blackbody radiation source [14]. Three blackbody sources were placed on the top covers of the sample chambers to illuminate each sample separately. A thin film of NiCr on a sapphire or silicon substrate, with contact pads for the heating current and a thermometer for temperature control, was used as such a blackbody radiation source. Mesh bandpass filters were used to determine the actual bandwidth of the incoming signal and suppress the background radiation. A diaphragm and bandpass filter between the BB source and the SINIS detector were used to suppress out-of-band background radiation.  
A current response of 0.7 nA in the SINIS with a suspended Cu absorber at a radiation power of 0.06 pW corresponds to a current responsivity of 1.1 \(\times\) 10\({}^{4}\) A/W. The detected current of 0.7 nA corresponds to 4.3 \(\times\) 10\({}^{9}\) electrons per second, and a power of 0.06 pW corresponds to 2.8 \(\times\) 10\({}^{8}\) photons at 350 GHz per second. The quantum efficiency in this case was over 15 electrons per absorbed radiation photon [14].
## 5 Discussion and Future Works
Compared to image-based approaches, if major advances are made in the computer vision field and more specifically for pose estimation, SPI-Net could still be relevant. * **Temporal tracking of pedestrians:** In the real world, there are usually more pedestrians on the streets passing and occluding each other, which requires sophisticated mechanisms not only for their detection but for their temporal tracking without mixing their identity over time. In order to compare SPI-Net to the literature on JAAD, our current approach completely omits such issue and relies on the ground truth spatial coordinates and individual IDs of each pedestrian provided by the data set. To address a better follow-up of the protagonists in the scene and to avoid mixing the dynamics of two protagonists due to a change of camera angle, future research will focus on building an end-to-end framework based on unlabeled coordinates of pedestrians, temporal tracking of pedestrians and SPI-Net for intention prediction. Current research direction tends to evaluate the benefits of using a pose estimation model sequentially based on pose matching for tracking [90; 91; 92; 93] compared to a frame by frame pose estimation model [94; 95; 14; 96] combined with more naive identifications approaches [97; 98] that are supposedly faster. * **Data set size:** A recurrent barrier to using deep-learning is small data sets. Even though JAAD is one of the most complete data sets for pedestrians intents, the number of instances present in the data set is still undersized to use the generalization ability of neural networks to its finest. In the present work, we had to focus a lot on regularization techniques present in the literature to avoid over-fitting. It is, therefore, necessary to extend the total number of instances for such task. Our model is directly extensible to other input formats with different 2D or 3D skeletal data structures: the proposed approach can therefore be applied to a broader family of applications that discover the intentions of moving subjects. However, to use the generalization ability of neural networks to its finest on such small data sets, future research will focus on proposing a tool to enrich the existing databases on human skeletal dynamics by combining both Geometric features and Cartesian features in order to generate skeleton dynamics that are coherent both spatially and sequentially. * **Continuous intention prediction of pedestrians:** SPI-Net showed that one could link the dynamics of a pedestrian to its discrete intention faster and better. Consequently, future research will focus on using SPI-Net to build a multi-modal architecture taking as input information such as skeleton, image semantic segmentation and qualitative information where discrete intention prediction could be used to infer the continuous trajectories describing the future movement of the pedestrian and therefore propose an intention prediction of pedestrians framework for both discrete and continuous intention prediction.
# Bidirectional Representations for Low-Resource Spoken Language Understanding
## 2 Methods
### Decoder  
Although an encoder equipped with CTC is perfectly able to transcribe speech, it does so by assigning a token to each position independently. [PERSON] et al. [12] reported that the addition of a decoder integrates language modeling in the learning process. Indeed, the decoder combines the acoustic cues of the encoder with the previous tokens to predict the next one. Here, we opt for a bidirectional transformer decoder instead of a left-to-right model. Left-to-right decoders predict one token at a time and stop after producing a special end-of-sequence token. This method is slower because a prediction depends on the previously predicted tokens. Additionally, the network only has access to previous tokens, thus limiting the amount of information available to make a prediction. In modern NLP, bidirectional architectures have become popular to learn powerful representations that make use of both left and right contexts [3; 17]. For instance, BERT [3] is optimized with a masked language modeling objective by randomly masking some tokens in the input sequence. In ASR, a similar approach was adopted by [PERSON] et al. [15], who obtained a rough text prediction with CTC, which was refined by masking low-probability tokens and letting the decoder predict the missing items in the sequence, similarly to [PERSON] et al. [18]. One issue with these models is the decoder's initial state definition. As the output sequence is unknown at the inference time, it is necessary to provide the decoder with a template consisting of a sequence of mask tokens of the same length as the target sequence. The decoder is not able to add or remove symbols to adjust the sequence length, and the correct length is unknown at the inference time. [PERSON] et al. [18] presented a solution that involves predicting the length as part of the prediction task of the encoder. Then, during inference, the decoder predicts multiple candidates of different lengths corresponding to the predicted lengths with the highest probability. [PERSON] et al. [15] assumed the greedy CTC prediction to be of correct length, although they showed that it leads to weaker transcriptions. In this work, we focus on learning rich representations for spoken language understanding. We thus work around this issue, as we assume that the length is not essential for our purpose. We follow [PERSON] et al. [15] for the architecture of the decoder. We use the output of the penultimate layer as the linguistic representations of speech.
# Computed Tomography-Driven Design and Fused Filament Fabrication of Graded Density Bone Scaffolds
## 1 Introduction
### Computed Tomography and Hounsfield Units  
CT is one of several medical imaging techniques based on tomography, alongside MRI and positron emission tomography (PET), all of which are used in radiology for diagnostic purposes by producing cross-sectional images of the internal anatomy. Unlike MRI or PET, CT relies on X-rays to acquire image data.  
A CT scan results in a volumetric model composed of a stack of two-dimensional images, where the pixel values (scalars) are stored in a regular voxel-based data structure. Each voxel's pixel value corresponds to a specific property measured by the scanner and then converted into a CT number.  
DICOM (digital imaging and communication in medicine) is the internationally recognized standard for the digital storage and transmission of medical images. It was developed by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) [9; 10].  
In DICOM standards, the conversion units--known as the rescale type--are specified in the metadata tag [0028,1054]. Pixel values can be converted into meaningful units using one of two methods: rescale slope and intercept (tags [0028,1052] and [0028,1053]), typically used when the relationship is linear, where the values of the slope and intercept are device-specific and provided by the hardware manufacturer; lookup table (LUT) (tag [0028,3000]), used when the conversion relationship is nonlinear.  
CT numbers are most commonly expressed in Hounsfield units (HUs), a relative quantitative measure of radiodensity widely used in clinical interpretation [11]. However, the limitations and inconsistencies of HUs as absolute values have been known since the early days of CT [12]. HU values are proportional to the linear attenuation coefficient \(\mu\) (cm\({}^{-1}\)) of a material, which depends on the effective photon energy E (keV). In practical terms, HU values represent CT attenuation normalized to the reference values of water water water and air \(\mu_{air}\):  
\[HU=\left(\frac{\mu-\mu_{water}}{\mu_{water}-\mu_{air}}\right)\cdot 1000\]  
which are 0 and \(-1000\) by definition, respectively [13]. It also possible to obtain HU values vs. physical density \(\rho\) curves [14; 15].
## 3 The Proposed System
### Energy Model
#### 3.2.1 LEACH Protocol  
Since LEACH is a common clustering routing protocol that has high representativeness, it is used as a research object. These are the preconditions assumed by the LEACH protocol:  
1. There are no differences between sensor nodes, and radio signals in every direction consume the same energy; each node has the same initial energy, and energy is limited, so each node can tell how much energy is left. Every node possesses sufficient computing power to control the transfer distance and transmit power.
2. All nodes communicate together and are directly connected to the BS.
3. The sink nodes are fixed and some distance from the whole WSN, as they are assumed to have adequate power supply.  
The round is the cycle in which a cluster reconstruction process in the LEACH protocol cycles. The round is separated into the setup phase (cluster) and the ready phase (transmission). The setup phase is shorter than the ready phase.  
_CH Node Selection:_ In the setup phase, select the CH based on the number of CHs needed in the network and the number of times each node became a CH.  
\[T_{n}=\begin{cases}\frac{P}{1-P\left(r*mod\left(\frac{1}{r}\right)\right)}&if \quad n\in G\\ 0&if\quad n\
otin G\end{cases}\]  
\[P=\frac{l}{k}\]  
where:  
\(T_{n}\) Threshold selected random value from [0, 1]; \(r\) is the round's number; \(G\) is a group. \(P\) refers to the percentage of the CH node in the network, \(l\) denotes the CH nodes needed in the network, and \(k\) denotes the total number of sensors. After the CH node is determined, the entire network is notified through broadcast. Sensor nodes provide data to the CH node during the ready phase. The CH node gathers data from all cluster nodes and sends it to the BS [25].
## 2 Design of the Proposed Antenna Array
### Driven Slot Patch Antenna Layer  
The driven antenna element characteristic mode analysis and design processes are presented in Ref. [17]. The antenna is composed of a nonuniform patch with a pair of coupling slots on a low-dielectric-constant high-frequency substrate (Rogers 5880, USA,  
Figure 1: Antenna array 3D diagram.  
ROGERS Corporation, h = 0.508 mm, \(\varepsilon_{r}\) = 2.2, tan\(\delta\) = 0.0009), as shown in Figure 2a. The equivalent circuit is shown in Figure 2b, which could be characterized by a series of characteristic modes (CMn). An inductively coupled excitation (ICE), such as conductor circles or slots, could move a higher-order CM to a lower-order desired CM to enhance the bandwidth of the antenna. As shown in Figure 2c, two matching slots adopted on the patch are used for impedance matching, which can help enhance the bandwidth and gain by shifting CM3 closer to CM2.  
The optimized parameters of the proposed antenna element were simulated in detail, and the final fabricated and measured results are shown in Figure 3. Figure 3a shows the top- and bottom-view photographs of a fabricated prototype, with a compact size of 7.23 \(\times\) 2.6 \(\times\) 0.508 mm\({}^{3}\). The impedance (\(Z_{\text{in}}\) = \(R_{\text{in}}\) + \(jX_{\text{in}}\)) and reflection coefficients (S11) are shown in Figure 3b. The antenna impedance is well matched in the range of 25 GHz to 28 GHz, where S11 \(<-\)10 dB, and it obtains \(R_{\text{in}}\) = 50 \(\Omega\) and \(X_{\text{in}}\) = 0 at 27 GHz. Figure 3c shows the simulated and measured realized gain versus frequency. The measured peak gain is 7.9 dBi; the enhanced gain characteristics are thanks to the successful implementation of the nonuniform patch [17].  
Figure 2: (a) Schematic topology of the driven slot patch antenna element; (b) equivalent circuit model of the proposed antenna element; (c) graphical demonstration the antenna size and CM frequency.
# Trustworthiness of Situational Awareness: Significance and Quantification
## 5 Numerical Results
### Case 1: High-Trust Information Source
Now that we have calculated the trustworthiness of the ISR UAV as well as all the nodes and links in the path between the UAV and the CBN, we can calculate the trustworthiness of SA information provided by the ISR UAV at the CBN using Equation (2) as:\[\mathcal{T}^{UAV}_{COP} = \mathcal{T}_{UAV}\cdot\mathcal{T}_{GCC}\cdot\mathcal{T}_{GN} \cdot\mathcal{T}_{RN}\cdot\mathcal{T}_{DFN}\cdot\] \[\mathcal{T}_{I_{1}}\cdot\mathcal{T}_{I_{2}}\cdot\mathcal{T}_{I_{3 }}\cdot\mathcal{T}_{I_{4}}\cdot\mathcal{T}_{I_{5}}\] \[= (0.995)(1)(1)(1)(1)\times\] \[(0.99875)(0.99988)(0.99988)(0.99988)(0.99988)\] \[= 0.99328\]