esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 AI Fairness in Practice
### Business
#### 7.2.4 Customer Service  
Artificial intelligence is also introduced into customer service, or the customer relationship management (CRM) system [296], which enhances customer interactions and support. As shown in Figure 6, the chatbots utilize natural language processing and sentiment analysis to understand customer queries and provide accurate, timely responses. They enable automated, efficient, and personalized customer interactions, improving user experience. AI-driven chatbots handle routine inquiries, offer real-time support, and gather insights for businesses to enhance their services. This technology aims to streamline customer service operations while ensuring effective and satisfactory customer interactions.  
However, some customer service chatbots show biased responses to users who speak certain language dialects [297; 298; 299]. Aiming at this, AI fairness is used in chatbot design to avoid biased responses or inappropriate behavior towards users [300; 301]. After implementing fairness checks, the chatbot provides culturally sensitive and fair interactions [302; 303].  
The current challenge in the customer service bot domain pertains to mitigating uncertain biased and inappropriate responses. Chatbots often unknowingly offer biased or offensive replies due to training data exposure, leading to customer dissatisfaction and reputational harm. Additionally, limitations in comprehending diverse linguistic expressions hinder accurate responses to various language forms, including slang. Further, inadequacies in addressing sensitive topics and emotional responses lead to inappropriate interactions in some customer service bots.  
In prospective research, countering the aforementioned concerns requires embedding bias detection and mitigation mechanisms to identify and address biased language and responses in chatbot interactions. Mitigating biased replies can be achieved by adopting inclusive training data representing diverse user demographics and employing natural language processing techniques to enhance language comprehension. Continuous learning is essential for customer service bots to adapt and comprehend various language styles through user interactions.
## 3 Methodology
### Risk Factor Recognition Model  
In this study, pre-trained DCNNs (ResNet50, MobileNetV2, ShuffleNetV1) were selected as the backbone. The ResNet model won the 2015 ImageNet large-scale visual recognition challenge contest with an error rate of 3.57% [33]. When training a deep neural network, the gradient may gradually become lost as the network gets deeper. This is called the gradient vanishing problem. To address this for improved training performance, a residual connection was proposed using a new technique called skip connection. The MobileNet model having dramatically reduced parameters was developed by Google in 2017 to run a deep learning model in a mobile environment [34, 35]. MobileNet is based on a structure in which the existing convolution operation is changed to depth-wise separable convolution, which is a combination of depth-wise convolution and point-wise convolution. Although the performance of MobileNet was not noticeably improved, its long-running speed and its memory usage are lower, which are improvements over existing models. MobileNet made it possible to utilize deep learning models in embedded devices such as mobile phones and tablet PCs. ShuffleNet is a model designed to focus on minimized parameters like MobileNet, and a new computational method combining point-wise group convolution and channel shuffle was proposed [36]. Point-wise group convolution is designed in a way that only operates on channels in a specific area, and the amount of computation is dramatically reduced. In addition, it was designed to efficiently extract features by allowing information between channels to be mixed well through the channel shuffle process.  
In this study, these three backbone models were trained using the transfer-learning method. Transfer-learning has the advantage of achieving high accuracy in a relatively short time, so it was judged to be suitable for this research environment. At this time, fine-tuning was performed using two strategies, and the individual methods are as follows. First, only a part of the upper layer was retrained in the feature extractor of the pre-trained DCNN models. Given that the image of the construction site used in this study has little similarity to the ImageNet dataset used for pre-training, only the upper layer was partially retrained to extract information specific to the problem. Retraining the upper layer so that it can learn features that can help detect risk factors is important for the performance and training of algorithms. Second, all layers of feature extractors of pre-trained DCNN models were retrained. This method uses only the structure of the pre-training model and trains every parameter newly based on the prepared dataset. The overall model architecture used in this study for recognizing the risk factor is shown in Figure 2.
## 3 System Model  
Consider an agricultural farm denoted as \(\mathcal{F}\) having a width (\(w\)) and a length (\(l\)). The farms are sub-labeled as \(f_{1}\),\(f_{2}\)...\(f_{m}\in\mathcal{F}\), where \(m\) represents the total number of farms. There exist a total of \(n\) sensors \(\mathcal{S}\) deployed to obtain field data. The framework can accommodate diversified sensors with different transmission times (\(T_{s}\)) and energy requirements (\(E_{s}\)). The sensor location is defined as \(S_{xy}\). The sensors also serve as gateways, forwarding data across the farm in addition to generating their own data. The UAVs are used for data collection from sensors and offloading to the back-end server for further analysis and decision-making. Each UAV has an energy denoted as (\(E_{u}\)) and a communication range (\(R_{u}\)).
# Instagram and WhatApp in Health and Healthcare: An Overview
## 2 Instagram  
Instagram's uses are mainly educational/informational and motivational/supportive. The app has great potential to serve as a social networking platform in visually rich disciplines, such as clinical dermatology [12], clinical infectious diseases [13] and radiology [14], owing to its strong, albeit not unique, photo- and video-sharing affordances. However, negative uses capitalizing on these same visual affordances have also been documented in the literature, such as the promotion of tobacco and alcohol use [15; 16; 17], of pro-anorexia messages [18] and of unhealthy foods that are high in calories but low in nutrients [19]. According to [PERSON] et al. [20], non-suicidal self-injury or self-harm content is also popular on Instagram and may influence adolescents, with 'Content Advisory' warnings failing to protect unwitting users.  
[PERSON] et al. [21] experimented with the use of Instagram in disease-related discussions and photo-sharing for peer-to-peer support among adolescents with type 1 diabetes. Another study by [PERSON] et al. [22] concluded that the use of Instagram with a home exercise programme as a motivational modality could be attractive and effective to reinforce adherence and maintain an appropriate level of physical activity. However, [PERSON] and [PERSON] [23] reported that Instagram fitspiration images (promoting exercise and healthy food to inspire viewers towards a healthier lifestyle) might have negative unintended consequences for body image among certain users, who are more prone to develop increased negative mood and body dissatisfaction and decreased state appearance self-esteem upon exposure to such images.  
Photo-sharing platforms are also being used for information exchange about public health crises, but the useful-signal-to-noise ratio can be low. Seventy-eight percent (78%) of images in an Instagram sample about Ebola were primarily coded as 'jokes' or 'unrelated' in a study by [PERSON] et al. [24]. Nevertheless, Instagram remains one of the tools used by the World Health organization (WHO), the US Centers for Disease Control and Prevention (Figure 1, [10]) and other public health bodies to disseminate visually-rich public health messages that both educate and interest the general public, and for risk communication during public health crises and man-made or natural disasters. [PERSON] et al. [25] report one such use by the WHO following Typhoon Haiyan in the Philippines.  
[PERSON] et al. [26] describe a quite novel use of Instagram to monitor Drug-Drug Interactions (DDI) and Adverse Drug Reactions (ADR) among the general public through complex network analysis of Instagram user timelines.
# LTE: Lightweight Transformer Encoder for Orbit Prediction
## 3 Results
### Prediction Performance
As a consequence, LTE-h6 achieved a 42.40% improvement over Transformer-h6 (encoder only). Performance on K5: For K5, the LTE model consistently performed well regardless of the \(n_{\text{head}}\) value. The best-performing model was LTE-h3, with a prediction error of 0.0000007, followed by LTE-h6 with a prediction error of 0.000008. Among a total of 12 models, excluding ours, Transformer-h3 (encoder only) performed best, with a prediction error of 0.0000010. Consequently, LTE-h3 demonstrated a 30.00% improvement over Transformer-h3 (encoder only). In summary, our LTE models rank as the best (bold with a checkmark) and the second best (bold without a checkmark) among a total of 14 models. The best-performing model (bold with a checkmark) demonstrates a substantial improvement across datasets, significantly reducing prediction errors compared to the best-performing model excluding our models (underlined). The results clearly show that LTE models outperform all other models in their prediction performance. The LTE model achieves excellent results with both three and six attention heads, suggesting the model's robustness. Additionally, not only for the LTE model but also for other algorithms (e.g., Transformer (encoder only)), the overall performance was generally higher when \(n_{\text{head}}\) was set to three rather than six. This demonstrates that when using less complex datasets, capturing relations between elements in a simplified way is more effective than capturing more complex relations.
## 3 Background
### Thermal System Overview
Figure 27 illustrates PGF superiority in terms of specific thermal conductivity as compared to copper and aluminum. Thermal energy storage units are another interesting technology developing for application on the CubeSat platform (Figure 28). They can store energy to protect components, and store said energy to power on-board systems. When designing a thermal system, careful consideration must be taken to consider electric energy dissipation of internal components, external incident radiation, particularly direct solar and albedo, and the harsh climate background temperature of space. The magnetic flux of the satellites orbit  
Figure 27: PGF vs. Aluminum and Copper Thermal Straps [45]. Figure 26: Mechanical Cryo-Cooler Configuration [23]. path also must be considered as this can cause interference with electrical components and affect the trajectory of the CubeSat's path by generating torques due to magnetic forces.
## 3 Related Works
#### Spectral Efficiency Enhancement
Generally, new signal processing techniques for 5G communication networks can be characterized into the following four parts:  
* New modulation, signal estimation, and coding techniques. * Effective spectrum management and new schemes (licensed and unlicensed). * Using new spatial processing schemes. * New system-level enabling technologies.
## 4 Simulation and Results
### Interference Modeling  
In essence, saying a method can handle RRI and RTI collisions does not guarantee they will not happen, but rather that the method has ways to lessen their impact. Proof involves showing how the method's design or algorithm manages resources and scheduling to minimize collisions, typically through mathematical modeling, simulation, or performance analysis under different conditions.  
In our study, we introduce a novel formula to quantify the number of active readers within a TDMA-based RFID system, considering multiple simulation periods (Ps) and available time slots (TSs).
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 7 Discussion  
### Applicability to Fading Channels  
In this paper, we have assumed AWGN channels with a constant SNR \(\rho_{i}\) in the \(i\)-th hop for \(i=1,2\). However, in real-world scenarios, wireless channels often experience random fluctuations due to fading and path loss [32; 33]. In this subsection, we discuss the applicability of our analysis and optimization framework under such conditions.  
First, in the average PAoI analysis, the PER expression \(\epsilon_{i}\) in (1) can be replaced with its expected value \(E[\epsilon_{i}]\), which accounts for random fluctuations in the SNR \(\rho_{i}\). Suppose that the instantaneous SNR \(\rho_{i}\) is modeled as a random variable with probability density function \(f_{\rho_{i}}(\cdot)\). Then, the average PER \(E[\epsilon_{i}]\) can be computed as  
\[E[\epsilon_{i}] =\int_{0}^{\infty}E[\epsilon_{i}\mid\rho_{i}=x]\,f_{\rho_{i}}(x)\, \mathrm{d}x\] \[\approx\int_{0}^{\infty}Q\Bigg{(}\frac{\frac{1}{2}\log_{2}(1+x)- \frac{S_{i}}{L_{i}}}{\log_{2}(e)\sqrt{\frac{1}{2L_{i}}(1-(1+x)^{-2})}}\Bigg{)} f_{\rho_{i}}(x)\,\mathrm{d}x. \tag{21}\]  
Details can be found in ([14], Footnote 2), ([16], Equation (20)), ([17], Equation (3)), ([34], Section V.A), ([35], Equation (11)), and ([36], Equation (8)). This approach is well suited to block-fading channels, where the channel coefficient remains constant during each packet transmission and varies independently between transmissions. Among various models, the Rayleigh block-fading channel has been widely adopted in prior studies on AoI analysis in the context of short-packet communications [16; 17; 35; 36]. It is also worth noting that the \(Q\)-function in (21) can be tightly approximated by a segmented linear function [37], which has been employed in recent studies to simplify the evaluation of \(E[\epsilon_{i}]\) (e.g., [16; 17; 35; 36; 38; 39]).  
Given the average PERs \(E[\epsilon_{1}]\) and \(E[\epsilon_{2}]\), the term \(p_{s}\) in (2) can be replaced with \(p_{s}=(1-E[\epsilon_{1}])(1-E[\epsilon_{2}])\), resulting in an average PAoI expression of the same form as in (10). Consequently, the optimization procedure described in Section 5 remains applicable, provided that the partial derivative \(\frac{\partial}{\partial L_{i}}\frac{1}{p_{s}}\) in (18) is reformulated to account for the dependence on \(E[\epsilon_{i}]\) for \(i=1,2\).  
Figure 7: Comparison of the minimum average PAoI \(\bar{A}_{\min}\) between group size optimization in [5] and JBG optimization for varying numbers of sensors \(N\).
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Model Interpretation  
The significance of interpretability arises from the fact that the prediction itself is often insufficient in real-world circumstances, such as agricultural ones. [PERSON] and [PERSON] [66] stressed that knowing what the model predicts is not always enough. Understanding the model's reasoning for making that prediction holds equal importance to answering the underlying query or decision-making need. Interpretability fills this gap, by providing insights into the model's reasoning, promoting informed decision-making, responsibility, and trust in precision agriculture [67].  
In the present framework, SHAP was implemented (SHAP Python library, version 0.47.2) to improve interpretability by quantifying each feature's contribution to the ML model's output. This analysis was applied only to the most accurate ML model identified in this study to understand the relative importance of spectral bands and VIs in crop yield prediction. Specifically, the present analysis used TreeSHAP, a customized version of SHAP for tree-based models [68]. Although calculating SHAP values can be computationally costly, this specialized variant offers an efficient technique by utilizing conditional expectancies and processing all feature combinations within the tree structure. TreeSHAP computes SHAP values in polynomial rather than exponential time by taking advantage of the hierarchical nature of tree-based models. This makes it practical for complex datasets. TreeSHAP evaluates the marginal contribution of each feature along all paths of a decision tree. It does so by comparing the model's output for a specific instance against a baseline expectation, iterating through the tree structure. Three important properties are satisfied by the local feature importance values: (a) SHAP values are designed such that their sum corresponds exactly to the model's prediction; (b) Features that are not utilized by themodel contribute nothing to its prediction, as their corresponding SHAP values are zero; and (c) A feature's SHAP value does not decrease as its contribution increases [46].
## 4 Parameters Affecting Nano-EOR
### Rock Wettability  
Increasing the water-wetness of the rock will increase oil recovery [158] and vice versa [159]. According to [PERSON] [159], strong water wetness and associated high capillary imbibition are favorable for more efficient oil displacement. However, in special cases, it has been reported that an oil-wet reservoir [160] and neutral wettability [161] offer higher oil recovery. Thus, wettability plays an important role in hydrocarbon mobility, affecting the distribution and displacement process of the hydrocarbon and other reservoir fluids within the matrix [5].  
During nano-EOR, initial wettability will determine the magnitude of the wettability alteration. An experimental study using silica NPs showed that the highest incremental oil recovery was yielded from an intermediate-wet core [150]. In an intermediate-wet condition, oil and brine are in the equilibrium state, which reduces the possibility of a disconnected and trapped oil phase in the matrix [150]. [PERSON] et al. [4] added that wettability affects the adsorption of NPs. Water-wet and neutral-wet cores have a higher adsorption of NPs than oil-wet media. In an oil-wet medium, the adsorption area is very close to the desorption area, which means that desorption is likely to happen. In that case, the effect of NPs on the oil-wet rock will be diminished.  
More advanced studies are needed to investigate the known and unknown parameters that could potentially affect the implementation of nano-EOR. Deeper understanding of both mechanisms and parameters is the key to success in real-world application of nanotechnology in EOR.
## 3 Results and Discussions
### Performance Comparisons of Faster R-CNN and Mask R-CNN
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Backbone Structures** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **AP (\%)** & **Time (SPF)** \\ \hline \multirow{4}{*}{Faster R-CNN} & \multirow{4}{*}{ResNet50} & FPN & 90.6 & 92.4 & 91.5 & 86.5 & 0.228 \\  & & C4 & 87.1 & 90.6 & 88.8 & 85.7 & 0.536 \\  & & DC5 & 88.1 & 90.2 & 89.1 & 85.6 & 0.379 \\ \cline{2-8}  & \multirow{4}{*}{ResNet101} & FPN & 84.5 & 90.4 & 87.4 & 90.1 & 0.251 \\  & & C4 & 88.7 & 84.5 & 86.5 & 87.1 & 0.564 \\  & & DC5 & 89.1 & 90.4 & 89.7 & 88.2 & 0.431 \\ \hline \multirow{4}{*}{Mask R-CNN} & \multirow{4}{*}{ResNet50} & FPN & 92.4 & 93.6 & 92.9 & 87.4 & 0.210 \\  & & C4 & 91.7 & 92.5 & 92.1 & 87.3 & 0.533 \\ \cline{1-1}  & & DC5 & 90.3 & 89.7 & 90.0 & 88.5 & 0.390 \\ \cline{1-1} \cline{2-8}  & \multirow{4}{*}{ResNet101} & FPN & 92.7 & 93.1 & 92.9 & 91.4 & 0.242 \\ \cline{1-1}  & & C4 & 91.3 & 92.4 & 91.8 & 88.7 & 0.614 \\ \cline{1-1}  & & DC5 & 90.6 & 90.1 & 90.3 & 90.2 & 0.422 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance data with different backbone structures. Figure 9: Testing results: (**a**) original image, (**b**) F-R50-FPN, (**c**) M-R50-FPN.
# Acoustic Negative Refraction and Planar Focusing Based on Purely Imaginary Metamaterials
## 5 All-Angle Negative Refraction and Planar Focusing  
For illustration, we arbitrarily take the incident angles as \(0^{0}\), \(30^{0}\) and \(55^{0}\). From the red curve (\(n\) vs. \(\theta\)) of Figure 5(a), it is found that the corresponding refractive indices are 0.84, 0.97 and 1.29, respectively. To verify the analytical results, numerical simulations are performed using COMSOL software. The Floquet periodic boundary condition is applied in the y direction. By launching plane acoustic waves from the left side with corresponding incident angles, pressure field distribution are shown in Figure 5(b)-d. In each picture, the white boxes with a thickness of \(0.3\lambda\) on the left and right side represent the PIM slab and PIM* slab, respectively. The black arrows represent the Poynting vector field. It can be seen that negative refraction occurs at the boundary of both the PIM and PIM* slab, demonstrating the perfect negative behavior described in Figure 4.  
In order to achieve planar focusing, negative refraction should be achieved for all angles of light emitted from the point source. Different longitudinal coordinates of the slab correspond to different incident angles. For example, a point source is placed to the left of the PIM slab, then the coordinate \(y\) is relevant to an incident angle of \(\theta=a\tan(y/f)\). Therefore, the refractive index of the PIM slab is inhomogeneous. The planar focusing performance of PIM-air-PIM* is calculated using Comsol software, and the material parameters are set to satisfy the dispersion relation between the refractive index and incidence angle, as shown in Figure 5(a). The thickness \(d\) is set to \(0.3\lambda\), and the length of the air gap \(L\) is set to \(12\lambda\). The point source is located \(6\lambda\) from the left PIM slab. Perfectly Matched Layers (PML) [32] boundary conditions are used at the perimeter of the structure to reduce reflections. As shown in Figure 7, the acoustic waves emitted from the point source are focused both in between the slabs and outside the right PIM* slab, respectively. The image resolution is diffraction-limited, as only the propagating wave components are considered.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 9 Challenges and Future Directions
### Open Problems
#### 9.1.4 Networking  
To address the networking issues in BackCom tags, [PERSON] et al. considered all BackCom nodes as a virtual sender, while dealing transmissions by tags as rateless codes [59]. This networking scheme deals with a large number of backscatter tags (100 or 1000). Similarly, efficient communication with BackCom-enabled passive devices is one of the challenging tasks. There are number of research opportunities to use backscattered signal-based network connectivity in a ubiquitous 5G network that enable efficient networking protocols.