esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 4 Experiments
### State-of-the-Art Comparison
#### 4.3.1 Results on OTB  
OTB benchmarks include OTB50 [36] and OTB2015 [37]. OTB2015 dataset contains 100 manually labeled video sequences, so the OTB2015 dataset is also called OTB100. OTB50 selects 50 difficult video sequences from OTB2015. Common evaluation metrics in the OTB benchmarks are accuracy and success rate. Accuracy refers to calculating the Center Location Error (CLE) between the model's forecast location (bounding box) and the ground-truth of the target given a threshold T. The success rate refers to the IoU between the predicted position frame of the model and the target real position frame given a threshold T. On the basis of the accuracy and success rate indicators, OTB proposed the One Pass Evaluation (OPE) robustness evaluation indicator, which refers to initializing the first frame with the position of the target in the ground-truth, and then running the tracking algorithm for the purpose of gaining the average precision and success rate, generate OPE metrics.  
The comparative experiments between the SiamFP tracker and the more advanced trackers SRDCF [38], Staple [39], CFNet, SiamFC, fDSST [40], and SiamRPN have achieved leading levels in both OTB50 and OTB2015 benchmarks, and the OPE accuracy graph and the success rate graph is displayed in Figure 4. The experimental outcomes show that in the OTB50 and OTB2015 benchmarks, the tracking accuracy reaches 0.829 and 0.88, and the success rate reaches 0.604 and 0.658, respectively. Compared with the SiamFC algorithm,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Module**} & \multicolumn{2}{c}{**OTB50**} & \multicolumn{2}{c}{**OTB2015**} \\ \cline{2-5}  & **Success** & **Precision** & **Success** & **Precision** \\ \hline baseline & 0.519 & 0.693 & 0.586 & 0.772 \\ +PW & 0.552 & 0.764 & 0.620 & 0.835 \\ +PW+FP & 0.577 & 0.781 & 0.634 & 0.857 \\ +PW+FP+Att & 0.604 & 0.829 & 0.658 & 0.880 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation Study of SiamFP on OTB Benchmarks. PW denotes Optical flow Estimated Network. FP denotes Feature Pyramid. Att denotes Dual-attention Module.  
the precision is increased by 13.6% and 10.8% correspondingly, and the success rate is increased by 8.5% and 7.2% correspondingly. In the OTB2015 benchmark, the performance is significantly improved, especially in three complex scenes, occlusion (OCC), illumination variation (IV), and fast motion (FM). As shown in Figure 5. More results are summarized in Table 2. For the purpose of determining the effective improvement of the algorithm in this study in actual video sequences, video sequences with relatively complex scenes were selected from the OTB dataset as the visualization results, as shown in Figure 6. To sum up, the SiamFP algorithm adding optical flow network and attention mechanism has a good ability to suppress background interference.  
Figure 5: The Success Plots and Precision Plots on OTB2015 Dataset with Three Attributes.  
Figure 6: _Cont_.
## 2 Materials and Methods
### Specific Migration of Styrene Monomer from PS Pots and Sheets into Food Simulants  
Determination of the specific migration of styrene monomer into food simulants was achieved using the thermoformed PS pots and the extruded sheets.  
Migration testing was carried out by the total immersion of the test specimen in pressure-resistant stainless-steel cells under the conditions of 10 days/40 \({}^{\circ}\)C in the 50% ethanol food simulant. Additionally, migration experiments were performed under the conditions of 10 days/40 \({}^{\circ}\)C and 10 days/20 \({}^{\circ}\)C in the 20% ethanol food simulant. Each test was carried out in triplicate. The test specimens were made into strips, with dimensions of 2.5 cm \(\times\) 10 cm, cut from the pots and the sheet samples. These strips were placed in stainless steel migration cells containing 80 mL of food simulant, minimizing the remaining headspace. The applied surface/volume ratio in the migration experiments corresponded to the conventional EU cube model of 6 dm\({}^{2}\)/kg. The total surface (0.5 dm\({}^{2}\)) in the total immersion test was calculated by considering both sides of the test specimens, excluding the area of the cut edges. This choice was made by taking into account that PS is a low-diffusive polymer and migration is independent of both sides, in accordance with the draft \"Technical guidelines for compliance testing\" [21].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Name** & **Description** & **Styrene Residual Content \(\pm\) SD** \\ \hline GPPS-1 & GPPS pellets grade 1 & 373 \(\pm\) 5 \\ GPPS-2 & GPPS pellets grade 2 & 407 \(\pm\) 8 \\ GPPS-3 & GPPS pellets grade 3 & 505 \(\pm\) 15 \\ HIPS-1 & HIPS pellets grade 1 & 248 \(\pm\) 3 \\ HIPS-2 & HIPS pellets grade 2 & 312 \(\pm\) 3 \\ \hline PS-S-11 & PS sheets made with GPPS-1 and HIPS-1 (converter A) & 286 \(\pm\) 2 \\ PS-S-22 & PS sheets made with GPPS-2 and HIPS-2 (converter B) & 381 \(\pm\) 5 \\ PS-S-32 & PS sheets made with GPPS-3 and HIPS-2 (converter B) & 412 \(\pm\) 4 \\ \hline PS-P-11 & PS pots made from PS-S-11 (converter A) & 262 \(\pm\) 6 \\ PS-P-22 & PS pots made from PS-S-22 (converter B) & 376 \(\pm\) 4 \\ PS-P-32 & PS pots made from PS-S-32 (converter B) & 394 \(\pm\) 5 \\ \hline \hline \end{tabular} SD: standard deviation of triplicate analysis.  
\end{table}
Table 1: Content of styrene monomer in the investigated PS sample materials.  
Static headspace GC, coupled with FID, was applied to determine the migrated amount of styrene in the simulants. An Agilent 6890 system (Santa Clara, CA, USA) equipped with a ZB 624 column was used, under the same conditions as those described in a previous publication [20]. The quantification of styrene was carried out by external calibration in 20% ethanol using m-xylene (CAS: 108-38-3; Sigma-Aldrich, Saint Louis, MO, USA) as the internal standard. Independent triplicate migration experiments and triplicate measurements of each migration solution were performed.  
The HS-GC-FID method used for the analysis of food simulants has a detection limit of 1.3 ug/kg in 20% ethanol and of 3.2 ug/kg in 50% ethanol.
# Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
## 5 Discussion, Implications, and Limitations
Optimization of algorithms may be needed, such as reducing data dimensionality or employing parallel or distributed machine learning techniques. 3. Larger datasets may introduce noise or heterogeneity, necessitating careful data preparation and cleaning. This study's findings are based on the analysis of 500 transactions from a single e-commerce store. While sufficient for validating the model's concept, this limited scope poses challenges for generalizing the results. Data from a single source do not capture the diversity of market conditions encountered across different industries or regions. The data collection process relied on the management system of the selected online store, and transaction labels were assigned by experts. Although standard for early-stage research, this introduces potential biases, such as subjective label assignments or limitations stemming from the technology used. Future research will aim to expand the dataset to include data from various industries, regions, and diverse market structures. This approach will provide a more comprehensive evaluation of the model's effectiveness under different conditions, enhancing the generalizability of results. Additionally, detailed descriptions of data collection processes and analyses of potential biases will be essential to further improve the model's reliability. A critical aspect of dynamic pricing models is their alignment with ethical principles to ensure fairness and transparency. One possible mechanism to address these concerns involves implementing clear rules that minimize price discrimination and protect vulnerable consumer groups. 1. Algorithm Transparency--pricing models should be designed transparently, with explicitly defined criteria for price adjustments that are accessible and understandable to consumers. This prevents unintentional or unjustified price discrimination. 2. Price Ceilings and Floors--establishing maximum and minimum price thresholds can prevent excessive exploitation of temporary market imbalances, such as during crises where demand exceeds supply. 3. Rules for Vulnerable Groups--the model should account for discounts or special pricing rules for specific groups, such as seniors, students, or low-income individuals, to avoid market exclusion. 4. Monitoring and Audit Mechanism--regular reviews of the algorithm, including independent audits, can ensure compliance with ethical standards and reduce the risk of abuse. These considerations emphasize the need for a balance between dynamic pricing efficiency and societal expectations of fairness. Integrating such mechanisms into the proposed model could enhance its acceptance and practical applicability.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 3 Overview of NR PRS and SRS-Pos Design
### DL Positioning Reference Signal, DL-PRS
In NR system, such a requirement is achieved by specifying muting option1 (i.e., Opt1) and option2 (i.e., Opt2) through different bitmaps. When a bit in the bitmap is zero, the PRSs at the corresponding time instants are not transmitted. For Opt1, one bit corresponds to one PRS transmission period, while corresponding to one PRS repeated transmission within each PRS period for Opt2. Application of Opt1 and Opt2 simultaneously in NR system to mute not only a repeated instant but also a periodic instant is allowed. Several examples under different muting patterns are provided in Figure 3. Example (a) is a normal PRS transmission without muting. Example (b) represents PRS1 and PRS2 transmission under muting Opt1 with a bitmap \(\{1,0,0,1\}\). Then, the PRS transmission in the second and the third periods are muted. Under the same bitmap with Example B, the second and third repeated PRS transmission within each period are muted in Example (c) with a muting Opt2. By combining Opt1 and Opt2, the second and third repeated PRS instants of each period are muted, while the second and third periods are also muted in Example (d). Figure 1: Illustration of PRS and SRS-Pos design architecture. (**a**) PRS Design architecture, (**b**) SRS-Pos design architecture. Figure 2: Illustration of PRS and SRS-Pos comb-based time-frequency RE mapping. (**a**) PRS comb-based RE mapping. (**b**) SRS-Pos comb-based RE mapping.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 5 Centralized Key Management Scheme for LoRaWAN
### Backward Compatibility Scheme (BCS)  
Current LoRaWAN v1.1 supports backward compatibility. However, it would result in a downgrade in security as the security issues that happened in the lower version would emerge as the v1.1 end device tries to connect with a version number lower than v1.1 network server. Hence, this would result in a lower protocol to be used in that case. Therefore, in the BCS that the lower version network servers would tell the end device what to do based on the requirement of the device-security level, i.e., it would continue the processing for a low-security level end device. However, for a high-security level end device, the network server would redirect the traffic to a nearby v1.1 network server (See Figure 4).
## 5 Experimental Results
### Results and Discussion  
This study was developed in Python 3.10 using the Google Colab-Pro platform. We selected the robust hardware infrastructure supported by NVIDIA's A100 GPU. This hardware has a capacity of 2 \(\times\) 16 G (32 GB) RAM. We evaluated the performance results for the standard models in terms of the precision, accuracy, recall, F1, mean absolute error (MAE), median absolute error (MAD), and maximum absolute error metrics.
## 3 Data Collection and Preprocessing
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline \(Timestamp_{t}\) & Absolute time (Unit: ns) at time \(t\) \\ \(Act_{t}\) & Touch state at time \(t\) \\ \(\{\)D (Down), M (Move), U (Up)\(\}\) \\ \(X_{t}\) & Coordinate of x-axis at time \(t\) \\ \(Y_{t}\) & Coordinate of y-axis at time \(t\) \\ \(Ax_{t}\) & Angular velocity of x-axis at time \(t\) \\ \(Ay_{t}\) & Angular velocity of y-axis at time \(t\) \\ \(Az_{t}\) & Angular velocity of z-axis at time \(t\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of seven raw data variables. \[T\_Diff_{(t,t+1)}=Timestamp_{t+1}-Timestamp_{t}, \tag{1}\]  
As presented in Table 6, we used five combinations of the variable set to investigate the effect of each variable category on authentication performance. To develop the sequential model, we used the following input variables: (i) past-to-previous-time variables, \(Ax\), \(Ay\), \(Az\), \(X\), and \(Y\), and (ii) past-to-current-time variables, \(T\_Diff\) and \(Act\). Then, we used the following output variables from the current time: \(Ax\), \(Ay\), \(Az\), \(X\), and \(Y\). Figures 4 and 5 depict the used variables based on the five combinations of the variable set for the forward and backward sequential structures, respectively. Coordinate-A/B, Angular-A/B, and full variable combination sets use the output variable coordinate-based feature (\(X\),\(Y\)), angular velocity-based feature (\(Ax\), \(Ay\), \(Az\)), and both, respectively. \begin{table}  
\end{table} 
Table 6: Five combinations of variable sets used in the study. Figure 4: Input and output variables at time \(t\) based on five combinations of variable sets for the forward sequence (blue and yellow denote input and output variables, respectively). Figure 5: Input and output variables at time \(t\) based on five combinations of variable sets for the backward sequence (red and yellow denote input and output variables, respectively). \begin{table}  
\end{table} 
Table 5: Data transformed from the collected raw data.
## 2 Systematic Analysis Methodology
By detailing each of these sub-steps, the methodology ensures a clear understanding of how NLP techniques and expert judgment were integrated to structure and validate the thematic categorization. This hybrid approach ensures a balance between automated NLP scalability and expert-driven interpretability, enhancing the overall reliability of the thematic framework. The following breakdown provides a step-by-step explanation of this process, outlining the key stages involved in the integration of NLP methods and expert evaluations:  
1. Document Corpus: The process begins with a consolidated set of documents, selected after applying inclusion and exclusion criteria. This ensures that only relevant and high-quality studies are included for analysis. 2. NLP Pre-processing: A systematic text-cleaning process is applied, including tokenization, stop-word removal, and lexical normalization. These steps prepare the textual data for subsequent analytical processes. 3. NLP Topic Modeling and Term Extraction: Statistical and machine learning-based approaches are used to detect recurring themes and key terms within the document set. 4. Expert Validation and Feedback: Domain experts review the preliminary themes, providing insights to refine and correct any inconsistencies. 5. Integration of Experts' Insights (Hybrid Review): Expert feedback is integrated into the thematic classification, ensuring a balance between automated processing and domain relevance. 6. Refined Themes and Categorization: Based on expert contributions, topics are relabeled and organized into a structured framework. 7. Final Thematic Structure: The process concludes with a validated set of thematic categories that serve as the foundation for subsequent quantitative and qualitative analysis.
## 5 Conclusions
\begin{tabular}{l l} P & Perimeter, m \\ P\({}_{\rm el}\) & Electricity, kW \\ Q & Heat rate, kW \\ T & Temperature, \({}^{\circ}\)C \\ t & Thickness, m \\ t\({}_{\rm fin}\) & Thickness of the fin, m \\ T\({}_{\rm sky}\) & Sky temperature, K \\ U & Thermal transmittance value, W/m\({}^{2}\) K \\ V\({}_{\rm w}\) & Wind speed, m/s \\ W\({}_{\rm c}\) & Compressor work, kW \\ W\({}_{\rm fin}\) & Width of the fin, m \\
**Greek symbols** \\ \(\alpha\) & Absorptance \\ \(\beta\) & Thermal loss coefficient, K\({}^{-1}\) \\ \(\varepsilon\) & Emmittance \\ \(\eta_{\rm ls,c}\) & Isentropic efficiency of the compressor \\ \(\eta_{\rm motor}\) & Motor efficiency of the compressor \\ \(\eta_{\rm PV}\) & Photovoltaic efficiency \\ \(\eta_{\rm ref}\) & Reference efficiency of the PV cell \\ \(\sigma\) & Stefan-Boltzmann constant (=5.6\(\cdot\)10\({}^{-8}\) W/m\({}^{2}\) K\({}^{4}\)) \\ \(\tau\) & Cover transmittance \\ \(\Psi\) & Linear thermal transmittance value, W/mK \\
**Subscripts and Superscripts** \\ abs & Absorbed \\ am & Ambient \\ b & Back surface \\ cell & PV cell \\ cond & Condenser \\ condv & Conduction \\ conv & Convection \\ evap & Evaporator \\ fg & Window glass and frame \\ fin & Fin of the back surface \\ frame & Window frame \\ glass & Window glass \\ loss & Thermal losses \\ m & Mean value \\ melt & Melting point \\ PV & Photovoltaic \\ rad & Radiation \\ sol & Solar \\ tb & Thermal bridge \\ u & Upper surface \\ u,rad & Upper radiative surface \\
**Abbreviations** \\ ACH & Air Changes per Hour \\ BAPV & Building Applied Photovoltaic \\ BIPV & Building Integrated Photovoltaic \\ DHW & Domestic Hot Water \\ EU & European Union \\ PCM & Phase Change Material \\ PV & Photovoltaic \\ ZEB & Zero Energy Building \\ \end{tabular}
## 3 Results and Discussion
### Suggested Adsorption Mechanism  
The adsorption mechanism of MB on the BCA-Mag/AB surface is proposed based on the existing surface functional groups such as -OH, COO-, C=O, and C-O bonds as found in alcohols, ethers, or phenols. It has been found that there are coordination sites provided by two main functional groups, carboxylate (COO-) and hydroxyl (-OH), which facilitate the formation of Fe\({}^{3+}\) complexes [18]. The adsorption of MB dye onto m-(BCA-Mag/AB) can occur through various interactions, including electrostatic interaction, hydrophobic interaction, and \(\pi\)-\(\pi\) stacking. Electrostatic interactions take place between the negatively charged groups on the adsorbent surface and the positively charged groups of the dye [32]. Magnetic nanoparticles are recognized as effective adsorbents due to their large specific surface area and low diffusion resistance. Additionally, hydrogen bonding could occur between the free hydrogen in m-(BCA-Mag/AB) and the nitrogen atoms of the MB dye molecules, representing another potential interaction. Lastly, \(\pi\)-\(\pi\) interaction may occur between the \(\pi\)-electron system of structure and the \(\pi\)-system of the aromatic rings in MB [33].
# Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity
## 4 Experiments
### Exploratory Study  
We conduct extensive experiments to evaluate the performance of components in our proposed method on THUMOS'14. Firstly, we evaluate the capability of Res3D classifier for segments with 16 frames. The segment-level recognition accuracy in the Table 2 shows that our proposed Res3D outperforms the Res3D-16S [19] for a segment with 16 frames. For the binary classifier in the proposal step, we improve the segment-level recognition accuracy from 68.1% to 78.3%. For the multi-class classifier in the classification step, the accuracy is improved from 67.3% to 69.8%.  
In addition, we evaluate our proposed discriminative temporal selective search (DTSS) on the features of the trained Res3D. Sliding window is widely used in action proposal and detection. We compare DTSS with sliding window (SW) and temporal selective search (TSS) on the features of the pre-trained Res3D. We combine these methods with the aforementioned regression network and NMS to form the complete action proposal step and conduct the evaluation of the SW-based, TSS-based, and DTSS-based action proposals. The AR-AN curves of our DTSS-based action proposal are illustrated in Figure 6. The dashed lines are for the recall performance over various tIoU thresholds, while the solid line is the average recall across all tIoU thresholds. For a clear comparison of these three methods, we present the average recall and metric score of the compared methods in Figure 7a and present Recall@1K-tIoU in Figure 7b. These results show that our DTSS-based method performs the best. The SW-based and TSS-based proposal methods only employ window-level classifier. In contrast, our DTSS-based methods employ both of segment-level and window-level classifiers. Thus, these results also support our 'detection from fine to coarse granularity' idea.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifiers** & **Res3D-16D** & **Res3D-16S** & **Our Res3D** \\ \hline Binary Classifier & Not Functional & 68.1 & 78.3 \\ Multi-class Classifier & Not Functional & 67.3 & 69.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Segment-level recognition accuracy (%) of different Res3D versions using RGB input field.  
Figure 6: Average Recall vs. Average Number of Proposal (AR-AN) curves of our discriminative temporal selective search (DTSS)-based proposal.  
In this paper, we provide practices to transfer the knowledge learned by 3D CNN from RGB to optical flow and impose our method on RGB and OF segments. Table 3 presents the results of our action detection using different input fields. The results show that the transfer learning works well and our method benefits from the optical flow field.