esa-sceva/satcom-chunk-collection huggingface dataset:
# Face Recognition and Tracking Framework for Human-Robot Interaction
## 5 Experiments and Analysis
### Results  
The metrics used to measure the overall system performance are precision, recall, F-score, and recognition rate. We classify the predictions into True Positives (TP), False Positives (FP), False Negatives (FN), and True Negatives (TN). A _True Positive_ can be obtained in recognition when the model correctly predicted the subject class (i.e., subject ID), which means that it matches the ground truth. Otherwise, the prediction is considered a _False Positive_.  
A _True Negative_ can be obtained in recognition when the model is not supposed to predict a subject that is not in the database. Otherwise, the prediction is considered a _False Negative_.  
_Precision_ is the matching probability of the predicted subject identity relative to the ground truth identity, which shows the results of a correctly recognized subject. It can be calculated as follows:  
\[Precision=\frac{TP}{TP+FP}. \tag{3}\]  
_Recall_ measures the probability of the subjects that were correctly recognized among ground truth subjects, which is the total number of true positives relative to the sum of true positives and false negatives, as follows:  
\[Recall=\frac{TP}{TP+FN}. \tag{4}\]  
_F-score_ is evaluated as the harmonic mean of precision and recall to see which model best performs. It can be calculated as follows:  
\[F\text{-}score=\frac{Precision*Recall}{Precision+Recall}*2. \tag{5}\]  
The recognition performance can be obtained by the face recognition rate \(FR_{R}\), and it is the ratio between the total number of correctly recognized faces and the total detected/tracked faces. It can be calculated as follows:  
\[FR_{R}=\frac{TP}{Totalfaces}*100. \tag{6}\]In order to evaluate the proposed framework, we tested it for two different evaluations: dataset, and online evaluations.
## 3 Results and Discussion
### Perming Resistance to Shampoo  
The perming resistance to shampoo was determined by comparing the perming efficiency after six wash cycles with the perming efficiency before any washing step (Table 3). To determine the perming resistance to shampoo, we mimicked the conditions normally used in a daily routine: using tap water and a commercial shampoo brand (Pantene(r)Basic), without any pH adjustment. The perming resistances compared in all of the wash cycles with the perming efficiency before any washing step are represented in Figure 5. This parameter provides information about how strong is the protein effect during hair curling. Hair with high perming resistances to shampoo maintained the desired hair shape longer, while hair with low perming resistances lose the curled shape within the first few wash cycles. It is important to note that a higher perming resistance to washing does not reflect higher perming efficiency results.  
Comparing the results obtained for the loss of perms after six wash cycles, the hair treated with BSA in ethanolic formulation at pH 5 presented the highest perming resistance to shampoo. The effect of BSA on the hair resistance to wash (93.58%) was even higher than that measured for the chemical method (70.37%), pointing out the potential of this protein to be used as a perming agent. For the perming efficiency results, the hairs treated with keratin presented promising results for all of the tested pH, with perming resistance to shampoo ranging from \(\approx\)30% for pH 7 to \(\approx\)48% for pH 5. The hair treated with the other proteins also presented good perming resistance results; however, the perming efficiency of the hair treated with these proteins was not as high as that for BSA and keratin.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Perming Resistance (\%)**} \\ \hline  & **pH 5** & **pH 7** & **pH 9** \\ \hline Chemical & 70.37 & 70.37 & 70.37 \\ Keratin & 48.65 & 29.66 & 47.64 \\ KP-UM & 28.97 & 24.00 & 37.07 \\ Silk Fibroin & 41.45 & 30.00 & 36.08 \\ BSA & 93.58 & 11.85 & 35.26 \\ KP-Cryst & 36.49 & 17.36 & 40.83 \\ EtOH Formulation & 33.90 & 15.32 & 29.29 \\ Control & 7.62 & 7.62 & 7.62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hair perming resistance to washing. The resistance values were obtained by comparing the perming efficiencies after six wash cycles with the perming efficiencies before any wash step.
# Derivation of New Staggered Compact Schemes with Application to Navier-Stokes Equations
## 2 Problem Formulation and Derivation of New Compact Schemes
### Schemes for Second Derivative  
The evaluation of the second derivative of \(P_{j}\) at node \(x_{j}\) leads to the 4 CH-D2 scheme:  
\[f_{j}^{\prime\prime}=2\frac{f_{j-1}-2f_{j}+f_{j+1}}{h^{2}}-\frac{f_{j+1}^{ \prime}-f_{j-1}^{\prime}}{2h}, \tag{11}\]  
which was already derived by [PERSON] [12]. The combination of Equations (1) and (11) is again a centred fourth-order formula with truncation error \((1/360)h^{4}f^{VI}\). The graphical sketch of the proposed scheme, which is built upon the successive application of Equations (1) and (11), is given in Figure 2c. The computation of the explicit scheme in Equation (11) requires 7\(N\) operations. The reference fourth-order scheme for collocated second derivative, 4 CC-D2 is:  
\[\frac{1}{12}f_{j-1}^{\prime\prime}+\frac{5}{6}f_{j}^{\prime\prime}+\frac{1}{1 2}f_{j+1}^{\prime\prime}=\frac{f_{j-1}-2f_{j}+f_{j+1}}{h^{2}}, \tag{12}\]  
with truncation error \((1/270)h^{4}f^{VI}\).
# Contingent Task and Motion Planning under Uncertainty for Human-Robot Interactions
## 3 A Proposal for Contingent Task and Motion Planning
### The Proposed Framework  
The proposed framework for task and motion planning under uncertainty extends the basic _Contingent-FF_ planner, aiming to incorporate different geometric reasoning procedures, observations on sensing actions, as well as human-robot collaboration within planning. The overview of the system is sketched in Figure 1. It involves three main parts: _Heuristic Computation_, _Space Search_, and _Conditional Plans Evaluation_.  
_Heuristic Computation_ basically provides a value which is distance to goal and promising actions for each belief state. The basic _CRPG_ is initially computed and the associated relaxed plan is obtained. This plan is forwarded to the relaxed geometric reasoner determining the feasibility of actions in terms of reachability, collisions, manipulation constraints, and graspability. The heuristic value is returned along with helpful actions if such constraints are met. If a constraint is violated, the associated belief state is updated with facts describing the cause of failure, and an alternative relaxed plan is looked for. Hence, the heuristic function is informative both in terms of symbolic and geometric constraints.  
_Space Search_ maintains the basic algorithm of the _Contingent-FF_ planner that is based on the _And-Or_ search strategy. From each belief state, the action resulting in the state with lowest heuristic value is selected and is a candidate to be added to the conditional plans. The only difference is that the heuristic value now accounts for geometric constraints.  
_Conditional Plans Evaluation_ tries to solve motion planning for an action if possible. It considers sensing procedure to evaluate sensing actions and may assign actions, which are infeasible for robots, to operators.  
If motion planning fails, the current belief state is updated with the cause of failure and the search resumes. In general, the geometric failure could be due to collisionable objects, so the literal _isCrit_ is added to the belief state. If the failure is because of fixed obstacles blocking the way of reaching an object, inverse kinematic problems, or motion planning time-out problem, the literal _infeasByRob_ is added to the state. In such failure, if the type of the evaluated action is either transit or open, the literal _assist_ is also inserted.  
Otherwise, when motion planning succeeds, the action is added to the tree-shaped conditional plans at hand. After finding the complete conditional plans, feasible actions are executed by a robot or a human operator in the real world and sensing actions are observed either using a perception module or the information provided by the human operator in run-time. Therefore, the robot plan can determine the correct branch to follow up its plan.
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 2 Terahertz Signal  
Realizing an efficient, stable, and high-power THz signal source is key to the development of related applications. Especially in scenarios pursuing long-distance, high-capacity wireless communications, the performance of the signal source directly determines  
Figure 1: Path loss and atmospheric loss: (**a**) free space path loss; (**b**) atmospheric loss.  
the system's link budget and transmission capabilities [46; 47]. Currently, as shown in Figures 2 and 3, the generation of THz signals primarily relies on two main technological pathways: electronic approaches and photonic approaches.
# Will the Use of Pharmacogenetics Improve Treatment Efficiency in COVID-19?
## 2 Remdesivir (GS-441524)
Apart from CYP enzymes, remdesivir has been demonstrated to be a substrate for the organic anion transporting polypeptide 1B1 (OATP1B1) and P-glycoprotein (P-gp) [32]. The first one belongs to the transmembrane family of transport proteins and is responsible for the uptake of substances into the cells of various organs, primarily the liver, while the latter one is an efflux pump [33]. OATP1B1 transporter is encoded by _SLCO1B1_ gene and until now several allele variations affecting drug disposition (rs2306283, rs56101265, rs56061388, rs72559745, rs4149056, rs72559746, rs5901008, rs59502379, and rs56199088) have been found. All these variants are associated with reduced transporter function [34]. In turn, P-gp is encoded by _ABCB1_ gene (belonging to adenosine triphosphate (ATP)-binding cassette (ABC) genes) which is also polymorphic (e.g., rs1128503, rs2032582, and rs1045642). The results of studies have revealed that P-gp is involved in viral resistance and trafficking cytokines and enveloped viruses [35]. The aforementioned variants could theoretically affect the pharmacokinetics of remdesivir, but since the risk of significant genetic pharmacokinetic interactions seems to be low, no guidelines recommend pharmacogenetic testing before the administration of this drug [3]. No pharmacogenomic analysis has been performed so far. Ongoing clinical trials which study the antiviral activity of remdesivir in patients with COVID-19 include NCT04292899, NCT04292730, NCT04257656, NCT04252664, and NCT04280705 [36].
## 5 Experimental Results
### Geometric Attacks
#### 5.3.1 Cropping Attack  
A piece of the picture is trimmed, which is expected to influence the watermark installed in the picture. As the watermark is installed into the picture utilizing DWT and SVD strategies, we can, in any case, recuperate the watermark picture, and the genuineness remains unblenished.
# Research on O2O Takeout Delivery Route Optimization Problem Based on Improved Genetic Algorithm
## 3 Establishment and Handling of Mathematical Model
### Customer Satisfaction Function  
The customer's satisfaction mainly depends on the delivery time of the order. [PERSON] [23] summarized three commonly used customer satisfaction functions, and this paper adopts the linear time satisfaction function among them. Let the satisfaction of the customer \(i\) be denoted as \(s_{i}\), whose order is served by the rider \(k\). The time at which the rider starts service for this customer is denoted as \(t_{ik}\). The schematic diagram of the function \(s_{i}\) is shown in Figure 1. When \(e_{i}\leq t_{ik}\leq l_{i}\), \(s_{i}\) takes the value of 1; when \(l_{i}<t_{ik}\leq L_{i}\), it decreases linearly with time, and its expression is \(\frac{L_{i}-t_{ik}}{L_{i}-l_{i}}\); when \(t_{ik}>L_{i}\), \(s_{i}\) takes the value of 0.
## 3 Methods
### Channel Attention Mechanism  
To assess and adjust the significance of various channel features, we integrated a CAM module into the feedback block architecture. [27] This module comprises four components: global average pooling, feature compression, attention weights generation, and feature rescaling.  
The first step in the channel attention mechanism involves global average pooling. This process begins by passing the input feature map through an adaptive average pooling layer, which serves to globally average the spatial information of each channel. This pooling step condenses each channel into a single value that represents the average intensity of that channel across the entire feature map. The next phase, the feature compression layer, takes the average pooling result and flattens it before passing it through a series of fully connected layers. These layers consist of two fully connected linear transformations followed by a Relu activation function. The first linear transformation compresses each channel dimension to preserve essential information and reduce computational complexity. The subsequent Relu activation function introduces nonlinearity to capture complex feature dependencies. Finally, the second linear transformation maps the compressed features back to their original channel dimensions, providing attention to each channel. The next stage involves the generation of attention weights. After the fully connected layer processing, the feature dimension is restored to the original feature map, allowing for element-wise multiplication with the original features. The output of each channel is then scaled to the range [0, 1] using the Sigmoid activation function, resulting in attention weights for each channel. These attention weights are then multiplied by the corresponding channel features of the input. Finally, the attention weights are multiplied by the features of the corresponding channels of the input, which emphasizes the regions that contribute the most to the classification decision. This process enhances the model's ability to generalize across various scenarios and periods, which is essential for consistent change detection across diverse regions and timeframes.
###### Abstract  
Traditional rate-distortion (RD) theory examines the trade-off between the average length of the compressed representation of a source and the additive distortions of its reconstruction. The rate-distortion-perception (RDP) framework, which integrates the perceptual dimension into the RD paradigm, has garnered significant attention due to recent advancements in machine learning, where perceptual fidelity is assessed by the divergence between input and reconstruction distributions. In communication systems where downstream tasks involve generative modeling, high perceptual fidelity is essential, despite distortion constraints. However, while zero distortion implies perfect realism, the converse is not true, highlighting an imbalance in the significance of distortion and perceptual constraints. This article clarifies that incorporating perceptual constraints does not decrease the necessary rate; instead, under certain conditions, additional rate is required, even with the aid of common and private randomness, which are key elements in generative models. Consequently, we project an increase in expected traffic in intelligent communication networks with the consideration of perceptual quality. Nevertheless, a modest increase in rate can enable generative models to significantly enhance the perceptual quality of reconstructions. By exploring the synergies between generative modeling and communication through the lens of information-theoretic results, this article demonstrates the benefits of intelligent communication systems and advocates for the application of the RDP framework in advancing compression and semantic communication research.  
Keywords:rate-distortion-perception trade-off; perceptual fidelity; lossy compression; AI-empowered communication +
Footnote †: journal:
## 1 Introduction
In this research, we propose a context-based fuzzy C-means (CFCM) clustering-based rule generation approach instead of a general clustering-based rule generation methodology that takes into account the patterns of the input space as well as the output space and ANFIS in the form of an incremental tree structure rather than a single structure. Whereas general clustering methods only take the input space into account, the CFCM clustering approach often takes the output space pattern into account, so that the cluster can be generated more accurately. There are many inputs when using big data in numerous application fields. In the neuro-fuzzy system, as the number of inputs increases, the number of rules increases exponentially. Therefore, it creates meaningful rules by designing a point-of-point tree structure using multiple ANFISs rather than a single ANFIS structure. To evaluate the performance of ANFIS in an incremental tree structure based on the CFCM clustering method, a computer performance prediction experiment was conducted using a building heating-and-cooling dataset [32]. The building heating-and-cooling dataset is a dataset used for energy efficiency forecasting created by Xifara. It consists of eight input variables and two output variables and has a data size of \(768\times 10\). The remainder of this paper is structured as follows. Section 1 explains the background of the study. Section 2 describes the method and structure of ANFIS rule creation. In Section 3, the proposed method, ANFIS, with an incremental tree structure based on the CFCM clustering method, is described. Section 4 analyzes the predictive performance of the proposed method, and Section 5 addresses conclusions and future research plans.
# Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph
## 4 Experiments
### Experimental Results  
In order to verify the performance of NACF, we compared NACF with baselines in top-k recommendation and CTR prediction. In the top-k recommendation, the trained model selects \(K\) items with the highest click rate for each user of the testing set, and evaluates the selected items of the model with Recall@\(K\) Precision@\(K\) and NDCG@\(K\) (\(K\in\) 1, 2, 5, 10, 50, 100). In CTR, we use area under curve (AUC), accuracy (ACC) and F1 score as metrics to evaluate the performances of all models. All experiments are conducted four times; the average values of the metrics are calculated.  
The performances of all models in the top-k recommendation and CTR prediction are shown in Figure 4 and Table 4. We can observe:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MovieLens-20M** & **Last.FM** & **Dianping-Food** \\ \hline \#Users & 138,159 & 1872 & 2,298,698 \\ \#Items & 16,954 & 3846 & 1362 \\ \#Interaction & 13,501,622 & 42,346 & 23,416,418 \\ \#Entities & 102,569 & 9366 & 28,115 \\ \#Relations & 32 & 60 & 7 \\ \#KG triples & 499,474 & 15,518 & 160,519 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MovieLens-20M** & **Last.FM** & **Dianping-Food** \\ \hline \#Neighbor & 4 & 4 & 4 \\ \#Dimension & 32 & 64 & 32 \\ \#hop & 2 & 1 & 2 \\ \#Batch\_size & 1024 & 256 & 1024 \\ \#Epoch & 10 & 10 & 10 \\ \#learning rate & \(2\times 10^{-2}\) & \(5\times 10^{-4}\) & \(2\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyper-parameter settings.  
1. As shown in Figure 4, NACF achieve optimal performance in the top-k recommendation of three datasets. Specifically, NACF outperforms the best of baselines by 16.48%, 10.62% and 24.88% in Recall@20 on the movie, music and restaurant sets, respectively. It also achieved 15.30%, 24.43% and 17.51% performance lead in Precision@10. In NDCG@10, NACF took the lead by 8.11%, 18.37% and 9.60% respectively. It shows that NACF can make good use of knowledge graph to assist recommendation in TOP-K scenarios.
2. In the CTR prediction, Table 4 shows that NACF achieved leading performance in AUC, ACC and F1. But the improvement is not very great, especially in movie. For music, the improvement of metrics is better than for movie. It shows that NACF can solve the sparsity problem to a certain extent, since Last.FM is much sparser than Movielens-20M and Dianping-Food.
3. The performances of NACF, KGCN and RippleNet are basically ahead of CKE, SVD and libFM. It is worth mentioning that KGCN and Ripple also use the multi-hop neighborhood of KG, which illustrates that extracting multi-hop information in KG is necessary for recommendation. The CKE model performed very poorly in our experiments. It proves that CKE cannot make full use of KG with KGE algorithm like transR.
## 2 Literature Review
### Price Forecasting  
#### 2.2.1 Statistical Methods  
The price volatility is a crucial element in evaluating risk and determining the pricing of downstream commodities. [PERSON] [9] has noted that fluctuations in the trading prices of commodities often present characteristics such as a spiky thick-tailed distributions of returns, the aggregation of fluctuations, persistence, leverage effects, volatility spillover effects, and interdependence among higher-order error terms. [PERSON] [10] introduced an Autoregressive Conditional Heteroskedasticity Model (ARCH) and applied this model to study the volatility of the U.K. inflation index. However, its limitation is that it treats positive and negative fluctuations as having the same impact on volatility and imposes strict parameter constraints. [PERSON] [11] proposed the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model based on the ARCH model, providing a more detailed description of asset volatility dynamics. The ARCH model can capture the interdependence between errors and describe the temporal aggregation and variation of time-series. It is frequently employed in the forecasting of financial asset volatility. [PERSON] [12] introduced the stochastic volatility model, which is capable of capturing abrupt changes in financial time-series.  
The abovementioned models are primarily based on low-frequency data. [PERSON] et al. [13] put forth the realized volatility, which is applicable to high-frequency data for assessing price volatility. This method boasts ease of computation and non-parametric modeling characteristics. While the ARIMA model can predict non-stationary time-series and offers quick computation, its accuracy is compromised when the series presents higher volatility.