esa-sceva/satcom-chunk-collection huggingface dataset:
# NZEB Renovation Definition in a Heating Dominated Climate: Case Study of Poland
## 2 Calculation Methodology
### Stage II--Cost-Optimal Renovation Variant of the Single-Family Houses  
In the second part of the analysis the cost optimal scenario for renovation of two reference buildings was determined. As an optimizing criterion, the minimum cumulative cost (1) was used, calculated for the 30-year period, covering renovation cost and discounted exploitation cost. The calculation was based on the aforementioned methodology framework established by the Commission [34; 35] and the literature review: the study [47] shows in a clear way how to define construction variants, [48] steps involved in the cost-optimal methodology, [49; 50] calculations made for single-family house, [51] experiences from NZEB renovation variants analysis in the neighbouring country and [52] application of RES in the renovation process.  
The two basic scenarios of renovation were defined based on the heat transfer coefficient of the building elements calculated in stage I. Additionally, renovation of ventilation (two options), central heating and DHW system was included. Use of RES was limited to DHW. For each scenario the energy need for heating and ventilation was calculated on the basis of the methodology given in ISO 13790 standard [53]. The energy efficiency of building systems was evaluated by the simplified calculation methods derived from the Polish regulation [54] and several European reference standards: EN 15243 [55], EN 15316 [56], and EN 15316-3-1:2005 [57]. The primary non-renewable energy demand for heating, ventilation, DHW and auxiliary systems was calculated following the Polish regulations [54]. The following PEF (only non-renewable part) values were used: heating and DHW energy: 1.1, electrical energy: 3.0, and solar energy: 0.0.  
The renovation cost for a given scenario was determined according to a simplified method of the investment cost calculation. For each building element (e.g., external wall) the unit renovation cost (from stage I) was multiplied by the element area. The 8% tax on goods and services was included for two base scenarios.  
The cost of the annual exploitation is a sum of final energy cost used for heating and ventilation, DHW, by auxiliary equipment operation and energy prices. The energy demand for heating and ventilation was calculated for three locations in Poland--Warsaw (medium climate), Szczecin (the warmest climate), and Suwalki (the coldest climate). The same discount rate, energy price increase rate, and inflation level were used as in stage I.
## 3 Experimental Setup
### Key Transmission Performance Metrics
#### 3.2.3 Performance Under Varying Distances and Real-World Channel Impairments  
* Current Setup
* Our current experiments are performed at 1 m with an LED-based Li-Fi link operating around 300-440 lux. We deliberately tested different lux levels to simulate typical indoor illumination conditions, but we have not yet covered a range of link distances (e.g., 2 m, 5 m, etc.).
* Impact of Distance
As distance grows, the received optical power drops (following an inverse-square law in free-space optical scenarios), and the SNR typically decreases.
* Lower SNR can degrade the autoencoder's ability to reconstruct heavily masked waveforms, pushing EVM higher.
* However, if we can ensure an adequate SNR above the forward error correction threshold, the overall concept should still apply. The system might require a lower masking ratio at longer distances or a higher optical power/better photodiode sensitivity to maintain acceptable EVM.  
Real-World Channel Impairments
* In practice, ambient light interference, multi-LED flicker, device aging, and reflection-induced multipath can further degrade SNR or introduce nonlinearities.
* Our masked autoencoder approach is relatively robust because it learns the global structure of the DMT waveform. However, more severe channel distortions may require additional training data or an expanded neural network capacity.
* We intend to investigate multi-path or large-signal distortion scenarios in future work. The masked approach could also be combined with a pre-distortion or equalization stage if necessary.
* Future work
* We concur that a more in-depth test across various distances and real-world building environments (e.g., offices, corridors) will help demonstrate the generalizability of our approach. We will aim to include such results in an extended version of this research or subsequent publications.  
In summary, BER, computational overhead, and distance/channel impairments are as follows:  
* BER Discussion: While we measured EVM in our current paper, we acknowledge that a direct BER vs. SNR analysis can provide additional clarity. We will integrate approximate BER estimates and potentially run separate BER tests in future expansions.
* Computational Overhead: Our method introduces a deep learning step for reconstruction, which can be performed in real-time with optimized hardware. A direct complexity comparison to traditional Li-Fi techniques is planned to give numerical overhead estimates.
* Distance and Channel Impairments: We have validated the method at 1 m and under typical indoor illuminance, but broader testing at multiple distances and channels is an important next step.
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 3 Methodology
### YOLOv5s Architecture
#### 3.1.2 Backbone  
The primary constituents of the backbone network are the Convolution Module (Conv), the C3 module, and the Spatial Pyramid Pooling Fusion (SPPF) module. Conv is a fundamental component encompassing Convolution, Batch Normalization, and Sigmoid Linear Unit operations that extract features from input data. These operations of conv aid in detecting and highlighting relevant image patterns and structures. The C3 module plays a crucial role in feature extraction by facilitating cross-stage connections. It enables data exchange between the various phases or layers of the neural network. This exchange contributes to a greater understanding of the input data by simultaneously capturing low-level and high-level features. The parameter N in C3 specifies using the BottleNeck module N times within a single module. Including a residual structure within the BottleNeck module has been observed to effectively address the gradient disappearance arising from the deepening of neural networks. The SPPF structure evolved from Spatial Pyramid Pooling (SPP) to alleviate computational pressure and increase network speed. This module is designed to capture information at multiple spatial dimensions. It divides the input feature maps into various grid sizes, and the network's feature extraction is accomplished via maximum pooling with varying pooling kernel sizes independent of each grid. This process enables the model to concentrate on multiple image details regardless of their spatial dimension, thereby improving object detection accuracy.
## 6 Conclusions  
In this paper, we introduce NPSPT as a new challenge to evaluate the real-game performance of the player-tracking algorithms in non-professional multi-player soccer scenes. NPSPT includes single-view recording systems for continuous data collection, and is the largest single-view dataset recorded in real games, which is much more diverse and more suitable for this challenge than the current datasets.  
However, the performance of four state-of-the-art methods cannot reach a perfect accuracy, suggesting NPSPT presents a major challenge for the tracking community, requiring principled novel approaches.  
Based on our future experimental results and feedback from other researchers, we will target the scenarios and situations that need to be trained. More and more real-game-videos and labeled clips will be collected and will be open to all researchers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GameType** & **Algorithms** & **HOTA \(\uparrow\)** & **DetA \(\uparrow\)** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **IDSW \(\downarrow\)** \\ \hline
5-A-Side & Trackor++ & 23.64 & 47.35 & 58.17 & 23.42 & 586 \\  & CenterTrack & 40.00 & 62.65 & 80.41 & 41.33 & 218 \\  & FairMot & 47.51 & 61.66 & 82.26 & 58.46 & 315 \\  & TransTrack & 37.30 & **62.83** & 82.18 & 39.33 & 222 \\ \hline
7-A-Side & Trackor++ & 23.42 & 33.27 & 4.46 & 30.40 & 119 \\  & CenterTrack & 52.07 & 64.00 & 73.66 & 58.79 & 58 \\  & FairMot & 57.76 & **69.85** & 92.26 & 71.59 & 36 \\  & TransTrack & 43.84 & 62.44 & 80.23 & 51.75 & 116 \\ \hline
8-A-Side & Trackor++ & 27.41 & 48.66 & 53.37 & 31.90 & 748 \\  & CenterTrack & 44.71 & **75.87** & 90.75 & 44.76 & 283 \\  & FairMot & 54.20 & 72.01 & 90.84 & 62.92 & 111 \\  & TransTrack & 30.91 & 55.65 & 74.89 & 35.78 & 656 \\ \hline
11-A-Side & Trackor++ & 22.11 & 38.95 & 12.21 & 23.77 & 327 \\  & CenterTrack & 43.25 & 66.54 & 72.52 & 41.17 & 232 \\  & FairMot & **50.27** & **68.88** & 72.15 & 51.26 & 130 \\  & TransTrack & 16.56 & 40.55 & 18.03 & 14.54 & 929 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the candidate methods on NPSPT.  
Author Contributions: [PERSON] and [PERSON] proposed the idea of the paper. [PERSON] and [PERSON] helped manage the annotation group and helped clean the raw annotation. [PERSON] conducted all experiments and wrote the manuscript. [PERSON], [PERSON] and [PERSON] revised and improved the text. [PERSON], [PERSON] and [PERSON] are the people in charge of this project. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Key Projects of Major Humanities and Social Sciences of Zhejiang's Universities 2019-2020] grant number [2021 QN043], [The National Natural Science Foundation of China] grant number [62007029].
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 5 Conclusions  
In this paper, we proposed a novel collaborative localization using BLE-based encounter communication. In this encounter communication, an architecture that achieved communication of 500 Bytes was realized. Our method allows multiple communications to be established. We also developed a method to compare the localization of two devices. Virtual walking data were generated and we trained a decision tree model. In addition, we proposed a novel method for correcting the position and direction by using the output value of the comparison. The experimental results showed that the comparison model achieved an 83.0% accuracy for 16 routes at eight facilities. In addition, the proposed method improved the baseline method by 13.0% by appropriately using the collaborator's location.  
The limitation of the proposed method is the manual analysis of map images taken from information boards. In this study, passage regions are manually extracted from the map images. Thus, an automatic method of passage extraction is necessary. A potential approach for automatic extraction is using semantic segmentation models, such as fully convolutional networks [43] and DeepLabv3+ [44].  
Conceptualization, K.K. and Y.S.; methodology, K.K.; software, K.K.; validation, K.K. and T.M.; formal analysis, K.K.; investigation, K.K. and Y.S.; resources, K.K.; data curation, K.K.; writing--original draft preparation, K.K. and T.M.; writing--review and editing, S.O.; visualization, K.K.; supervision, S.O.; project administration, S.O.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Japan Society for the Promotion of Science (JSPS) KAKENHI under grant 21K12135.  
Not applicable.  
The dataset used in this study will be available from the authors.  
The authors declare no conflicts of interest.  
Figure 14: The walking paths (red and blue) for encounter communication. The non-English terms in the map indicate room names.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Bldg. No. 1** & **Complex Bldg.** & **Overall** \\ \hline Detection rate & 0.90 & 1.00 & 0.95 \\ Average distance (m) & 1.26 & 2.57 & 1.91 \\ SD. of distance (m) & 0.87 & 1.29 & 1.26 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of encounter communication.
# A Review on Internet of Things for Defense and Public Safety
## 3 Target Scenarios for Mission-Critical IoT
### Logistics  
Logistics is an area where multiple low-level sensors are already being used in defense. Currently, their deployment remains constrained to benign environments with infrastructure and human involvement. The military has already deployed some IoT technologies in non-combat scenarios in order to improve back-end processes. For example, RFID tags have been used to track shipments and manage inventories between central logistics hubs.  
In the following subsections, we describe examples that belong to two main categories: fleet management and individual supplies.
## 3 Analysis of Experimental Results
### Analysis of Information Recognition Effectiveness for Secondary Cabinet Terminal Blocks
#### 3.5.2 Analysis of Physical Entities Recognition Results for Secondary Screen Terminal Blocks
After area localization using YOLOv7-AO, irrelevant background areas and severe distortions and obstructions on the left side of another terminal block are effectively eliminated. In the second layer of the model, DB is introduced. The YOLOv7-DBAH model, tailored for detecting small physical entities of terminal blocks, is used to locate all physical object boundary points under distorted shooting angles accurately, achieving good results. Finally, perspective technology is used to correct the distorted text information, enabling complete physical entities recognition. Figure 11: Results of the physical recognition model. (**a**) Area Location. (**b**) Outcome Binarization Maps. (**c**) Identification Results.
## 3 Numerical Simulation Analysis
### Model Validation  
Based on the above modeling approach, a numerical model is established to simulate the PC specimen (see Figure 14). Hysteretic curves and skeleton curves of both the simulation and the experiment are shown in Figures 15 and 16, respectively, for comparison.  
It is shown in Figures 15 and 16 that the simulation and experimental results share similar curves. Specifically, the peak load of the experiment is 244.6 kN and the corresponding displacement is 19.2 mm, whereas the peak load of the simulation is 251.9 kN and the corresponding displacement is 19.9 mm. In other words, the peak load and the corresponding displacement of the simulation are slightly higher than those of the experiment, and the values for the simulation and experiment are 2.9% and 3.6% at the highest, respectively. The error meets the requirements of practical engineering. Therefore, the numerical model developed for the PC shear wall can be used for parameter analysis.  
Figures 17 and 18 show the stress cloud diagrams of the connector and the bolts. It is noted that the welding area of the connector and rebars and the bolt holes yielded, when it reaches the maximum stress of 254 MPa (the yield stress is 235 MPa). However, the other areas of the connector are in the elastic stage. As for bolts, the maximum stress is 607 MPa, which appears in the outermost bolts. Since the yield stress of bolts is 640 MPa, all the bolts are in the elastic stage. The result is consistent with the test result described in Section 2.3.2.
# Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
## 3 Spatiotemporal Models for Continuous Emotion Recognition
### Spatiotemporal Models
#### 3.3.1 Cascaded Networks (2D CNN-LSTM)  
Long short-term memory units (LSTMs) are a special kind of RNN, capable of learning order dependence that may be found in a sequence of frames from a video. The core of LSTMs is a cell state, which adds or removes information depending on the input, output, and forget gates. The cell state remembers values over arbitrary time intervals, and the gates regulate the flow of input and output information of the cell.  
The architecture of the proposed cascade network combines the 2D convolutional layers of VGG-16 for representation learning with an LSTM to support sequence prediction, as shown in Figure 6. Video frames are fed to the VGG-16-LSTM and then accumulated to form a feature vector representing one clip. After going through the LSTM unit for modeling temporal information between frames, the fully connected (FC) layers perform valence and arousal values regression. The LSTM has a single layer with 1024 units, with random and uniform distribution initialization to extract temporal features from the face features learned by the 2D CNN. In addition, some dropout (20%) and recurrent dropout  
Figure 5: Evolution of the number of sequences for training, validation and test sets for different configurations: 10 and 50 fps; sequence length of 16, 32 and 64 frames; overlapping ratio of 20%, 50% and 80%.  
(20%) were added on LSTM units to avoid overfitting. There are also three fully connected layers stacked after the LSTM to improve the expressiveness and accuracy of the model.  
The VGG-16-LSTM architecture is pre-trained considering two different strategies: (i) VGG-16 pre-trained on the VGG-Face dataset; and (ii) VGG-16 pre-trained on the VGG-Face dataset and fine-tuned on the RAF-DB dataset. The former strategy adds extra information to the models, such as the classification of discrete emotions with RAF-DB, which could help to improve the performance on the regression task.
## 1 Introduction
Since THGEM holes are traditionally formed via mechanical drilling, they may be subject to variations in hole size across the THGEM due to drill bit wear. This may lead to further gain non-uniformities. This paper details the manufacture and characterisation of glass THGEMs (G-THGEMs), created using a novel masked abrasive machining process (patent pending [16]), within the ARIADNE R&D framework [17]. Performance of the G-THGEMs is compared to a conventionally manufactured THGEM. The conventional THGEM discussed in this paper has a 1 mm thick FR4 substrate and copper-coated electrodes, with 500 \(\upmu\)m diameter holes formed via mechanical drilling on an 800 \(\upmu\)m pitch hexagonal array, with 50 \(\upmu\)m etched rims. Glass GEMs (G-GEMs), produced from photosensitive glass, have already been fabricated (and tested) via a photolithography technique [18; 19]. The new manufacturing technique presented in this paper is not limited to photosensitive glass and is suitable for a wide range of substrates. Both substrate and electrode materials can be tailored depending on specific application requirements--for example, high stiffness, low outgassing and/or high radiopurity. Abrasive machining allows for unprecedented versatility in THGEM patterning in terms of hole shape and layout. In addition, the abrasive machining process is no longer subject to drill bit wear, potentially improving hole diameter consistency across the THGEM. The abrasive machining manufacturing process could have far reaching consequences within the neutrino and dark matter sectors [12; 13; 14; 15; 20], as well as in medical imaging [21].
## 2 Experiment and Methods  
### Experiment Setup  
The schematic diagram of the experimental system is shown in Figure 2. The gas flow of each component is controlled by the mass flow controller (MFC) and mixed uniformly by the gas mixer before entering the DBD reactor. The exhaust gas after DBD treatment is absorbed by alkali liquor,  
Figure 1: (a) Regulation of NO\({}_{\text{X}}\) emission; and (b) regulation of SO\({}_{\text{X}}\) emission. ECA, emission control area. Tier 1 took effect in 2000, Tier 2 in 2011 and Tier 3 in 2016.  
which reduces the damage to the environment. The concentrations of NO, NO\({}_{2}\) and O\({}_{2}\) were measured by flue gas analyzer (Horiba PG-350). The total gas flow rate is 5 L/min and the gas temperature is 25 \({}^{\circ}\)C.  
The high voltage AC (HVAC) power supply is used as the power supply of DBD reactor. The supply voltage used to generate plasma in the DBD reactor is within 0-25 kV and discharge frequency is 10 kHz. The discharge power of DBD reactor is controlled by changing the supply voltage. The measurement capacitance (\(C_{m}\)) of DBD discharge transmission charge is 0.47 \(\upmu\)F, and the discharge voltage (voltage between inner and outer electrode) is decreased 1000 times by high voltage probe (TEK P6015A), which is input into oscilloscope. The discharge power is calculated by Lissajous figure. Figure 3 shows Lissajous figure obtained by digital oscilloscope (TEK TBS1052b).  
A cross-sectional view of DBD reactor and inner electrodes are shown in Figure 4. The DBD reactor is coaxial cylindrical, and the dielectric barrier is a quartz tube. The length of the quartz tube is 450 mm, the outer diameter is 18 mm, the inner diameter is 15 mm and the thickness is 1.5 mm. The quartz tube is wrapped with copper foil as the outer electrode, and the length of discharge interval is adjusted by changing the length of copper foil. The materials making up the inner electrode are  
Figure 3: Lissajous figure obtained by digital oscilloscope (supply voltage V\({}_{\mathrm{PP}}\) =14 kV, DBD reactor with inner electrode diameter 14 mm, electrode length 100 mm).  
Figure 2: The schematic diagram of the experimental setup.  
copper, stainless steel and aluminum with diameters of 10, 12 and 14 mm. The shape of the inner electrode is rod or screw.
# A Local Discrete Feature Histogram for Point Cloud Feature Representation
## 4 Experimental Results
### Performance Evaluation of the LDFH Descriptor
#### 4.3.3 Efficiency  
For evaluating the time performance of descriptors, we randomly select 1000 key points from each model in the B3R dataset and describe the local surface of their surrounding regions under different support radii. The average computation time for every feature descriptor is recorded. The support radius \(R\) is extended from 5 mr to 30 mr at 5 mr intervals. Figure 7 displays the results. From the figure, SHOT has the lowest computational time, followed by TOLDI. The computation time of LDFH is comparable to SHOT and TOLDI, with smaller support radii, and the computation time gradually increases with the increase in the support radius, but still remains efficient compared to the other descriptors. Due to FPFH computing simplified point feature histograms for every point, it has the highest computation time.