esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Frequency-Selective Output Constraint Algorithm for Active Vibration Control
## 5 Conclusions and Discussions  
This paper proposed a modified rescaling algorithm to implement a frequency-selective output constraint control for active vibration control. It not only retains the features of accuracy and flexibility of the original rescaling algorithm but also has new advantages and features such as having less distortion and harmonics, being suited for individual sub-frequency components' amplitude constraint and optimal control power allocating, etc. Thus, the proposed algorithm is very practical for AVC applications in order to improve safety and optimal control when the limits of actuators have to be taken into account. Simulations conducted with single-frequency and multi-frequency cases demonstrated its effectiveness, accuracy, and flexibility.  
Although the algorithm presented in this paper is well studied, there are still some areas open to further research. There are some other projection algorithms such as the affine-scaling algorithm can be used to replace the rescaling method in order to accelerate the convergence rate. Correspondingly, in narrow-band vibration applications, those algorithms could be further studied. Furthermore, in some applications, it may be necessary to dynamically adjust the constraint values at some target frequencies rather than constant constraints. The impact of dynamic constraint values on the stability of the NFXLMS algorithm should be studied next.  
Investigation, X.M. and Z.C.; Validation, Z.C.; Writing--original draft, X.M.; Writing--review & editing, X.M. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All data included in this study are available upon request by contact with the author [PERSON].  
The authors declare no conflict of interest.
## 3 Experimental Setup
### Software Implementation
#### 3.2.2 Classification and Prediction Process  
The input data are classified into \(k\) clusters, where \(k\) is determined using the elbow method. Each incoming data point is assigned to the most relevant cluster based on feature similarity. Once categorized, the appropriate pre-trained LSTM model, optimized for each cluster, is used to predict power consumption.  
The prediction interval is set to 15 min, consistent with the electricity billing cycle used by KEPCO. Predictions are made 5 min in advance to allow time for real-time energy management decisions. For instance, the model predicts power consumption for noon at 11:55, giving the system ample time to optimize energy allocation and implement any necessary demand-side management actions.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 4 Methodology  
In this review, we aimed to search for IoT-based healthcare systems that use privacy-preserving techniques, as shown in Figure 4. To gather related studies, we performed a structured search on several well-known academic databases, including IEEE Xplore, PubMed, Scopus, SpringerLink, and Elsevier ScienceDirect. Several keyword combinations were used during our search, such as the following:* (\"IoT\" OR \"Internet of Things\") AND (\"Healthcare\" OR \"Medical\") AND (\"Privacy\" OR \"Security\" OR \"Confidentiality\").
* (\"Data Privacy\" OR \"Privacy-Preserving\") AND (\"IoT Security\" OR \"Healthcare Data Security\").
* (\"Federated Learning\" OR \"Homomorphic Encryption\" OR \"Blockchain\") AND (\"Healthcare Applications\").  
After the search process, we initially gathered 80 research papers, focusing on studies published between 2020 and 2024. However, some highly cited articles from 2018 and 2019 were included, as they provided essential background knowledge on privacy-preserving techniques. Moreover, we followed a three-step process to improve the selection: removing duplicate papers, reviewing titles and abstracts for relevance, and conducting an in-depth full-text analysis. After this process, we reduced our focus to 58 studies that aligned closely with our research objectives and classified the selected studies according to their proposed privacy-preserving techniques, such as blockchains, federated learning, and homomorphic encryption. We also reviewed their key contributions, including novel frameworks and approaches to Secure Multi-Party Computation. In addition, we considered the methodologies used in these studies, including whether they involved simulations, real-world implementations, or theoretical analysis. A significant part of our evaluation also looked at the challenges related to using these privacy-preserving techniques, such as scalability concerns, computational demands, and risks related to data sharing.  
To present our findings clearly, we have used a table to summarize different privacy-preserving techniques and their applications. A framework was used to group the studies based on the privacy-preserving techniques they investigated, allowing us to identify key trends, highlight research gaps, and outline potential future directions in this field.  
Figure 4: Methodology workflow.
# Historically Based Room Acoustic Analysis and Auralization of a Church in the 1470s
## 3 Digital and Historical Reconstruction
### Model Creation
#### 3.1.3 The Monks' choir  
The monks were located in the choir behind the chancel, in the apse in the west of the church. (left side, Figure 2). As the ground level in this area is about 1 m below the nave, a wooden platform has been suggested to bring the monks to an elevation more liturgically favorable [36]. Possible beam holes and records of the removal of wooden elements from this location support this theory, and it is adopted in this reconstruction.  
The shape and size of the wooden platform were determined based on physical traces in the church. Beam holes, book niches, professionals, and a door opening prescribed the solution shown in Figure 5. choir stalls were based on duplicates of two choir stalls which remain today.  
Similar to the nuns' gallery, this space served the purpose of housing a monastical congregation. Some differences with the nuns' gallery are noted. First, the distance between monks and the vaulted ceiling was significantly larger than that between nuns and the ceiling. Second, the monks' choir was in a recessed position nestled in the apse behind the chancel. It is thus enclosed by close, hard walls on three of four sides, which may affect the sound field within the choir to the point where it differed significantly from that in the gallery and the nave. The acoustic characteristics of this space, especially as compared to the gallery, are further investigated in Section 4.1.  
Figure 4: 3D model of the nuns’ gallery (other wave elements mentioned in the text are not visualized here), showing the entrance niche in the northern wall, leading through a crossing to the gallery. The panels on the shorter sides have small windows for visual access. The gallery is supported from beneath by four brick pillars. View from south to north.
## 3 System Overview  
For better intersection safety, a dynamic all-red signal control is necessary to avoid collisions due to sudden appearances of RLRs. To address the issues with conventional fixed signal extension approach, the proposed system identifies first which incoming vehicles are likely to be RLRs and then utilizes the driving characteristics of the detected RLR to adjust the length of the all-red signal accordingly. Hence, the proposed system improves the overall safety as well as efficiency of intersection traffic.  
Figure 1 shows the overall architecture of the proposed dynamic all-red signal control system. The first step of the process begins with traffic data collection from the intersection traffic environment. Traffic data to be collected includes traffic signal as well as all incoming vehicles' movement data such as each vehicle's speed, acceleration, distance to the intersection (DTI) and headway during a certain time duration. Note that, for the purpose of all-red signal length control, the system requires traffic data measured while the traffic signal is in the yellow state. The next step of the process is to identify which incoming vehicles are likely to be RLR. As shown in Figure 1, we use the MC-DCNN classifier for this purpose. The proposed MC-DCNN classifier classifies not only whether an incoming vehicle is likely to be an RLR or not but it also classifies into several different types of RLR based on the vehicle's driving characteristics if the vehicle is likely to be an RLR. Then the last step of the process is to determine the length of all-red signal extension based on the detailed classification result from the MC-DCNN. For this step, we use a multi-level regression approach consisting of the Hougen-Watson nonlinear regression and a quadratic polynomial fitting to determine the necessary all-red signal extension time. More details on each of the steps in the process are covered in the following sections.
## 5 Results  
### Experimental Results  
Figure 7 shows the transmissibility obtained from processing the measured acceleration data described in the experimental section. The data is presented for four body segments: head, trunk, thigh, and leg. The results showed that the upper trunk and head segments have the highest magnitude of transmitted vibrations, both at 0.5 and 2.0 m/s\({}^{2}\) rms. The thigh and calf on the other hand are less susceptible to the transmitted vibrations.  
Figure 6: Hybrid optimization method (HOM) flowchart.  
Figure 7: Transmissibility obtained from experiments at 0.5 and 2.0 m/s\({}^{2}\) rms forcing magnitudes.  
Furthermore, at a forcing amplitude of 0.5 m/s\({}^{2}\) rms, all body segments exhibit a first principal resonant frequency in the range of 4-6 Hz, while the second principal resonance frequency is prominent in the head, thigh, and calf regions, within the range of 8-15 Hz. Our experimental approach confirms that the that the peak resonance fre-quencies of human response to vibration in the vertical direction are in the low-frequency region and the resonant frequencies obtained for each of the head, trunk, thigh and calf are in the region reported in literature [29; 30; 31].  
Additionally, a non-linear response of the human body to an increase in excitation magnitude was observed for all the body segments. The'softening effect' was observed as the excitation magnitude increased from 0.5 to 2.0 m/s\({}^{2}\) rms, indicating the strong non-linearity of human body biodynamics. Also, the peak magnitude of transmissibility at each body segment reduced, and the resonance frequency became less observable and diminished in magnitude as the vibration exposure time increased.
# The Impact of Cell Phone Usage on Fall Risk and Postural Control During Free-Standing
## 4 Discussion
The lower SampEn values during phone use indicate a loss of adaptability in postural control, heightening the risk of falls under dynamic conditions. Similarly, LyE, which quantifies the divergence of nearby trajectories in a dynamic system, reflected the variability in postural adjustments during static conditions, exhibiting significantly higher values in the AP direction for the EO trial compared to both EP and SMS trials. This suggests that, even under static conditions, the postural control system involves dynamic processes that can be assessed using LyE to capture the stability and adaptability of balance. This finding suggests that postural control in the AP direction becomes more chaotic and less stable during phone use, particularly when texting. The decrease in LyE during the SMS trial in the ML direction may reflect a reduced sensitivity to small postural disturbances, signaling a diminished ability to respond to shifts in balance. Finally, the fact that nonlinear parameters consistently revealed significant differences, whereas linear measures showed less sensitivity, supports the hypothesis that mobile phone use interferes with more subtle aspects of postural control--issues that might not be captured by simpler linear measures like CoP path length. The analysis of limb involvement during phone use revealed clear patterns based on the task at hand. During static conditions, most participants used one hand to hold the phone, while texting necessitated the use of both hands. This shift was even more pronounced under dynamic conditions, with nearly all participants using two hands to text. The connection between limb involvement and postural stability is particularly relevant, as asymmetric weight distribution and restricted arm movement can influence balance and postural control [35; 36]. The preference for right-hand dominance, especially in one-handed phone use, aligns with the participants' self-reported hand dominance, indicating that this factor may influence how phone use impacts postural control. This connection to postural control is especially important in dynamic conditions, where the ability to make balance adjustments is crucial. The reliance on both hands for texting limits compensatory arm movements, potentially increasing instability. Moreover, holding the phone with two hands restricts the natural sway of the upper limbs, which normally assists in maintaining balance. Interestingly, the increased reliance on both hands for texting may have contributed to greater postural instability in the SMS trial. Holding the phone with two hands, as opposed to one, likely shifts the body's center of mass and requires additional motor coordination, which may have compromised balance during the texting task.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 2 Methods of On-Board Ship Detection
### Ship Detection Based on the YOLOv4-Tiny Network
#### 2.3.2 Loss Function  
The loss function of YOLOv4-tiny contains three parts and can be expressed as follows:  
\[loss=lbox+lobj+lcls, \tag{9}\]  
where, _lbox_, _lobj_, and _lcls_ are confidence bounding box regression loss, classification loss, and confidence loss functions, respectively.  
YOLOv4-tiny uses complete intersection over union (CIoU) loss instead of Mean Square Error (MSE) loss, which takes the three geometric factors of the two detection boxes into account, namely their overlapping area, the distance between their center points, and their aspect ratios. Accordingly, the bounding box regression loss function is:  
\[lbox=1-IoU+\frac{\rho^{2}(b,b^{st})}{c^{2}}+\alpha\cdot v, \tag{10}\]  
\[\alpha=\frac{v}{(1-IoU)+v}, \tag{11}\]  
\[v=\frac{4}{\pi^{2}}(\arctan\frac{w^{st}}{h^{st}}-\arctan\frac{w}{h})^{2}, \tag{12}\]  
where, _IoU_ is the intersection over union between the truth bounding box and predicted bounding box; \(b\) is the coordinate of the center point of the predicted bounding box; _bst_ is the center point coordinates of the real bounding box; _p_(\(\cdot\)) is the calculation of Euclidean distance; \(c\) is the diagonal distance of the box that can contain the predicted and truth bounding boxes; _wst_ and _hsst_ are the width and height of the truth bounding box; and, \(w\) and \(h\) are the width and height of the predicted bounding box.  
The confidence loss function is:  
\[\begin{array}{l}lobj=-\sum\limits_{i=0}^{S\times S}\sum\limits_{j=0}^{B}I_{ ij}^{obj}[C_{i}\log(\hat{C}_{i})+(1-C_{i})\log(1-\hat{C}_{i})]-\\ \sum\limits_{i=0}^{S\times S}\sum\limits_{j=0}^{B}\left(1-I_{ij}^{obj}\right)[ C_{i}\log(\hat{C}_{i})+(1-C_{i})\log(1-\hat{C}_{i})],\end{array} \tag{13}\]  
where, _S_\(\times\)_S is the grid size; \(B\) is the number of bounding boxes in a grid; _lij_ is a function of the object, if the _j_th bounding box of the _i_th grid is responsible for detecting the current  
Figure 4: YOLOv4-tiny network architecture.  
object, \(I_{ij}^{obj}=1\), otherwise \(I_{ij}^{obj}=0\); \(\hat{C}_{i}\) and \(C_{i}\) are the confidence scores of the predicted and truth boxes, respectively.  
The classification loss function is:  
\[lcls=-\sum_{i=0}^{S\times 5}\sum_{j=0}^{B}I_{ij}^{obj}\sum_{c\in classes}[p_{i}(c )\log(\hat{p}_{i}(c))+(1-p_{i}(c))\log(1-\hat{p}_{i}(c))], \tag{14}\]  
where, \(\hat{p}_{i}(c)\) and \(p_{i}(c)\) are the predicted probability and true probability that the object belongs to \(c\) classification in the \(j\)th bounding box of the \(i\)th grid.  
The detection results will produce a large number of overlapping detection bounding boxes. Based on traditional Non-Maximum Suppression (NMS), distance intersection over union non-maximum suppression (DIoU-NMS) is used to resolve problems when the bounding boxes do not overlap, using the distances between the center points of the bounding boxes to do so. We chose 0.4 as the threshold for DIoU-NMS in this article.
## 1 Introduction
Secondly, a case study was conducted to evaluate the usability of immersive technologies in industrial design, using the 'Gravity Sketch' software in combination with the 'Meta Quest 3' headset. The research followed an embedded single-case-study design, comprising seven Design Units that served as embedded subunits of analysis. Usability was assessed based on the ISO 9241-11 standard [65], which defines usability through the dimensions of effectiveness, efficiency, and satisfaction. The following nine usability indicators were included: tasks completed, time to complete tasks, dimensional accuracy, interoperability, interactivity, fatigue, human error, learning curve, and perceived creativity. This comparative analysis allowed an assessment of the effectiveness, efficiency, and satisfaction associated with the use of VR and AR technologies in product sketching, modelling, and visualisation tasks. This study addresses several of the limitations identified in the recent scientific literature. In contrast to previous works, it is not restricted to the analysis of a single immersive technology but instead provides an integrated comparison of VR, AR, and MR. This enables a assessment of their applicability in various scenarios within the design process. It also includes a current market analysis to support the selection of tools representative of the professional context of industrial design, encompassing both hardware and software resources. From a methodological perspective, the study proposes a structured and comprehensive set of indicators for effectiveness, efficiency, and satisfaction, aligned with the ISO 9241-11 standard and specifically adapted to the product development workflow; this approach is suitable for evaluating the performance of immersive technologies in real industrial design tasks. The article is structured as follows: Section 2 presents the methods used in the research; Section 3 describes the current state of immersive technologies in industrial design; Section 4 presents the results of the usability assessment of immersive technologies in the design process; Section 5 discusses the results obtained; and Section 6 presents the main conclusions of the work.
# Workflows for Rapid Functional Annotation of Diverse Arthropod Genomes
## 3 Results
### Results and Discussion
#### 3.3.3 Pathway Annotation  
High throughput sequencing has enabled the profiling of longitudinal transcriptional response at the organismal, tissue, and single-cell level in addition to multiple life stages and conditions. Although GO terms are highly effective at deducing the changes in gene expression, pathway-level perturbations provide valuable biological insight for the interpretation of functional genomics data sets and are critical for integrating proteome and metabolome data sets to understand phenotypes. Therefore, we were also interested in automatically reconstruct metabolic pathways from the proteomes from a range of arthropod genomes.  
Pathways data is provided by resources, such as KEGG [46], Reactome [47], and BioCyc [48], and as we developed our workflow, we selected KEGG pathways for our workflow because it supports the most extensive set of invertebrate species, and the KOBAS tool is freely available [26]. In our initial tests using the KOBAS tool to annotate pathways, we determined that comparing the arthropod proteome sets against the KEGG _Drosophila melanogaster_ ('dme') provide the most comprehensive results, and this well-studied arthropod species also has the broadest set of functional information based on experimental validation, including pathways.  
Not surprisingly, _A. mellifera_ and _T. castaneum_ references had similar proportions of proteins assigned to pathways, although a slightly lower number of proteins per pathway than \(D\). _melanogaster_ (Table 6). The reference species had about one-third of proteins assigned to pathways, and most of the test species were annotated to the same degree or better. Curiously, several species did substantially better than the reference set: _V. destructor_, _A. rosae_, _D. citri_, and _L. lunatus_ all had about 40% of proteins assigned to pathways, and a similar effect was seen for the GO annotation in these species. We note that most of these species have well-assembled genomes with high contig and scaffold N50 and BUSCO completeness scores. The average number of proteins per pathway scaled with the genome contiguity and BUSCO duplication rate, suggesting that the higher gene copy number accounts for this variance (Figures S1 and S2).