esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Energy-Harvesting Performance of The Upstream Harvester
The results indicate that the wake flow field can be divided into two cases based on the value of the spacing ratio: the near wake flow field and far wake flow field. If the downstream harvester is placed in the near wake flow field, it plays a positive role in enhancing the performance of the upstream harvester; otherwise, the effect disappears. Figure 7a,b show the variations of the output power and vortex shedding frequency with the water speed and the spacing ratio, respectively. Figure 7a illustrates that the gradient of the output power is very large along the spacing ratio direction in the low spacing ratio range (\(L/D\leq 3.75\)); that is, the spacing ratio has a great effect on the performance of the upstream harvester. While in a larger spacing ratio range, the effect of the spacing ratio becomes weak because the gradient of the output power is approximately equal to zero along the spacing ratio direction. On the other hand, the water speed always has a great effect on the performance of the upstream harvester because the gradient of the output power is always very great along the water speed direction. Figure 7b illustrates that increasing the water speed results in an increase in the vortex shedding frequency for a specific spacing ratio. With the spacing ratio increasing, however, the vortex shedding frequency remains roughly  
Figure 6: Output powers of the upstream harvester and the single one as a function of the water speed for eight different spacing ratios. stable for a specific water speed. It can be concluded that the vortex shedding frequency depends on the water speed rather the spacing ratio.
# Multimodal Chinese Sarcasm Detection Integrating Audio Attributes and Textual Features
## 3 Methodology
### Feature Extraction Layer  
#### 3.2.1 Text Feature Representation  
In this study, as shown in Figure 2, we employ a pre-trained BERT model to obtain the semantic representation of the input text. The initial step involves encoding the raw text using a tokenizer to produce text vectors \(T_{\text{input}}\), which are compatible with BERT's input requirements. This encoding step is not merely a transformation of the text data but a crucial step in ensuring that the text information can be effectively captured and passed on in multimodal tasks. Since BERT is capable of modeling deep semantic relationships through multiple layers of contextualization, text encoding becomes the foundation for improving sarcasm detection performance.  
\[T_{\text{input}}=\text{Tokenizer}(T) \tag{1}\]  
The text sequence is passed into the pre-trained BERT model, which outputs a three-dimensional tensor called the last hidden state with the shape (batch_size, sequence_length, feature_dim).  
\[\text{last\_hidden\_state}=\text{BERT}(T_{\text{input}}) \tag{2}\]Since the information in this tensor is overly redundant, a dual-pooling operation is applied to better capture the semantic content within the sequence. This process helps to retain the features most related to sarcasm, resulting in the final feature vector:  
\[H_{\text{pooled}}=\text{concat}\left(\frac{1}{n}\sum_{i=1}^{n}H_{i},\max_{i=1}^{ n}H_{i}\right) \tag{3}\]  
Here, \(H_{i}\) represents the hidden state of the \(i\)-th token, and concat denotes the concatenation operation between the pooled vectors.
# A Reconfigurable Formation and Disjoint Hierarchical Routing for Reshapeable Bluetooth Networks
## 6 Scatternet Performance
### Routing Performance
However, the average packet delay eventually stops increasing and saturates because those packets affected by the buffer overflow phenomenon will be dropped. For the same reason, the throughput performance in Figure 11 increases slowly after the packet generation rate increases above three packets/nodes. On the other hand, mesh-tree delay performance saturates when the packet generation rate increases above ten packets/nodes for the \(k=2\), \(3\) and \(4\) cases. This is because the root and other masters in the mesh-subnet finally become bottlenecks when the network traffic is saturated. A packet dropping probability metric is used here to evaluate the effect of scatternet congestion, which causes the buffer overflow phenomenon in some nodes. When the FIFO buffer overflows in a node, the affected routing packets, including both the newly generated routing packets and the currently active routing packets, are dropped. The packet dropping probability is defined as the ratio of total number of dropped packets over the total number of generated packets in all nodes. Figure 13 shows the packet dropping probability of a reconfigurable mesh-tree and BlueHRT. In BlueHRT, the packet dropping probability increases when the packet generation rate exceeds three packets/nodes. It appears that above this rate, the buffer overflow phenomenon becomes serious. This phenomenon also causes new routing packets to be dropped, and deteriorates the throughput performance, as shown in Figure 11.  
Figure 12: Average packet delay of mesh-tree and BlueHRT. Figure 13: Packet dropping probability of mesh-tree and BlueHRT. From the simulation results in Figure 13, the layer = 5 case achieves the worst performance on the packet dropping probability. From the observation of our simulation, the longer the delay packets were dropped and the average packet delay decreases as the packet generation rate is greater than four in Figure 12.  
As a comparison, mesh-tree does not encounter this buffer overflow phenomenon until the packet generation rate exceeds six packets/nodes as the 10% dropping ratio for the \(k=3\) cases, because its traffic load is more balanced due to its mesh-shaped architecture and its hierarchical routing protocol. In general, the mesh-tree topology has a much lower packet dropping probability than BlueHRT.
## 3 Detection Approaches Review
### Stress Analysis and Keystroke Dynamics  
Stress analysis has been addressed in the literature using different sources of information, like in the case of sentiment analysis. In Reference [7] text is used as the source of information for an algorithm that employs a lexicon of stress-related terms for calculating the stress score of a sentence, based on the score of the highest stress term found, with some rules that modify the base approach (e.g., spelling correction or negating stress words). Keystroke dynamics refers to the way un user types at a keyboard and includes different features such as timing features of key press and release and accuracy rate while typing. As an example of keystroke dynamics applied to stress analysis, in Reference [35] authors show through a Wilcoxon Signed Rank test that several keystroke dynamics features reject the null hypothesis that stress and non-stress data does not have a significant difference at least at 90 % confidence level. Another example is Reference [30], where the authors used keystroke dynamics and linguistic features for the analysis of free text, and demonstrated that such techniques can be effectively used to detect cognitive and physical stress from free-text data. The authors claim that the accuracy of detection of cognitive stress was consistent with those obtained using affective computing methods, and that the accuracy for the detection of physical stress, while not being as high as the ones obtained for cognitive stress, still encourages further research. There are works that perform sentiment analysis using a model trained with keystroke dynamics data. In Reference [36], the authors used IADS [37] sounds for inducing sentiments to a series of users and record their keystroke dynamics after hearing them. They showed that the effect of arousal on keystroke duration and keystroke latency was significant but the one on the accuracy rate of keyboard typing was not. The works performed in Reference [35] and Reference [36] demonstrate that there is an effect of stress and sentiment, respectively, on keystroke dynamics data, showing that models can be built to use this kind of data for performing sentiment and stress analysis. Keystroke dynamics data used in Reference [30] to detect stress, and text data used in Reference [7] for this same purpose can be compared in Table 1, showing that the text-based method outperforms the keystroke method when using stress strength detection while allowing the matches to be \(\pm 1\) stress level away of the label, while being outperformed when considering exact stress level matches.  
Related to the inclusion/exclusion criteria, References [35] and [36] are selected for the review because they perform an analysis on the significance of the effect of stress and sentiment on keystroke data, respectively. For reviewing a method for detecting stress, References [7] and [35] were selected, since a method that addressed stress analysis on text data is presented in the former and one addressing stress analysis on keystroke data is shown in the later.
###### Abstract
The advantage of hybrid recommendation lies in its ability to combine the strengths and weaknesses of different recommendation algorithms and applicable scenarios, and then choose the most suitable ratio to fuse multiple algorithm recommendations based on real-time data. Current feature combination methods can be divided into three categories: summation [1], concatenation [24; 25], and weighted fusion [12; 26]. However, these methods mainly consider the overall relationship between independent features, even the latest practical industrial methods. When it comes to the long-short interest model, it ignores the relationship between aspect-level interest and short-term interest in long-term interest features. Therefore, we explicitly partition the long-term interest features and use similarity and contrast loss to compensate for the shortcomings of previous methods. In practical applications, more side information is often introduced to enhance user and item features and reduce the bias of fusion features [27; 28; 29]. However, less research has been conducted on the enhancement of interest features. To address this gap, we specifically designed a novel fusion enhancement module, and the final experimental results demonstrate the effectiveness of our method. In the following text, we will answer the following three main questions:  
1. Why train the long and short interest models separately? 2. How does the integration module correct for interest bias? 3. What is the versatility of our approach? In conclusion, the starting point of this paper is to connect the independent long and short interest models and correct the deviation between each other to improve the performance of the models. The main contributions of this paper are summarized as follows:  
1. A plug-and-play user long and short interest fusion module is designed, which can effectively and quickly fuse long and short interests using the shared attention mechanism, thus improving the model accuracy;The sources of interest bias are analyzed experimentally, and an improved ternary contrast loss function is introduced to accelerate the convergence of the model by using the bias between features as the index of the loss function;  
The effectiveness and generality of our proposed method is demonstrated by combining and experimenting several different long and short interest models on several different domain datasets with data of different sequence lengths as input.
## 5 Discussion
### Limitations and Recommendations  
Although our systematic review successfully analyzed research hotspot evolution and future trends, several limitations must be acknowledged. Primarily, our exclusive reliance on WoS databases restricts the comprehensiveness of our findings. Future studies would benefit from expanding data collection to include Scopus, Google Scholar, and the relevant gray literature, ensuring these additional publications complement our results while broadening analytical perspectives, particularly in specialized technical domains. However, future researchers should carefully adapt search strategies when employing multi-database approaches due to varying syntax requirements across different platforms. Additionally, our sole dependence on CiteSpace (version 6.4.R1 Advanced) introduces methodological constraints, as the findings are inherently shaped by this tool's specific algorithms and functionalities. More robust analyses could emerge from incorporating alternative bibliometric tools or newer versions in subsequent research, potentially yielding more nuanced insights into the field's development trajectory and emerging research directions. Furthermore, future researchers could consider adopting shorter time windows (e.g., 10-year periods) to conduct more focused temporal analyses, which may reveal more granular patterns in research evolution and provide complementary insights to our comprehensive 25-year overview.  
Additionally, our analysis reveals that current research predominantly focuses on three main VRU categories: pedestrians, two-wheeler users (including cyclists, motorcyclists, and scooter users), and visually impaired pedestrians. While these groups represent the most extensively studied populations in the existing literature, with some studies including age-diverse participants, the broader VRU community encompasses additional vulnerable groups that remain underexplored, including wheelchair users and individuals with hearing impairments or other sensory disabilities. Future research should expand the scope of VRU safety studies to investigate the specific safety technology needs of these under-represented groups, developing inclusive safety solutions that address the unique challenges faced by all VRU categories. Furthermore, subsequent studies should investigate the integration of advanced hardware technologies and their impact on user adoption and develop standardized experimental protocols and reporting guidelines to enhance reproducibility across different types of wearable safety technology studies, as these represent valuable research directions for future comprehensive reviews. This expanded focus would contribute to a more comprehensive understanding of VRU safety requirements and enable the development of universally accessible safety technologies.
## 4 KGs Evaluation
### Intrinsic Evaluation
#### 4.1.2 Intrinsic Evaluation: Metrics, Methods, and Case Studies
In the Chinese domain, intrinsic performance was systematically verified by quantitatively evaluating the embedding representation and the effect of negative samples in an entity linking model using the FB15K237, WN18 RR, and SCWS datasets, measured using the Pearson correlation coefficient and F1-Score [168]. Furthermore, a study evaluating the effect of mutation operators introduced several metrics--including average effectiveness, average n-step effectiveness, standard deviation of effectiveness, and execution time--to quantify the algorithm optimization performance [169]. Another research compared the performance of entity-relation extraction using the New York Times, WebNLG, and ADE datasets with Micro-F1 and Macro-F1 metrics [66]. In an air quality prediction task, the KnowAir and UrbanAir (North) datasets were used to quantitatively evaluate prediction errors with RMSE, MAE, and MAPE [170], and a study on patient similarity search based on medical data reported excellent performance in terms of Accuracy and F1-Score using the MIMIC-III and MIMIC-IV datasets [171]. Additionally, research utilizing datasets that include spatiotemporal information employed Mean Reciprocal Rank, Hit@1, and Hit@10 to demonstrate performance improvements exceeding three times that of TransE [172], and another study analyzed the effect of text embeddings and graph neural network layer tuning in multilingual entity alignment using the DBP15K dataset [173]. Studies evaluating the effect of temporal logical rules using the ICEWS and GDELT datasets [174; 175] have provided valuable insights; furthermore, an iterative approach for applying temporal logical rules to improve reasoning performance was proposed in [176]. Finally, research that quantitatively evaluated performance in fields such as legal judgment prediction, link prediction, KG generation, and railway defect detection in the domains of law, biomedicine, agriculture, and railway fault detection further exemplifies intrinsic evaluation [177; 178; 179; 180].
## 4 Discussion  
This study was conducted as a follow-up to the previous study [18]. The number of data points, which was a problem in the previous study, increased from 57 to 212. As a result, it was confirmed that the performance of the model improved, as it was a data-based study. However, despite the increase in the number of data points, the number of 212 samples tends to be rather small considering that this is a data-based study. When there is little data, some problems can arise. For example, in the case of this study, some overfitting may occur even though several methods were used to solve the overfitting problem.  
In general, there is no linear dependence between odor substances and complex odors. Additionally, depending on the combination of the odor substances, it may affect the complex odor, but the effect is different, such as synergy and antagonism, and research is insufficient [16]. For this reason, research is needed to find out the causal relationship, which we tried to find out based on the data.  
As a result of the PDP, in the case of NH\({}_{3}\), VLA, and BTA, as the value increased, the probability of being classified as having no discharge increased, which is the same result as the expert domain. However, in the case of PPA, the result was somewhat contrary to the expert domain that the probability of being classified as no discharge decreases as the value increases. Since the sense of smell is affected not only by chemical effects but also by the physiological state of a person, it is thought that there will be errors in creating a model that explains only the concentration of a substance. In the case of such a negative relationship, it is judged that additional research is needed [6], and when checking the partial dependence calculated value (\(y\)-axis of the PDP isolation plot) of the PPA, the value was found to be small. It is judged that more accurate results can be obtained by securing more data.  
Currently, attempts are being made to reduce complex odors by managing the concentration of each odor substance in actual pig barns, and the concentration of odor substances is measured in real time through sensors in some pig barns. Based on the results of this study, it is expected that complex odors can be managed by designating thresholds for each odor substance. In addition, it is expected that the performance of the model will continuously improve as data is accumulated through sensors in pig barns.
###### Abstract  
SiC/SiC ceramic matrix composites (CMCs) are widely applied in the aerospace and nuclear industries due to their excellent material nature (strength, hardness, and irradiation tolerance) at high-temperature loads. However, high-quality machining cannot be easily realized because of the anisotropic material structure and its properties. In this study, a laser water jet (LWJ) was adopted for CMCs machining. Firstly, the finite element model (FEM) was established describing a representative three-dimensional microstructure including wet yarn, warp yarn, SiC base, and the pyrolytic carbon (PyC) fiber coating. The temperature distribution, as well as its evolution rule on substrate surface under LWJ machining, was analyzed. Moreover, a single-dot ablation test was carried out to verify the accuracy of the numerical simulation model. Secondly, the variation in maximum temperatures under different laser pulse energy was obtained by means of FEM. Nonetheless, a non-negligible deviation emerged in the ablation depth of the numerical calculation and experimental results. Although the simulation results were obviously superior to the experimental results, their proportions of different machining parameters reached an agreement. This phenomenon can be explained by the processing characteristics of LWJ. Finally, single-row and multi-row scribing experiments for CMCs with 3 mm thickness were developed to clarify the processing capacity of LWJ. The experimental results indicated that single-tow scribing has a limiting value at a groove depth of 2461 \(\upmu\)m, while complete cutting off can only be realized by multi-row scribing of LWJ. In addition, the cross-section of CMCs treated by LWJ presented a surface morphology without a recast layer, pulling out of SiC fibers, and delamination. The theoretical and experimental results can offer primary technical support for the high-quality machining of CMCs.  
laser water jet; CMCs; finite element model; scribing experiment; machining capacity Article  
Theoretical and Experimental Investigation on SiC/SiC Ceramic Matrix Composites Machining with Laser Water Jet. _Applied_  
doi:10.3390/app12031214  
Academic Editor [PERSON]  
Received: 17 December 2021  
Accepted: 21 January 2022  
Published: 24 January 2022  
**Published:** **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
## 5 Large Intestine  
Functional bowel disorders (FBD), such as IBS, chronic constipation, and fecal incontinence (FI), affect almost half of the population [118]. Despite the high prevalence rates, the diagnosis of FBDs mostly relies on subjective assessments such as clinical history, physical examination, and regular follow-up. Colonic motor activity is difficult to monitor and evaluate because of its erratic nature and the inaccessibility of the colon. In recent years, the use of MRI and high-resolution manometry to assess colonic motility has become more common. MRI has been used to measure the whole gut or colon transit time by tracing meals or markers [119]. In addition, several studies have shown the feasibility of assessing colonic motor patterns using MRI by employing visual assessments of wall motility, observing or quantifying the changes in the luminal diameter over time, and mapping the motility of the contents [120]. However, such techniques usually require bowel preparation and are prone to yield errors due to non-standardized methods. By measuring the pressures along the colon, high-resolution manometry has been shown to be useful to more accurately monitor the prominent motor patterns in the colon [121]. The improved resolution of recording sites helps to identify propagating motor patterns that are mostly missed by low-resolution manometry recordings. However, manometry performed via intubation of a catheter is an invasive procedure and, in most institutions, involves some form of anesthesia. Both MRI and manometry can provide insights on functional bowel symptoms and diseases by monitoring motor patterns, but the underlying factors still need to be identified.
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 4 Algorithm Design
### Spatio-Temporal Clustering  
This paper combines the spatial location of each logistics distribution point with spatio-temporal clustering of logistics nodes to facilitate path optimization and reduce the running time of the algorithm [27]. Customer clustering is a way to obtain better logistics network configurations and ensure efficient distribution.