esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 5 Experiments' Setup
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Total Page Size** & **Single-Object Web Page** & **Uniform Multi-Objects Web Page** & **Triangular Multi-Object Webs Page** \\ \hline
100 K bytes & One object with size 100 K bytes & 12.5 k bytes for all objects & 3 k, 8 k, 14 k, 20 k, 22 k, 16 k, 11 k and 6 k bytes \\
500 K bytes & One object with size 500 K bytes & 62.5 k bytes for all objects & 20 k, 40 k, 70 k, 100 k, 110 k, 80 k, 50 k \\  & & & and 30 k bytes \\
1 M bytes & One object with size 1 M bytes & 125 k bytes for all objects & 5 k, 50 k, 135 k, 230 k, 270 k, 200 k, \\  & & & 100 k and 10 k bytes \\
2 M bytes & One object with size 2 M bytes & 250 k bytes for all objects & 10 k, 100 k, 270 k, 450 k, 540 k, 400 k, \\  & & & 200 k and 20 k bytes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The size and organization of objects in each web page structure. page structures with varying sizes and organization. In this paper, we report the results of three main experiments that were designed to examine specific aspects of the protocol stack's configuration. The first experiment studies the behavior of parallel streaming in HTTP/2 and HTTP/3 protocols in a satellite link. The second experiment examines the effect of Cubic and BBR congestion control algorithms on HTTP while the third one focuses on the effect of the web page structure on HTTP/2 and HTTP/3 in a satellite link. In all cases, we set all links' speeds to 10 Mbps and the satellite link RTT to 500 ms. Furthermore, the same scenarios were conducted over a direct LAN link between the client and server in order to provide some baseline for our evaluation. All experiments are summarized in Table 4. It should be noted here that one must take careful consideration of the default operating behavior of the web client and server, and their host computers to ensure isolated, controlled experimentation of HTTP protocols. Issues of web browser caching, server disk delays, network ARP delays and DNS lookup delays need to be isolated first before recording any statistics.
## 3 Results and Discussions
### Research on the Relationship between the Extent of Damage Intensity Index and Mining Impacts
#### 3.2.2 Dependence of Damage Intensity of Large-Panel Buildings on Mining Impacts  
The results presented in Table 6 show significant correlations between horizontal ground deformations \(\varepsilon_{max}\) and indices:  
* Intensity of damage to load-bearing walls of the basement or to foundation walls \(w_{u2}\),
* Intensity of damage to interior and exterior overground load-bearing walls (including lintels and walls under windows) \(w_{u3}\),
* Intensity of damage to partition walls \(w_{u11}\),
* Intensity of damage to floors (floor layers) \(w_{u13}\),
* Intensity of damage to flashings, gutters and downpipes \(w_{u20}\).  
The results of the correlation study between the damage indices \(w_{ui}\) and the maximum horizontal components of vibration acceleration \(PGA_{H}\) indicate a significant correlation R = 0.193 \(\div\) 0.401 with the damage of all analysed building elements except for the damage to balconies and loggias (as well as eaves and cornices) \(w_{u9}\).  
The highest correlation coefficients with values of R = 0.310 \(\div\) 0.827, as in the case of large-block buildings, were shown between the index describing the influence of mining tremors \(a_{sg}\), and the damage indices of all significant elements.  
For both maximum deformation \(\varepsilon_{max}\) and the effect of \(a_{sg}\) and \(PGA_{H}\) tremors, significant correlations were found with the \(w_{u}\) index determined for the whole building, which are 0.156, 0.891, and 0.427, respectively.  
The biggest share in the variability of the damage intensity index for both large-block and large-panel buildings is determined by the \(a_{sg}\) tremors index. As shown in Tables 4 and 5, it obtains much higher values of correlation than the \(PGA_{H}\) index. This confirms that its definition is correct. Slightly higher values of correlation occur in case of large-panel buildings. This may be caused by repair work, which influences the total elimination or reduction of the scope of damages. Large-panel buildings are on average nine years younger and the scale of renovations they have been undergone so far is lower than in case of objects erected in large-block technology.
## 4 Experiments and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **MCD (dB)** & \\ \hline
**Methods** & **Cross-Gender (A8–A2)** & **Intra-Gender (A2–A23)** \\ \hline Source–target & 11.873 & 11.507 \\ GMM & 10.326 & 8.794 \\ DIFFGMM & 10.207 & 8.958 \\ BLSTM & 6.964 & 7.421 \\ Proposed & **6.786** & **7.155** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean values of the MCD of all non-silent frames of the VC methods on the THCHS-30 dataset (15 dB babble added to source). Figure 5: Results of subjective evaluation for different methods on THCHS-30 (15 dB babble added to source): (**a**) MOS for similarity with 95% confidence intervals and (**b**) MOS for naturalness with 95% confidence intervals. By reading the results in Table 3 and the boxes in Figure 6, it was found that the proposed method was good at capturing the details of the spectral features of speakers, which finally contributed to its relatively better performance than the baselines. For readers' reference, audio samples for the subjective listening tests are accessible via: [[https://github.com/miaoxk/Demo](https://github.com/miaoxk/Demo)]([https://github.com/miaoxk/Demo](https://github.com/miaoxk/Demo)).
# Specific Modeling Issues on an Adaptive Winglet Skeleton
## Abbreviations  
A/C Aircraft  
CFD Computational Fluid Dynamics  
CS2 Clean Sky 2 Programe  
CFRP Carbon Fiber Reinforced Polymer  
CL Lift coefficient  
DOF Degree of Freedom  
FE Finite Element  
FEM Finite Element Model  
GRA Green Regional Aircraft  
LCA Load Control and Alleviation  
LDO Leonardo Aircraft Division  
LE Leading Edge  
LoD Lift over Drag  
RL Standard Reference Load  
SDOF Single Degree of Freedom  
SHM Structural Health Monitoring  
TE Trailing Edge  
UD Unidirectional
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 2 Materials and Methods
### Datasets
#### 2.1.2 Note-Level Singing Voice Dataset (SVNote1)  
This dataset included 30 audio files sung by seven men and three women. Each of the singers recorded three popular pieces of music (1--\"soft kitty, warm kitty, little ball of fur\", 2--\"school bell\", and 3--\"Twinkle, twinkle little star\"). They are, in total, around 16 min of music with 1440 onset annotations. In addition, three people annotated the onsets of each audio file separately, which means that three sets of annotations are provided for each audio file. The three annotators' average is considered to be the ground truth for this study.
## 4 Construction of 3D Interpreted Model Network  
The definition and representation of 3 DIM are described above. This section focuses on solving the following problems: how to construct 3 DIM based on 3 DAM, and realize the computer representation and storage of 3 DIM, so as to support the further application.  
In this paper, the concept of a 3D interpreted model network is used as the computer representation and storage way of 3 DIM. This network is an instantiated representation structure proposed in Section 3.3. One 3 DIM corresponds to one 3D interpreted model network. The overall construction process of 3D interpreted model network is shown in Figure 6. Firstly, design knowledge resources and the 3D annotated model are integrated to form the 3D interpreted model by the design knowledge annotation method, and knowledge index files describing design knowledge and product information are generated. At the same time, P21 files describing product geometric and technical information are exported by CAD software. Then, the geometric information, technical information and the association group of geometric and technical information are extracted from the P21 file. Furthermore, the product information is fused with the design knowledge recorded in the knowledge index file to form a 3D interpreted model network. In this method, design knowledge annotation, information extraction and knowledge fusion are key steps. The specific implementation process is described below.
## 2 Materials and Methods
### Software Development Kit (SDK) Layer  
Bearing in mind that the script generates each 3D model with a positional accuracy criterion (3D coordinates), the present process can spatially arrange the entire group of such models (archaeological assemblage(s)) as a cluster that can move as one so that the same separation and orientation are always constant among the finds. According to the same principle, ARKit v. 2.0 allows the mobile device to display artefact aggregation in unison.  
Therefore, the archaeologist can anchor such a cluster to the excavation profile by utilising several permanent landmarks on the terrain and use them as a visual guide for tying up the cluster.  
To reach this goal, the user selects each required 3D landmark model on the mobile display and, through the tracking facility, the locator software component fixes that selected virtual landmark to the physical tag on the ground, slotting in a precise match between the virtual and real world.  
It is worth noting that the more anchors tie the cluster up to the excavation contour, the more stable the cluster remains (see Figure 5).  
Additionally, the current mixed reality application employs a 3D photogrammetry model of the Les Cottes cavity to integrate and combine the 3D cluster with a virtual 3D excavation trench. The incorporation of these two 3D models allows anchoring the composite recreation anywhere, providing the public with the possibility to experience a virtual 3D-tour around the excavated site. Therefore, the user can leave such an integrated 3D photogrammetry model on the screen to accomplish a virtual visit to the site (see Figure 6). Alternatively, when on-site, this can be turned off to view just the 3D models right on the real physical terrain without any other obstacle.  
Figure 5: Cavity landmarks (tags) that align the 3D cluster in the MR application.  
For its part, the visualiser software component can scale the complete cluster, permitting the archaeologist to exploit real-scaled, upscaled or downscaled model versions on the MR application (see Figure 6). Likewise, this component automatically activates the unzooming and zooming actions on the mobile screen depending on whether the MR application is moving away or towards a specific position.  
In this light, the SDK Layer uses ARKit v. 2.0 to perform the location and visualisation of 3D models through embedded methods, namely application program interfaces (API), coded under an Xcode framework utilising Swift.
# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 4 Res Inversion Method
### AE Network  
As shown in Figure 5, network 1 is an AE network composed of a feature encoder \(f_{e}\) and a reconstruction decoder \(f_{d}\).  
First, the input to the AE network is an ideal RF signal without RF structure information \(\mathbf{x}\).  
Then, the hidden layer dimension is set to be smaller than the input dimension. The feature encoder automatically learns the compression feature of the RF signal, which  
Figure 5: AE network.  
is \(\mathbf{h}_{sig}\)--the deep signal feature. The RF signal \(\mathbf{\hat{x}}\) is then reconstructed from this feature unsupervised via the reconstruction decoder, thereby realizing the mapping from the data domain to the feature domain and then back to the data domain.  
The coding and decoding process of the AE network can be described as follows:  
\[\mathbf{h}_{sig}=f_{e}(\mathbf{x},\theta_{e}) \tag{6}\] \[\mathbf{\hat{x}}=f_{d}(\mathbf{h}_{sig},\theta_{d}), \tag{7}\]  
where \(\theta_{e}\) represents the feature encoder parameters, and \(\theta_{d}\) represents the reconstruction decoder parameters.  
This reconstruction method can ensure that the loss of learned feature information is minimal. To train the AE network, it is necessary to define an objective function; that is, the reconstruction error:  
\[J_{AE}(\{\theta_{e},\theta_{d}\})=\frac{1}{I}\sum_{i=1}^{I}L(\mathbf{x}, \mathbf{\hat{x}}), \tag{8}\]  
where \(I\) is the number of input signal samples, and \(L(\mathbf{x},\mathbf{\hat{x}})\) represents the loss function. Common forms such as the mean square error (MSE) and cross entropy (CE) can be selected. This paper adopts MSE:  
\[L(\mathbf{x},\mathbf{\hat{x}})=||\mathbf{x}-\mathbf{\hat{x}}||^{2}. \tag{9}\]  
Therefore, the reconstruction objective function based on the MSE loss function is expressed as follows:  
\[J_{AE}(\{\theta_{e},\theta_{d}\})=\frac{1}{I}\sum_{i=1}^{I}||\mathbf{x}-f_{d}( f_{e}(\mathbf{x},\theta_{e}),\theta_{d})||^{2}. \tag{10}\]  
The exact solution of the input RF signal obtained via the AE network is as follows:  
\[\mathbf{\hat{x}}=f_{d}(f_{e}(\mathbf{x},\theta_{e}),\theta_{d}). \tag{11}\]  
Finally, the optimization method is used to train the AE network continuously, and the trained feature encoder is saved.
## 3 New GTR and GBR Using Tissue Engineering  
### Periodantal Tissue Engineering (TE)  
Previously regenerated tissues via conventional GTR or GBR procedures using graft materials and membranes showed an insufficient mechanical strength, unstable volumetric stability under an external force and limitations for application in a case with an extensive range of tissue defects [4; 5]. To overcome these limitations, tissue engineering (TE) was introduced as regenerative medicine. TE is a technique which fabricates tissues outside of the body and implants them into the body to regenerate the lost target tissues [59]. The classic TE paradigm includes four essential requirements: (1) scaffold, i.e., biomaterials which provide space for new cell ingrowth; (2) biological agent, i.e., appropriate regulatory signals; (3) cells, i.e., responsive progenitor cells; and (4) blood supply [60; 61]. The scaffold can be either acellular or cellular, whereby the former provides adequate space to recruit local stem cells and osteoprogenitor cells, and the latter incorporates the involved stem cells before the implantation [62]. The fabrication of a successful scaffold plays a crucial role in TE; thus, new technologies focus on 3D scaffolds [61]. Therefore, in this review, we discuss scaffolds, while other elements such as cells and biological agents were discussed in another review [63].  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Types of Grafts** & **Advantage** & **Disadvantage** \\ \hline \multirow{6}{*}{Autograft} & Osteogenesis: containing live cells & \\  & Osteoinductive: having BMP and other & \\  & growth factors & \\  & Osteoconductive & Pain \\  & Lack of immunity & Limited donor site: limited amount \\  & No disease transmission & \\  & Cost-effective & \\ \hline \multirow{6}{*}{Allograft} & No morbidity of donor site & \\  & Unlimited amount & No osteogenesis: no live cell inclusion \\  & Osteoinductive, osteoconductive & Disease transmission: viral or bacterial, \\  & Various mineral composition: cortical, & 12.9–13.3\% \\  & cortico-cancellous, cancellous & High cost \\  & Various form: powder, cancellous cubes, & Dependent on donor’s bone state: age \\  & cortical chips/fresh, fresh-frozen, & Ethical problem \\  & freeze-dried/mineralized, demineralized & \\ \hline \multirow{6}{*}{Xenograft} & No morbidity of donor site & No osteogenesis \\  & Unlimited amount & No osteoinduction \\ \cline{1-1}  & Osteoconductive & Disease transmission \\ \cline{1-1}  & & Ethical problem \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages according to the types of grafts.
# Calculation of Intracoronary Pressure-Based Indexes with JLabChart
###### Abstract  
The Fractional Flow Reserve (FFR) and instantaneous wave-Free Ratio (iFR) have been proposed and clinically validated to measure the pressure gradient across coronary stenoses. They provide quantitative information on stenosis severity. Both are used in coronary revascularization procedures to measure intracoronary pressure giving quantitative information to evaluate coronary diseases during angiographic procedures. We designed and implemented a tool able to acquire and measure iFR and FFR supporting the physicians studying and treating patients in interventional cardiology laboratories. We designed an extensive case study to assess the performance of the tool in (i) acquiring pressure signals from blood pressure measurement systems; (ii) calculating FFR and iFR; and (iii) filtering out extra-beats signals during realtime signal analysis phases. The tool, named JLabChart, is available online. We tested it on two sets of data for a total of 600 cycles from 201 pressure measurements performed on 65 patients, from the Interventional Cardiology Unit of Magna Graecia University. The recognition of cardiac cycles and keypoint of the pressure curve was effective in 100% of cases for proximal (aortic) pressure and in 99.2% for distal pressure. The FFR calculated by JLabChart had an excellent correlation (\(R_{p}=0.960;\,p<0.001\)) with the FFR values obtained through the commercial systems. Similar results were obtained with iFR (\(R_{p}=0.998;\,p<0.001\)). Finally, the tool measurement results were compared with a commercial tool proving JLabChart's efficiency with real cases. It was also compared with measurements performed on synthetic vessels and stenosis designed using the Comsol commercial tool. JLabChart is able to provide reliable measurements of FFR and iFR indexes used to support decisions on interventional procedures. It represents a valuable open source support system that can be used in an interventional cardiology laboratory.  
intracovitary measurements; coronary procedures; online measurements; Fractional Flow Reserve +
Footnote †: journal: Applied Sciences
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 3 Background
### 6 TiSCH Minimal Scheduling Function  
6 TiSCH (IPv6 over IEEE 802.15.4e TSCH mode) is an IETF standard for facilitating IPv6 communications over IEEE 802.15.4e TSCH mode [3]. It defines control plane protocols to align link-layer resources with application needs and integrates CoAP with link-layer security [23]. The 6 TiSCH architecture combines IPv6 with IEEE 802.15.4e TSCH MAC, organizing time into slots within slot frames in which each slot is a distinct time interval for node functions [24; 25]. Cells representing communications capability are controlled by the 6 TiSCH Operational Sublayer Protocol (6P), which manages cell allocations and time slot usage to ensure reliable communication and resource efficiency in IIoT environments [25; 26].  
The Minimal Scheduling Function (MSF) in 6 TiSCH networks adjusts cell allocation based on utilization metrics to meet dynamic bandwidth requirements [27]. Using TSCH at the MAC layer, MSF operates with minimal cells for network initialization, autonomous cells for default communication and negotiated cells for managing communication load [28]. MSF coordinates time slot and cell distributions, synchronizes node transmission schedules, and assigns specific roles to nodes [29]. This protocol ensures efficient bandwidth use, low latency, and high dependability, making it suitable for real-time control and monitoring systems [25].
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Real-World Application Scenarios of ViMindXAI  
This section introduces three case studies that demonstrate the real-world feasibility and domain adaptability of the ViMindXAI platform in smart buildings, healthcare, and industrial environments. Each scenario reflects practical requirements for low-latency processing, privacy preservation, and regulatory compliance. All case studies follow the four-layer architecture of ViMindXAI: edge data acquisition, hybrid data infrastructure, distributed analytics and learning, and user-facing outputs with explainability and compliance support. These examples highlight how the platform can be adapted to diverse operational contexts while maintaining modularity, scalability, and trustworthiness.
# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 4 Multi-WiIR  
### System Overview  
In this section, we elaborate on the Multi-WiIR system, as depicted in Figure 4, Multi-WiIR comprises four main modules:  
Figure 4: Multi-WiIR system framework.  
Figure 3: BiLSTM network framework.  
1. Data Collection: Responsible for gathering CSI data from both single-user and multi-user scenarios, this module labels the data to differentiate between legitimate and illegitimate individuals, storing them in the data collection module.
2. Data Processing: During the data processing stage, collected data undergo a series of processes, including antenna selection, outlier detection, filtering, and linear interpolation. The processed data are then fed into the feature extraction module.
3. Feature Extraction: The feature extraction module utilizes the D4 wavelet in the discrete wavelet transform (DWT) algorithm to process the processed CSI amplitude, calculating approximation coefficients, which are then input into the deep learning model for training.
4. Multi-user Legitimacy Authentication: Built upon a multi-branch deep neural network comprising a multi-layer convolutional neural network and bidirectional LSTM layers, this module extracts features for each user using each branch of the WilR-Net. Multiple binary classifiers are employed to determine the legitimacy of each user, achieving multi-user legitimacy recognition.
# A Low-Cost IoT-Based Bidirectional Torque Measurement System with Strain Gauge Technology
###### Abstract  
The scope of this paper is the development of a cost-effective wireless torque measurement system for vehicle drivetrain shafts. The prototype integrates strain gauges, an HX711 conditioner, a Wemos D1 Mini ESP8266, and a rechargeable battery directly on the rotating shaft, forming a self-contained sensor node. Calibration against a certified dynamometric wrench confirmed an operating span of \(\pm\)5-50 N-m. Within this range, the device achieved a mean absolute error of 0.559 N-m. It also maintained precision better than \(\pm\)2.5 N-m at 95% confidence, while real-time data were transmitted via Wi-Fi. The total component cost is below EUR 30 based on current prices. The novelty of this proof-of-concept implementation demonstrates that reliable, IoT-enabled torque sensing can be realized with low-cost, readily available parts. The paper details assembly, calibration, and deployment procedures, providing a transparent pathway for replication. By aligning with Industry 4.0 requirements for smart, connected equipment, the proposed torque measurement system offers an affordable solution for process monitoring and predictive maintenance in automotive and industrial settings.  
torque measurement; strain gauge; IoT sensor; bidirectional calibration; wireless data acquisition; calibration; error analysis; rotating shaft; predictive maintenance Article