esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Dataset
#### 4.1.2 Rational Assessment of Data Sets  
To evaluate the rationality of the dataset, we conducted a statistical analysis on the number of annotated bounding boxes in each image and the frequency of each category in the sampled dataset, as shown in Figure 6. Due to the large volume of the bone plates and the low camera shooting angle, the number of objects in each image is only between 1 and 4, with the majority being 1. There is also a significant difference in categories, with a roughly three-fold difference between the maximum and minimum values. This further increases the difficulty of model training and enhances the challenge of the task. To adapt to the characteristics of the dataset, we adjusted the number of pre-detection boxes in the improved DETR to 10 and made fine adjustments to the category loss calculation based on the probability of different categories appearing in the dataset. Specifically, we employed a weighted cross-entropy loss function. This loss function takes into account the probability distribution of each category in the dataset and assigns a weight to each category. The weights can reflect the degree of class imbalance.  
Figure 5: Annotation examples of the dataset: selecting regions with significant feature differences for annotation, rather than the entire object. In the image, letters A, B, C, D, and E correspond to different models of bone plates, with each letter indicating a specific model distinguished by its unique features.  
The weighted cross-entropy loss function is defined as follows:  
\[L(y,\ p)=-\sum[w\_i\ *\ y\_i\ *\ log(p\_i)] \tag{10}\]  
where \(L(y,p)\) is the weighted cross-entropy loss value; \(y\) is the one-hot encoded representation of the true labels; \(p\) is the probability distribution predicted by the model; \(i\) is the index representing the current class; \(w_{i}\) is the weight assigned to class; \(y_{i}\) is the value of class in the true label \(y\) (0 or 1); and \(p_{i}\) is the probability of class predicted by the model.  
To calculate the weight \(w\_i\), we can utilize the probability of occurrence of each class in the dataset. A simple approach is to use the inverse frequency of the classes:  
\[w\_i=N\ /\ (N\_i\ *\ K) \tag{11}\]  
where \(N\) is the total number of samples in the dataset, \(N\_i\) is the number of samples for class in the dataset, and \(K\) is the total number of classes.  
By using this approach, low-frequency classes are assigned higher weights, thereby having a greater influence in the loss function. This helps the model to pay more attention to those classes that occur less frequently, allowing for subtle adjustments during loss computation.
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 7 Conclusions  
This paper improves the anti-collision algorithm based on grouping, and puts forward the GMQT algorithm. Comparing the GMQT algorithm with other algorithms such as QT, 8-ary QT, IACA, BAT and MQT in terms of system efficiency and communication complexity, the simulation results show that the GMQT algorithm has the best communication performance. The GMQT algorithm can effectively keep the recognition efficiency between 0.6 and 0.65, and reduce the communication complexity. Therefore, the GMQT algorithm can be applied to an intelligent warehouse, which can improve the operation efficiency of warehouse more effectively. However, most of the existing anti-collision algorithms are based on the fact that all the tags to be identified are initially within the reader recognition range, and no movement occurs until the recognition is completed. The case where a new tag moves into the reading range of the reader is not considered in the identification process, for example, in the warehouse with thousands of items, new items are put into the warehouse or other items are put into the wrong warehouse, and the item information is not recognized by the reader in time, that is, unknown tags appear in the system. If we use the traditional tag anti-collision algorithm to collect all tags, and then compare the tag information stored in the original database to identify unknown tags, the whole recognition process will consume a lot of time. Thus, next, we will study the dynamic tag recognition algorithm to improve the efficiency of identifying unknown tags.  
Conceptualization, Y.X.; Data curation, Y.X.; Formal analysis, Y.X.; Funding acquisition, L.Y.; Investigation, J.L. and D.L.; Methodology, Y.X.; Project administration, Y.X.; Supervision, Y.Q. and L.Y.; Validation, Y.Q., L.Y. and J.L.; Writing--original draft, Y.X.; Writing--review and editing, Y.X. This research was funded by a \"Research on Key Technologies of Intelligent Digital Mine Information Platform Based on Internet of Things\" project of the Fujian Natural Science Foundation in 2019 Grant No. 2019J01011636 and \"Intelligent Mine Construction and Industrialization Based on Internet of Things and Virtual Reality\" project of the Fujian industrial area regional in 2019 Grant No. 2019H41010031. The authors declare no conflict of interest.  
Figure 5: Layout of intelligence warehouse.
# DCSPose: A Dual-Channel Siamese Framework for Unseen Textures Object Pose Estimation
## 3 Materials and Methods
### Training Phase  
Positive and negative sample pairs between the template and real data were constructed based on pose discrepancies. Subsequently, two feature extractors with shared weights extracted features from these sample pairs. The similarity between features of sample pairs is then computed, facilitating network training through contrastive learning. This entire process constitutes the Siamese structure, as depicted in Figure 5.  
**Construction of learning samples.** To train the network to excel in matching, it is important to construct positive and negative sample pairs. Positive sample pairs consist of matching viewpoints between the query and simulated template images, while negative sample pairs consist of differing viewpoints between the query and template images. Given that viewpoint sampling is discrete, and the discriminability of different viewpoint poses depends on the sampling density, a balance is needed. Taking all factors into account, we consider views with an angular difference of less than 5 degrees to be classified as positive sample pairs. Views with an angular difference greater than 5 degrees are regarded as negative sample pairs. Consequently, for each object, we can generate \(N\) positive sample pairs and \((N-1)\times N\) negative sample pairs.  
**Loss and Similarity.** We referenced the analysis and experimental results of several common contrastive learning loss functions in [9]. From these findings, We opted for the InfoNCE loss function [41] for our model training:  
\[L_{InfoNCE}=-\sum_{i}^{N}log\frac{exp(S(q,t))}{\sum_{k}^{N}1_{k\
eq i}exp(S(q,t ))}\,\]  
where \(S(q,t)\) represents the similarity between query and template feature maps, utilizing cosine similarity. This approach aims to maximize the consistency between positive sample  
Figure 5: Siamese structure for training.  
pairs while minimizing the consistency between negative sample pairs. It helps the network learn to match the query and template images effectively.
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 2 Materials and Methods
### Introduction of a Second Optimization Objective
#### 2.2.2 Step-by-Step Expansion of the Variance Term  
To show how the variance term \(\sigma_{c}^{2}\) factors into the double summation, we can rewrite Equation (3) as  
\[\mathcal{L}_{\text{energy}}(X_{c})=\frac{1}{2}\sum_{i=1}^{H}\sum_{j=1}^{W} \Bigg{[}\frac{\big{(}X_{c,i,j}-w_{c}\big{)}^{2}}{\frac{1}{H\times W}\sum_{a=1 }^{H}\sum_{\beta=1}^{W}\big{(}X_{c,\beta}-w_{c}\big{)}^{2}}\Bigg{]}. \tag{4}\]  
Figure 3: Improved visualization: feature map and energy function. This figure shows the visualization of feature maps and their corresponding energy functions for three different channels. Each color matrix represents the feature map for a specific channel, with values indicating the pixel-wise feature intensities. The mean and variance of each channel are indicated in the title of each subplot. The color bars on the right show the corresponding intensity values for each channel. Below each feature map, the energy function for that channel is plotted, where the red dashed line indicates the channel’s mean value (\(w_{c}\)) for each respective channel. The energy function quantifies the deviation of pixel values from the channel mean, with the energy function values decreasing as the pixel values approach the channel mean, and increasing as the deviation from the mean grows. The energy functions are plotted for Channel 1 (\(w_{c}=0.56\)), Channel 2 (\(w_{c}=0.44\)), and Channel 3 (\(w_{c}=0.44\)).  
Here, \(\sum_{t=1}^{H}\sum_{\beta=1}^{W}\left(X_{c,a,\beta}-\bar{X}_{c}\right)^{2}\) is the total squared deviation across all pixels in channel \(c\). By normalizing each pixel's squared difference \(\left(X_{c,i,j}-w_{c}\right)^{2}\) with this global measure, we obtain two major benefits:  
* **Variance-Based Penalty:** When \(\sigma_{c}^{2}\) is relatively small, even moderate pixel-wise deviations become significant in Equation (4). This helps preserve fine-grained but crucial details within low-variance channels, which might correspond to highly consistent tissue regions or small-scale structures in medical images.
* **Adaptive Robustness:** Conversely, for channels with larger variance, the same absolute deviation is penalized less severely. This prevents the model from imposing excessively strict constraints on channels whose inherent intensity distribution is naturally broad, ensuring the network remains robust to a wider range of variations in those channels.
## 3 Materials and Methods
### Datasets  
For EEG, this study utilizes a dataset originally published by [PERSON] et al. [34], which is openly accessible on both GitHub and Kaggle. As the dataset does not contain any identifiable personal information, ethical approval for its use is not required. [PERSON] et al. [34] collected data from four individuals (two male and two female) over 60 s intervals in three cognitive states: relaxed, concentrating, and neutral. EEG signals were recorded using a Muse EEG headband with dry electrodes positioned at the TP9, AF7, AF8, and TP10 sites. The Muse band includes a right aux channel designed as a placeholder for an auxiliary electrode; however, without an attached electrode, this channel produces only noise, and it was excluded from this study's analysis. Each.csv file in the dataset is structured with five columns: a timestamp, and EEG readings from the TP9, AF7, AF8, TP10, and right aux channels (the right aux channel is excluded from all calculations).  
For ECG, we utilized a publicly available dataset from Kaggle [35]. Each row in this dataset represents a complete ECG recording for a subject, consisting of 140 data points. Columns 0-139 contain the ECG readings, while column 140 provides the labels--either 0 or 1--indicating whether the ECG is normal or abnormal.
## 5 Conclusions  
In this paper, NHSS modified asphalt concrete was prepared to systematically investigate the durability and the performance of NHSS modified asphalt concrete in spring-thawing season according to a self-designed laboratory freeze-soak-scour cycle test, and the freeze-soak-scour damage process of the NHSS modified asphalt concrete is studied by the logistic judgment model. Moreover, the influence of freezing, soaking, and scouring damage factors on the damage process of NHSS modified asphalt concrete in spring-thawing season is analyzed based on the gray rational degree theory. The main conclusions are as follows:  
1. The durability and property test results of two mixtures after long-term freeze-soak-scour cycles illustrated that adding NHSS is an effective technique for mitigating freeze-thaw cycle damage of asphalt concrete in spring-thawing season.
2. The freeze-thaw-scour damage model of two mixtures were built and the damage speed and damage degree of the two mixtures were quantified based on the logistic judgment model. Model parameter A\({}_{\text{max}}\) can be used to evaluate the damage degree of the mechanincal properties of asphalt cement, and the parameter x\({}_{0.5}\) can be used to evaluate the damage speed of the properties of asphalt cement.
3. Through the application of the gray rational degree theory, it found that the freeze factor had a more significant impact on the damage process of NHSS-AC compared with the soak and scour factor. The impact of the scour factor on the durability of NHSS-AC was more significant than that of the soak factor, and the effect of the scour factor and soak factor on the low temperature mechanical performance was similar.  
Figure 12: Calculation results of NHSS-AC by the gray rational degree theory.  
**Author Contributions:** Conceptualization, X.G.; Data curation, W.G. and M.S.; Formal analysis, W.G., X.G., and W.D.; Funding acquisition, X.G.; Methodology, M.S., and W.D.; Project administration, W.D.; Writing--original draft, W.G.; Writing--review & editing, X.G. and W.D.  
**Funding:** This research was funded by the National Nature Science Foundation of China (NSFC) (grant no. 51178204). This financial support is gratefully acknowledged.  
**Conflicts of Interest:** The authors declare that there is no conflict of interests regarding the publication of this paper.
## 3 Results
### Thermal Performance of the Green Composite Pieces
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Piece** & \(T_{g}\) **C** & \(T_{cc}\) **C** & **\(\Delta H_{cc}\) **(/g)** & **\(T_{m}\) **(\({}^{\circ}\)C)** & **\(\Delta H_{m}\) **(/g)** & **\(X_{C\_max}\) (\%)** \\ \hline PLA & 62.3 \(\pm\) 0.4 & 122.7 \(\pm\) 1.9 & 4.30 \(\pm\) 0.35 & 151.3 \(\pm\) 0.4 & 11.32 \(\pm\) 0.99 & 12.08 \(\pm\) 1.80 \\ PLA/20 CHF & 61.3 \(\pm\) 0.3 & 124.7 \(\pm\) 1.3 & 8.04 \(\pm\) 0.42 & 153.7 \(\pm\) 0.5 & 15.17 \(\pm\) 1.10 & 20.24 \(\pm\) 1.60 \\ PLA/20 TCHF & 61.8 \(\pm\) 0.4 & 125.9 \(\pm\) 1.0 & 7.10 \(\pm\) 0.55 & 152.6 \(\pm\) 0.3 & 15.92 \(\pm\) 0.75 & 21.24 \(\pm\) 2.02 \\ PLA/30 TCHF & 61.3 \(\pm\) 0.4 & 122.1 \(\pm\) 0.3 & 17.80 \(\pm\) 0.76 & 150.7 \(\pm\) 0.6 & 19.59 \(\pm\) 0.14 & 29.87 \(\pm\) 2.35 \\ PLA/40 TCHF & 61.3 \(\pm\) 0.8 & 121.8 \(\pm\) 0.6 & 12.27 \(\pm\) 0.94 & 149.9 \(\pm\) 0.5 & 14.35 \(\pm\) 0.59 & 25.52 \(\pm\) 1.49 \\ PLA/50 TCHF & 60.7 \(\pm\) 0.5 & 123.2 \(\pm\) 0.9 & 10.27 \(\pm\) 0.35 & 149.6 \(\pm\) 0.4 & 12.05 \(\pm\) 0.99 & 25.72 \(\pm\) 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thermal properties of the injection-molded pieces of polylactide (PLA) with coffee husk flour (CHF) and torrefied coffee husk flour (TCHF) in terms of glass transition temperature (\(T_{g}\)), cold crystallization temperature (\(T_{c}\)), melting temperature (\(T_{m}\)), cold crystallization enthalpy (\(\Delta H_{cc}\)), melting enthalpy (\(\Delta H_{m}\)), and maximum degree of crystallinity (\(X_{C\_max}\)). with the addition of 20 wt% of TCHF, whereas a negative effect was observed for the pieces filled with higher TCHF contents. It is noteworthy that a higher thermal stability was observed in comparison to the PLA composites prepared with CSS by [PERSON] et al. [71], who ascribed the decrease to both the metals present and the moisture released by the fillers, which can catalyze degradation and induce hydrolysis of the ester groups, respectively. Since lower thermal stability values were attained for the PLA/20 CHF piece, the improvement attained herein is an indication that the less thermally stable compounds, such as hemicellulose and cellulose, were successfully removed during the torrefaction step [33], leading to a material with higher lignocellulosic content and, hence, improved thermal stability. Furthermore, as expected, one can observe that adding TCHF caused the char residue to increase. Such high char content is also related to the torrefaction step, which can generate a higher percentage of carbon compared with the non torrefied lignocellulosic filler [33].
## 3 Application of PSHRisk-Tool
### Section 3: Ground Motion Prediction Equation (GMPE)  
The graphical representation of this part is coded according to the existing attenuation model to examine the effect of various parameters. Alongside, it also shows the variation of acceleration in terms of distance (\(R\)) and magnitude (\(M\)). The tool accounts for ground motion variability and allows the retrenchment of the ground motion distribution at a user-defined number of standard deviations.
# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 3 Improved Lightweight Real-Time Detection Algorithm Based on YOLOX  
### Lightweight Improvement Strategy  
As an exemplary one-stage object detection algorithm, YOLO (You Only Look Once) has seen substantial advancements in recent years. In 2021, Opensight Technology introduced YOLOX, a high-performance one-stage target detection algorithm [41]. YOLOX outperforms its contemporary competitor, the YOLOv5 series, in both speed and accuracy. The paradigm of a one-stage object detection algorithm model can be summarized as a combination of a backbone, neck, and head. Drawing inspiration from the YOLOX-l model depicted in Figure 4, this paper has undertaken lightweight modifications to meet the real-time requirements of tunnel fire detection. The improved YOLOX model is presented in Figure 5.  
In contrast to the conventional YOLOX-l model (parameters size is 54.2 MB) shown in Figure 4, the improved YOLOX model (parameters size is 52.3 MB) in Figure 5 incorporates a deep separable convolution strategy. This strategic modification significantly reduces the network parameter size, enhances the model's operational speed, and lowers computational costs [42]. Moreover, to elevate performance in visual recognition tasks, a Cross-Stage Partial Connection Layer (CSPLayer) has been introduced to enhance the quality of feature representation. The structure of the CSPLayer is depicted in Figure 5. Within this context, the DenseBlock module in Figure 5 utilizes a deep separable convolution strategy, with 'add' indicating whether the residual module is employed. Finally, the  
Figure 4: YOLOX-l target detection model.  
Figure 5: Improved YOLOX model.  
Fast Spatial Pyramid Pooling (SPPF) module, presented in Figure 5, has been introduced to address the challenge of inflated parameters in the fully connected layer without altering the size of the output feature map of the convolutional layer [43]. This addition effectively boosts processing speed.
# Integrated Gradients for Feature Assessment in Point Cloud-Based Data Sets
## 2 Theory  
### Point Clouds  
Recent advances in 3D imaging sensors, deep learning, and semantic segmentation have allowed the generation and processing of high-resolution 3D scans of the real world. These can range from small objects with extremely high resolution in the nanometre range to entire cities with centimeter accuracy. In a conventional image, all of the pixels contain information due to the nature of 2D data. Conversely, point clouds are very **sparse** as most of the volume in a 3D depiction is empty. Therefore, saving a mostly empty volume in a discrete form where each voxel has to be filled is very inefficient and not feasible for a large number of data points. However, by leveraging this, **unstructured** but more flexible format operations that require structure, such as finding neighboring points, become more complex. This is exacerbated by their **unordered** nature, making them invariant to permutation, these properties are visualized in Figure 1. Point clouds are also **irregular**; for instance, a 30 \(\times\) 30 m point cloud might have any number of points, whereas a voxel representation always has a fixed number of voxels that are known in advance [11]. Due to these challenges, conventional 2D segmentation methods must be altered significantly to be successful when applied to point clouds.
# FLINT: Flows for the Internet of Things
## 2 Case Study--Port of the Future  
The Port of the Future will be equipped with technology to cope with day-to-day challenges more efficiently [5]. Sensor measurements can be used to analyze and monitor ports in order to make better operational decisions. For example, transportation can be tracked between different port terminals to increase the coordination of traffic and better manage transport logistic chains. Furthermore, the environment, engineering structures, vehicles and vessels can be tracked in order to optimize their behaviour and contribute to sustainable ports. This either involves (1) existing data sources which have already adopted a specific data format or (2) (wireless) IoT devices.  
In order to benefit from information that is already available (1), data from existing systems, legacy systems and systems that support different standards, should be converted to a common format [6].  
Furthermore, depending on the available wireless infrastructure of the port, this data will originate from a multitude of data sources (2):  
* Multiple wireless communication networks can coexist that serve devices equipped with a single radio;
* Devices can be equipped with multiple radios that connect to different networks over time;
* Devices can have a radio that supports multiple modulation schemes.  
For example, devices equipped with a sub-gigahertz (sub-GHz) compatible radio and multiple modulation schemes, such as Long Range (LoRa), DASH-7 (Gaussian Frequency Shift Keying (GFSK)) and Sigfox (Ultra Narrowband (UNB)), can alternate between long range, low throughput and medium range, higher throughput technologies [7]. FLINT was initially developed to interconnect such networks and devices. However, multiple cases, such as the integration with Obelisk [8], made clear that its flexible design was also well suited for fast prototyping and use case support. Therefore, this section presents several modules that were developed and illustrates the evolution of FLINT from a simple tool to a scalable IoT platform.