esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Performance Evaluation of the Emotion Evaluation Network  
As shown in Step 3 (Training the emotion evaluation network) in Figure 1, a quantitative analysis of emotions was conducted in student spoken audio, and corresponding scores were generated. This process is divided into two main stages: coarse tuning and fine-tuning. In the coarse-tuning stage, the emotion feature encoding network played a crucial role in extracting emotional features from input audio. By comparing the cosine similarity between the IEMOCAP dataset audio, synthesized audio, and student spoken audio, emotion evaluation scores for students spoken audio were generated, which were subsequently used to train the emotion evaluation model. In the fine-tuning stage, the model was further trained using teachers' ratings. Mean squared error (MSE) was used as the primary evaluation metric for this part. Detailed experimental results are available in Figure 6.  
In the coarse-tuning stage (represented by the blue dotted line), the model's mean squared error (MSE) is 2.808. However, in the fine-tuning stage (represented by the orange dotted line), this value decreases to 0.847. As shown in the above graph, during the coarse-tuning stage, the model is in its early training phase, and the loss function decreases rapidly but lacks precision. In contrast, during the fine-tuning stage, the model starts from a lower initial point in terms of loss. Although there may be some fluctuations in loss at the beginning, it demonstrates a stable trend over time.  
The experimental results clearly demonstrate that the emotion evaluation network exhibits accuracy in evaluating emotions in student spoken language. The higher mean  
Figure 5: Box plot of cosine similarity and teacher evaluation scores.  
Figure 6: Line chart of training loss for the emotion evaluation network.  
squared error (MSE) during the coarse-tuning stage may reflect the model's initial understanding of spoken language emotions during the early learning phase. However, with professional guidance from teachers, the MSE significantly decreases during the fine-tuning stage. This strongly indicates that after careful adjustments, the model's evaluation results become increasingly consistent with the assessments made by professional teachers.  
During the inference stage, the evaluation metrics for the four emotion sentiment models obtained by the proposed method are presented in Figure 7. The mean square error (MSE) for each emotion category is as follows: sadness is 0.837, anger is 1.004, neutral is 0.773, and happiness is 1.006. Through validation, it has been confirmed that the emotion evaluation network is effective and reliable in assessing emotions in student spoken language. As the model continues to learn and adapt, it gradually approaches the assessment level of professional teachers, providing a dependable auxiliary tool for spoken language instruction.
## 2 Materials and Methods
### Mechanical Design  
The mechanical design of the SASTs was designed using computer-aided design (CAD) software that is SOLIDWORKS, as shown in Figure 1. The SAST was designed to have a degree of freedom ranging between \(60^{\circ}\) and \(-60^{\circ}\) (east (E)-west (W) direction) to follow the sun's movement throughout the day. The assemblies were aimed to be stable under different weather conditions to obtain accurate data. Furthermore, the actual fabrication of the SASTs was carried out at the IAU FabLab and workshop, where all the needed materials and equipment were available.
## 2 Literature Review
### Project Complexity and Estimation Accuracy  
Project complexity is defined as the property of a project that makes it difficult to understand, foresee, and control its overall behavior, even when reasonably complete information about the project system is available [43]. High-complexity projects often have unclear functional requirements and ambiguous objectives, with work content that requires further development during the project [7; 44]. These inherent uncertainties in complex projects increase the difficulty of accurately predicting cost outcomes.  
The evidence shows that project complexity often affects estimation accuracy. [PERSON] et al. [45] identified project complexity as the most pronounced project characteristic that affects the accuracy of cost estimates. High project complexity--characterized by extended planning horizons, larger budgets, increased technical demands, higher expectations of innovation, and interconnected interfaces--introduces greater uncertainty [15; 46]. As complexity grows, the number of unpredictable variables rises, increasing the likelihood of deviations from initial estimates [47]. In addition, high-complexity projects are often large-scale with long durations, allowing even minor deviations to escalate into major shifts as multiple factors interact and amplify their effects. Optimism bias exacerbates performance evaluation challenges as project planners tend to understate potential risks and overestimate project outcomes, particularly in complex projects [48]. Thus, achieving high estimation accuracy in complex projects remains a significant challenge that requires advanced techniques to account for the multifaceted nature of these projects.  
Empirical studies highlight the significant influence of project complexity on cost outcomes. [PERSON] [13] found that complex projects often encounter substantial cost overruns due to unpredictable factors, and only one in ten large-scale infrastructure projects are delivered on time. Similarly, [PERSON] and [PERSON] [16] demonstrated a strong correlation between project complexity and the reliability of cost estimation. In addition, [PERSON] et al. [49] revealed that structural complexity particularly reduces cost estimation accuracy in supply chain design as it introduces more conflicting goals. These findings underscore the importance of incorporating complexity measures to better capture the high uncertainty associated with high complexity.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 4 Experiments
### Results on the COCO Dataset  
The experimental results from the COCO val2017 dataset are shown in Table 2. Our proposed method achieves better or comparable prediction accuracy compared to state-of-the-art methods. In the case of an input image size of 256 \(\times\) 192, our method achieves an AP score of 78.3. Compared to the state-of-the-art ViTPose-L [6] method, our method achieves similar prediction accuracy with only one tenth of the parameters and one eighth of the FLOPs. Similarly, compared to the PCT [7] (Base) method, which utilizes structured representations, RHPNet demonstrates a growth of 0.6 AP. Compared to PCT (Large), RHPNet achieves comparable accuracy and outperforms it in terms of the AP50 metric. Figure 7 showcases the detection results for RHPNet on the COCO val2017 dataset.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Method** & **Backbone** & **Input Size** & **Params** & **GFLOPs** & **AP** & **AP\({}^{50}\)** & **AP\({}^{75}\)** & **AP\({}^{\text{M}}\)** & **AP\({}^{\text{L}}\)** & **AR** \\ \hline Simple Baseline [3] & ResNet-152 & 256 \(\times\) 192 & 68.6 M & 15.7 & 72.0 & 89.3 & 79.8 & 68.7 & 78.9 & 77.8 \\ Simple Baseline [3] & ResNet-152 & 384 \(\times\) 288 & 68.6 M & 35.6 & 74.3 & 89.6 & 81.1 & 70.5 & 79.7 & 79.7 \\ HRNet-W32 [4] & HRNet-W32 & 256 \(\times\) 192 & 28.5 M & 7.10 & 74.4 & 90.5 & 81.9 & 70.8 & 81.0 & 79.8 \\ HRNet-W32 [4] & HRNet-W32 & 384 \(\times\) 288 & 28.5 M & 16.0 & 75.8 & 90.6 & 82.7 & 71.9 & 82.8 & 81.0 \\ HRNet-W48 [4] & HRNet-W48 & 256 \(\times\) 192 & 63.6 M & 14.6 & 75.1 & 90.6 & 82.2 & 71.5 & 81.8 & 80.4 \\ HRNet-W48 [4] & HRNet-W48 & 384 \(\times\) 288 & 63.6 M & 32.9 & 76.3 & 90.8 & 82.9 & 72.3 & 83.4 & 81.2 \\ UDP-Pose [24] & HRNet-W48 & 384 \(\times\) 288 & 63.6 M & 32.9 & 77.8 & 92.0 & 84.3 & 74.2 & 84.5 & 82.5 \\ DarkPose [29] & HRNet-W48 & 384 \(\times\) 288 & 63.6 M & 33.0 & 76.8 & 90.6 & 83.2 & 72.8 & 84.0 & 81.7 \\ TransPose [30] & HRNet-W48 & 256 \(\times\) 192 & 18 M & 21.8 & 75.8 & 90.1 & 82.1 & - & - & 80.8 \\ TokenPose [31] & HRNet-W48 & 256 \(\times\) 192 & 28 M & 22.1 & 75.8 & 90.3 & 82.5 & - & - & 80.9 \\ HRFormer-B [32] & HRFormer-B & 256 \(\times\) 192 & 43 M & - & 75.6 & - & - & - & - & 80.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results on the COCO val2017 sets. The best results from the cited papers are reported.  
Figure 6: Qualitative evaluation of RHPNet vs. OmniPose and ViTpose-B on the Running Human dataset.
## 3 Problem Formulation
### Power Consumption  
The total power dissipated in the system, encompassing \(K\) users, comprises various components, including the base station transmit power (\(p_{t}\)), hardware static power at the base station (\(P_{BS}\)), power consumed by the RIS relay (\(P_{M}\)), and power consumption at the user equipment (\(P_{VU}\)). With these components considered, the total power operating on the RIS-assisted wireless network can be defined as follows  
\[\mathcal{P}_{total}(t)=\sum_{k=1}^{K}(\xi p_{t}(t)+P_{VU}(t))+P_{BS}(t)+P_{R}(t), \tag{7}\]  
where \(\xi\cong\
u\) with \(\
u\) to evaluate the ability to effectively convert input electrical power into output radio frequency (RF) power by the power amplifier.  
Considering (7) as the denominator of the energy efficiency (EE) function, then the EE performance \(\eta_{EE}\cong(B\cdot\mathcal{R})/\mathcal{P}_{total}\), with \(B\) presenting the bandwidth, can be obtained using (6) and (7) as  
\[\eta_{EE}(t)=\frac{B\sum_{k=1}^{K}log_{2}(1+\gamma_{k}(t))}{sum_{k=1}^{K}(\xi p _{t}(t)+P_{VU}(t))+P_{BS}(t)+P_{R}(t)}, \tag{8}\]
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 3 Proposed Fingerprinting System
### Proposed Fingerprinting Strategies
#### 3.2.1 Net  
The _Net_ strategy uses the number of cells that two locations have in common, without taking into account any RF parameter, as the similarity metric between the two locations. Subsequently, NB-IoT cells are denoted as NPCIs in order to comply with NB-IoT standard terminology. The distance between the \(i\)-th RP and the location of a target device \(j\) to be estimated, i.e., the \(j\)-th test point (TP), is thus defined as follows:  
\[d_{Net}(i,j)=\frac{1}{L_{i,j}}, \tag{2}\]  
where \(L_{i,j}\leq L_{j}\) is the number of NPCIs in common between RP \(i\) and TP \(j\), and \(L_{j}\) is the number of NPCIs detected in TP \(j\). Given the definition in (2), the distance takes values in the discrete set \(\left\{d_{1}=1/L_{j},\;d_{2}=1/(L_{j}-1),\cdots,\;d_{L_{j}}=1\right\}\); as a result, sorting RPs based on increasing distance from TP \(j\) divides RPs in \(L_{j}\) groups associated with the distances in the set. Let \(G_{m}\) indicate the group of RPs at distance \(d_{m}\) (\(m=1,\cdots,L_{j}\)), which is defined as follows:  
\[G_{m}=\{i|d_{Net}(i,j)=d_{m}\}\qquad m=1,\cdots,L_{j}, \tag{3}\]  
with \(N_{m}=|G_{m}|\geq 0\) being the corresponding group cardinality. The \(k\) selected RPs for estimating the position of TP \(j\) thus include elements of the first \(q\) groups, with \(q\) such that  
\[\sum_{m=1}^{q}N_{m}\triangleq N_{g}\leq k<\sum_{m=1}^{q+1}N_{m}, \tag{4}\]  
and \(k-N_{g}\) RPs randomly selected within the group \(G_{q+1}\).
# Synthesis of Mesoporous MWCNT/HKUST-1 Composite for Wastewater Treatment
## 3 Results and Discussion  
### Microstructure and Grain Size Evolution  
Figure 1A shows an SEM image of as-received MWCNT powder, while Figure 1B shows an SEM micrograph of the functionalized MWCNTs (FMWCNTs) used to synthesis the MOF-CNT composites in this work.  
The high-magnification SEM image of the as-received MWCNTs reveals that they were in bundle form. It can be observed that the acid-treated MWCNTs did not show structural damage, which could have been due to the mild conditions of the HNO\({}_{3}\) used. The SEM images reveal a uniform diameter  
Figure 1: **(A)** As-received multiwalled carbon nanotubes (MWCNTs); **(B)** mill HNO\({}_{3}\)-functionalized MWCNTs (FMWCNTs).  
distribution and high purity for both the as-received MWCNTs and the functionalized MWCNTs. Figure 2 shows an SEM micrograph/EDS of the as-synthesized HKUST-1 MOF.  
The high-magnification SEM image reveals that the HKUST-1 produced had a well-defined octahedral crystal with average diameter ranges from 6.5 to 8 \(\upmu\)m with a smooth surface. It can be observed that the method used to produce the HKUST-1 MOF in this work yielded a high-purity product with a cubic shape. This could have served as a basis for the excellent textural properties obtained. The EDS elemental composition revealed only C, Cu, and O. Previous studies have shown that HKUST-1 synthesized at low temperatures usually results in crystals with a cubic shape and sharp edges [13; 35; 38]. [PERSON] et al. [39] reported a needle-like structure for Cu-BTC prepared by refluxing DMF/water or ethanol/water solvents under atmospheric conditions. The crystallinity of Cu-BTC synthesized at atmospheric conditions was found to be much lower than that of a hydrothermal reaction. However, Cu-BTC synthesized at 150 \({}^{\circ}\)C and agitated for 5 h in a hydrothermal reaction revealed spherical crystals as well as irregularly shaped particles together with some spherulitic impurity. Figure 3 shows an SEM image of the FMWCNT/HKUST-1 sample. The typical crystals of HKUST-1 were observed on the micrograph. This shows that the carbon nanotube did not prevent or damage the formation of HKUST-1 MOF crystals [9]. The energy dispersive spectroscopy (EDS) of FMWCNT/HKUST-1 composite revealed the presence of carbon, copper and oxygen.  
Figure 2: (**A**) SEM micrograph of HKUST-1 showing the particle shapes; (**B**) high-resolution SEM micrograph of the HKUST-1 MOF; (**C**) EDS analysis of the HKUST-1 MOF, showing the elemental composition.
## 5 Data Analysis and Results
### Moderation Effects
Table 12 presents the statistical significance of the differences between each pair of groups for previous experience. The three groups (i.e., low, good, and excellent) were statistically different from one another. This section presented the moderation effects reflected in H9, H10, H11, and H12. The hypothesis H10 pertaining to gender was validated using a \(t\)-test. Additionally, ANOVA analysis and multiple comparison analysis validated the influence of age (H9), education level (H11), and previous experience (H12) on information adoption (IA).
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 5 Analyzing the Selected Features
### Number of People in Household  
It is universally acknowledged that the number of people living in a household (NPE) impacts energy consumption, as evidenced in the analysis across three clustering algorithms (Figure 13).  
In the low-consumption profile, approximately 70% of dwellings house up to three individuals. This proportion significantly decreases in the medium- and high-consumption profiles. Notably, dwellings with a single occupant are almost exclusively found in the low-consumption profile, with their presence in the medium and high profiles being minimal or non-existent.  
Figure 12: Cumulative bar chart of the DHW percentage in each consumption profile. (a) K-means, (b) AHC, (c) SOM.  
Figure 13: Cumulative bar chart of the NPE percentage in each consumption profile. (a) K-means, (b) AHC, (c) SOM.  
Dwellings with two occupants also constitute a minority in the medium profile, accounting for around 20%, and this figure diminishes to nearly 10% in the high-consumption profile. In contrast, the high-consumption profile predominantly comprises households with four or more occupants.  
Therefore, it can be inferred that dwellings with up to two occupants in the medium-consumption profile and those with up to three occupants in the high-consumption profile are outliers or 'out-profiled' households, deviating from the typical occupancy patterns of their respective consumption categories.
# Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives
## 1 Introduction
In order to explore the potential of spectroscopic PA imaging systems, this review presents a summary of spectroscopic PA imaging studies performed by different research groups. From the literature, it was found that PA imaging can be used to monitor the efficacy of anti-cancer treatments, such as chemotherapy or molecular targeting therapies, through changes in the PA images of tumor-related blood vessels. Few studies reported the potential of PA imaging for breast cancer treatment monitoring and these reported results are summarized in this review. Along with providing a detail comparative analysis of the system parameters of the different PA imaging systems used by the different research groups until 2019, this review attempts to present the clinical evidence in support of using PA imaging for breast cancer detection. Highlights of this review are summarized in Table 1.  
This review paper is organized in the following manner: 'Methods' describes the list of various databases for the literature search, the search keywords and search period. 'Results' summarizes the results of the search mentioned in Methods section, and it provides a comprehensive overview of the research work performed by different research groups towards the utilization of PA imaging technology for the management of breast cancer. It is divided into four subsections containing the details of phantom studies, animal studies, ex vivo and in vivo studies with human patients, while animal studies are further divided into groups containing the results of studies with endogenous and exogenous contrast agents (including imaging of SLNs). 'Discussion' compares the configuration details as well as system parameters of different PA imaging systems developed by various research groups. It also provides a detailed discussion on the potential of the clinical use of PA imaging technology for breast cancer detection relative to the conventional imaging modalities, like US imaging, X-ray mammogram, and MRI, based on the results of in vivo studies conducted so far. The last section, 'Conclusion', highlights future research work, required for the translation of the technology from the laboratory environment to clinical practice. \begin{table}
\begin{tabular}{l} \hline Based on the study results reported by various research groups beginning from 1997 to 2019, this review provides: \\ \(\bullet\)  A complete picture of the evolution of photoacoustic (PA) imaging for breast cancer management. \\ \(\bullet\)  A detailed comparative analysis of PA imaging with other conventional imaging techniques used for breast cancer detection. \\ \(\bullet\)  Answers to the issues related to the role and utility of PA imaging technology for the detection and diagnosis of breast cancer. \\ \(\bullet\)  The utility of spectroscopic PA imaging studies to differentiate between different breast pathologies. \\ \(\bullet\)  The potential of PA imaging technology for breast cancer treatment monitoring. \\ \hline \end{tabular}
\end{table} 
Table 1: Highlights of the presented review.
## 1 Introduction
### _Clustering interference_  
After the implementation of power allocation, each sink obtains an appropriate transmission power for broadcast operations. Sensor nodes may receive the broadcast packets from more than one \"one-hop\" sink. These nodes need to decide which sink should to be chosen as centric sink. At this time, clustering interference is taking place.  
In this paper, we analyze an efficient multiple sink transmission power control scheme for a sink-centric cluster routing protocol in multiple sink wireless sensor networks. All sinks in the network know their location, and at the same time other sink nodes share their location information. Then every sink decides its communication radius by an absolutely distributed algorithm that uses the location information of the other sink nodes.  
The rest of the paper is organized as follows. A summary of related work is presented in Section 2. Section 3 describes the system model of the MSCWSNs-PC protocol. Section 4 describes the design of the MSCWSNs-PC protocol in detail. The performance of MSCWSNs-PC is evaluated in Section 5 and  
Figure 1: Sink-centric multiple sink cluster WSNs. (a) Sink as cluster head. (b) Non-sink as cluster head.  
compared with its improved versions using simulation. The paper concludes in Section 6 and some possible improvements to MSCWSNs-PC are pointed out.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 5 Discussion and Future Research Topics  
As discussed in previous sections, due to UWSNs' particularities and constraints, mature technologies and systems cannot be directly utilized in them. Even though some security technologies have been investigated to secure UWANs, but almost of them are still in the theory stage, and empirical research is rare. Moreover, few proposals addressed several security issues systematically. UWSNs security research is still in its early stage, and moreover, several important issues have not been solved adequately.  
In addition, network security is a complex cross-layer issue, and an optimal utilization of various security technologies among these layers is most important to minimize extra resource consumption, especially for severe resource-constrained UWSNs.  
As discussed above, UWSNs are deployed underwater, and it is difficult to protect each underwater node and detect compromised nodes. The security schemes are pre-installed in the sensor nodes. If the node is compromised and rejected in the network, it will cause more severe damage. In this case, it is necessary to reconfigure security systems periodically, while the design and realization of such reconfigureing systems is also a challenge and important issue.