esa-sceva/satcom-chunk-collection huggingface dataset:
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 1 Introduction
This paper aims to address these gaps by presenting the following novel contributions:  
* Based on the unique characteristics of underground construction environments, a hybrid network topology that integrates wireless sensor collection with wired transmission is proposed. A real-time routing and scheduling method is proposed, taking into account the transmission deadlines of individual sensors. This problem is proven to be NP-hard;
* We introduce algorithms based on both greedy and heuristic strategies and improve traditional shortest path algorithms to better suit the complex conditions in mining environments. Comparative analysis in various experimental setups demonstrates that the proposed algorithms significantly reduce transmission delays across a range of application scenarios. The remainder of this paper is structured as follows. Section 2 reviews related work in the field of WSNs in underground mining. Section 3 presents the system model and formalizes the problem definition. Section 4 describes the design of the proposed algorithms. Section 5 provides a detailed experimental analysis and a comparison of the algorithms' performance. Finally, Section 6 concludes the paper, summarizing the key findings and outlining potential directions for future research.
## 7 Algorithms for HPC Systems in the Exascale Era
### Parallel Solvers for Exascale Computing
Furthermore, the linear solution process, which is at the base of the solution of almost all described problems in the previous sections, requires constantly moving the sparse matrix entries from the main memory into the CPU, without register or cache reuse. Therefore, methods that demand less memory bandwidth need to be researched. There are promising linear techniques, such as coefficient-matrix-free representations [165], that totally avoid sparse matrices, or Fast Multipole Methods (see Section 7.2.4) [157]. **Mixed-Precision Arithmetic Algorithms**: Using a combination of single- and double-precision arithmetic in a calculation is known as mixed-precision arithmetic. Single-precision arithmetic is typically faster than double-precision arithmetic, but some computations may be so susceptible to roundoff errors that the entire computation is typically not possible to complete with just a single precision, so mixed-precision arithmetic can be used. In order to quantify the situations in which using mixed-precision arithmetic is advantageous for a given class of algorithms, further research is required, with a particular focus on the numerical behavior of this technique. In addition to speeding up problem-solving, mixed-precision arithmetic might also need less memory. When working with mixed-precision arithmetic, it is crucial to understand how round-off errors spread and affect computed value accuracy [157]. Section 7.2.3 provides examples of mixed-precision techniques for solving linear problems. **Fault-tolerant and resilient algorithms**: Allowing for nondeterminism in both data and operations is a beneficial--and perhaps essential--feature that makes fault-tolerant and robust algorithms possible. Algorithms based on pseudorandom number generators offer a multitude of possibilities at the extreme scale. Monte Carlo-like methods, or more in general, stochastic-based algorithms such as Cellular Automata, can identify resilient implementations by using stochastic replications, which can improve concurrency by several orders of magnitude by employing stochastic replications to find robust implementations [157] (see Section 7.2.5). Regarding fault-tolerant algorithms, a possible solution to the problem of global synchronous checkpoint/restart on extreme-scale systems is the use of localized checkpoints and asynchronous recovery. Recent work is focused on algorithm-based fault tolerance (ABFT): for example, much of the ABFT work on linear solvers is based on the use of checksum [157; 166].
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 5 The Energy Node Placement Algorithm
### Node
#### 5.3.2 The Allocation Method  
When the node receives a _FindAlternative_ request from the mobile it will check its neighbor table to find from all its upper nodes at least one alternative next-hop node. Algorithm 9 describes this method. For each upper node it finds, called a candidate alternative node, the node will communicate with it to ask if the candidate node can handle its extra resources.  
```
1:functionallocation_method(\(m_{j}\))
2:\(success=FALSE\)
3:for all\(n_{j}\in neighbor\_list\)do
4:if\(flag_{n_{j}}==TRUE\)then
5:send(\(AllocationRequest(x)\)) to \(n_{j}\)
6:wait until\(n_{j}\) replies
7:if\(reply==YES\)then
8:send(\(FA\)-SearchSuccess) to \(m_{j}\)
9:next_hop = \(n_{j}\)
10:\(success=TRUE\)
11:break
12:endif
13:endif
14:if\(success==FALSE\)then
15:send(\(FA\)-SearchFailed) to \(m_{j}\)
16:endif
17:endfor
18:endfunction
```  
**Algorithm 9** Allocation method for node \(n_{i}\)  
Upon receiving an _Allocation Request_ from a neighbor node, the candidate node will calculate its additional resources to check if the extra resources can be handled (see Algorithm 7, lines 3-12). The request is only accepted from the candidate node only if:  
\[x\leq\frac{R}{N_{s}+2}, \tag{2}\]  
where \(x\) is the amount of extra resources from the requesting node, \(R\) is the data rate of the network, and \(N_{s}\) is the number of the active neighbor nodes of the candidate node.  
If the extra resources \"x\" are in the range of the resources the candidate node can handle, then the reply to the requesting node is an _acceptance message_, and the node is added to the extra resources list (\(ERlist\)). However, if the extra resource cannot be handled, then the reply is a _rejection message_ without any other actions. Based on the reply of the current candidate node examine, the node will either continue its search or provide a reply to the mobile node. If the reply from one candidate node is positive, the reply to the mobile node is a _success message_ (see Algorithm 9, lines 7-12); otherwise, the reply is a _failure message_ (see Algorithm 9, lines 14-16). After the search method, the node waits for the results of the procedure from the mobile node, either a _success method message_ or _failed method message_. Upon receiving a _success method message_, the node removes the mobile node from its neighboring table (see Algorithm 7, lines 13-14) and assigns the selected node from the search as its next-hop node (see Algorithm 9, line 9). The latter is important, as without this assignment the normal function will indicate a random next-hop node, but this will not work here as the selected candidate is the one needed. However, upon receiving a _failed method message_, the node needs to deallocate its extra resources from the selected node by broadcasting a _stop process message_ (see Algorithm 7, lines 18-20).
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 2 Background
### Taxonomy
#### 2.1.5 Attribution vs. Non-Attribution  
The majority of proposed explanation techniques are attribution based methods. Attribution methods attempt to calculate the inputs of the neural network that are the most important with regards to the network's result. This can be broken further into two categories: perturbation based approaches and back-propagation based approaches. Perturbation based techniques estimate the most important features by removing one, calculating change in class score, and repeating for all features. It uses this to calculate the attribution of each feature. It then ranks feature importance by attribution. An example of this is SHAP. These are usually inefficient due to having to compute many iterations. Gradient-based methods conduct a similar procedure except with a single forward or backwards pass. Some of these methods provide only positive contributions (e.g., CAM) and some provide positive and negative contributions (e.g., DeepLIFT). In general, these methods produce results faster due to having to perform only one pass.
# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 3 Materials and Methods
### Avoid-Leader-Zone
#### 3.3.4 Path Planning  
A key difference between the Behavior Manager and the traditional Motor Schema approach is how motor responses are generated and acted upon. While the Motor Schema approach uses potential field response vectors and navigation, the Behavior Manager leverages layered costmaps with Vector Field Histogram (VFH) path planning. VFH is a path planning technique that was developed specifically to address the inherent limitations of potential field methods, such as U-shaped obstacle traps, oscillation in narrow corridors, or the inability to pass between closely spaced objects [43]. The VFH method represents the obstacles around a robot with certainty values in a Cartesian histogram grid. Data reduction over the histogram grid reduces it to a polar histogram of angular sectors around the robot with associated polar obstacle densities. Based on the desired goal and polar obstacle densities, drive and steering commands are given that best avoid obstacles while making progress towards the goal [44]. VFH was chosen as our path planning technique because of the aforementioned histogram grid. By using the combined layered behavioral costmap as the histogram grid, we naturally integrated costmap-based perceptual schemas with path planning, thereby avoiding potential field methods and their pitfalls.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 3 Sources and Detectors for Lidar Imaging Systems in Autonomous Vehicles
### Single-photon avalanche photodiodes
Statistical measurements of the time-dependent waveform of the signal may be obtained by measuring the time distribution of the received pulses, using the time-correlated single-photon counting (TCSPC) technique [21]. SPADs are used in a number of lidar applicationsand products, taking advantage of its extreme sensitivity, and may be found individually, or in 1D or 2D arrays. Their main drawback is their sensitivity to large back reflections which may saturate the detector and leave it inoperative for short periods, an event easily found in real life where large retroreflective signs are present almost everywhere on the road as traffic signs, as discussed in Section 2.2.2.a when speaking of flash imagers.
## 5 Discussion
### Generalization to Motion Blur and Real-World Degradations  
The DRFAN is currently trained using bicubic-downsampled data, which do not fully represent real-world degradations such as motion blur, sensor noise, or defocus. When applied to motion-blurred images, the DRFAN exhibits a decline in edge sharpness and structural continuity. This limits its applicability in dynamic or handheld imaging scenarios.  
To improve robustness, we plan to incorporate synthetic degradation kernels (e.g., motion blur simulation) and possibly adopt domain adaptation techniques to fine-tune the DRFAN on real degraded images without requiring a paired ground truth. This direction is essential for broader deployment in uncontrolled visual environments.
# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 3 Methods  
### Dataset  
The dataset used to train the modified YOLOv8 model was the FracAtlas dataset [32], a musculoskeletal bone fracture dataset used widely in many published works, manually annotated in COCO, VGG, YOLO, and Pascal VOC formats. It consists of 4083 images that have been manually annotated; among them, 717 images depict fractures. It is divided into X-ray images showing fractures and non-fractures and subdivided into fracture classification and localization. The preprocessing stage of the dataset included various steps. The first step was to divide the dataset into two sets: the hand fracture-exclusive and full-body fracture sets. Then, each set was divided into three subsets: a training set, a validation set, and a testing set, as seen in Figure 3.  
Figure 2: Detailed illustration of the expansion of the YOLOv8 Algorithm Architecture, with modules present within the Backbone, Neck, and Head. They include CZf, ConvModule, DarknetBottleneck, and SPPF for faster and more efficient real-time object/feature detection from images.  
Each set consists of two subfolders: the image and annotations folders. The data of the training set is then augmented using various techniques, including resizing the image to a manageable size. Additional preprocessing steps include normalization and data augmentation, such as rotation, flipping, contrast adjustment, and noise reduction, to enhance the image quality.  
The dataset was divided into a training set comprising approximately 80% of the images, a validation set of 12%, and a testing set corresponding to the remaining 8%. This distribution ensured that each subset maintained a similar fracture ratio to non-fracture images, promoting balanced training and evaluation. Hand fractures were then isolated from the entire dataset to refine the analysis further. Separate training and validation splits were then explicitly created for the hand fracture subset to ensure targeted and practical model training in this category.  
The experimental setup implemented the model, which includes the Deep Learning Framework (PyTorch 2.4.1, CUDA 12.1), utilizing Jupyter Notebook for local Python 3.11 code creation and editing. The YOLOv8 models were executed on an NVIDIA RTX 4070 GPU, leveraging its accelerated computing capabilities to handle the intensive processing tasks. This setup provided a robust environment for training and fine-tuning the models, enabling efficient experimentation and rapid iteration through 1000 epochs for each test.
# Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping
## 5 Discussion  
### Computational Complexity  
In our proposed method, two major computations are required for its implementation: (1) Initial creation and daily update of a base graph and (2) random walks on the graph. A unity graph is generated by summing the sequential click activities by users for a day, and a base graph is generated by combining \(T\) unity graphs, where \(T\) is the number of days that we consider to update the graph. Therefore, the computational complexity of generating a unity graph is \(O(c)\), and that of generating a base graph is \(O(Tc)\), where \(c\) is the number of click activities for a day. Figure 10a shows the time elapsed to generate unity graphs for 12 categories with different numbers of click activities. We can observe that the computational time linearly increases as the number of clicks increases, and approximately 15 s were spent for more than 70,000 clicks. The computational time for random walks is proportional to the number of nodes on the graph (\(n\)), and the number of random walks (\(N\)), which results in \(O(Nn)\). As illustrated in Figure 10b, the elapsed time linearly increases with respect to the number of nodes and the number of random walks. Our experiments show that 5000 random walks on 3500 nodes took only approximately 20 s. This empirical study on computational time proves that our proposed method is tangible and useful in practice. These experiments were performed on a computer with an Intel i5 processor running at 2.60 GHz using 8 GB of RAM, on x64 Windows 7.  
Figure 10: Analysis on computational time of proposed method. (**a**) Time elapsed to construct a unity graph. (**b**) Time elapsed for random walks.  
Figure 9: Comparison with existing collaborative filtering (CF) methods. (**a**) Without category purchase probabilities. (**b**) With category purchase probabilities.  
It would be worthwhile to compare the computational complexity of the proposed method with those of existing methods. For a fair comparison, let \(m\) be the number of users and \(n\) be the number of items. The complexity of training part of our method is \(O(nm)\), which is the same with that of the user-based CF under the reasonable assumption that \(Tc\ll nm\). The prediction part becomes \(O(Nn)\) as explained above. Table 4 compares the computational complexities of the methods tested in our experiments, where the complexities of existing methods came from [43]. For the Funk SVD, \(k\) is the number of latent features.
## 2 Results
### Binding of ppGpp and cAMP to CRP  
_E. coli_ CRP showed an intrinsic fluorescence when excited at 280 nm. The fluorescence intensity was decreased by the presence of ppGpp (Figure 1a). The dissociation constant of ppGpp binding to CRP was calculated as 35 \(\pm\) 3 \(\upmu\)M based on the changes in the areas below the spectra (Figure 1b). The binding of various ligands (including cAMP) was also investigated (Supplementary Figure S1) and the results are summarized in Table 1. The dissociation constant for cAMP to CRP was determined as 22 \(\pm\) 3 \(\upmu\)M, which was consistent with previous reports [8; 9; 23]. The calculated dissociation constants for cGMP and ATP were 17 \(\pm\) 1 and 33 \(\pm\) 4 \(\upmu\)M, respectively, suggesting that ppGpp, cGMP, and ATP bound to CRP with a similar affinity as cAMP. Based on equilibrium dialysis utilizing radioactive ligands, the binding of cGMP and cAMP to CRP (with a similar affinity) was reported previously [8], suggesting that the method described here was valid. The current method was much simpler and not limited by the availability of radioactive ligands. ATP binding to CRP with a high affinity was an unexpected observation.
## 2 System Model
### Metrics of Interest  
In this paper, we are interested in two types of metrics for the proposed slicing scheme: (i) user-centric and (ii) system-centric. The first types of metrics include session drop and session pre-emption probabilities as a function of the number of traffic classes, resource reservations, and priority order. The main system-centric metric of interest is the efficiency of resource utilization.
# Study of the Properties of Human Milk Fat Substitutes Using DSC and GC Methods
## 3 Results
### Kinetic Analysis of Oxidation  
If there is a need to determine the oxidative stability of fats stored and processed under various conditions, it is necessary to provide their kinetic parameters [17]. In Table 4, the experimental initial (onset) oxidation temperatures (\(T_{on}\)) obtained at five heating rates are shown. The \(T_{on}\) experimental values as a function of heating rates (\(\beta\)) were recalculated on ruthless onset temperatures (\(T_{on}\), K). Substituting values of \(T_{on}\) and \(\beta\) into the Equation (1):  
\[Log\ \beta=a\ \left(\frac{1}{T_{on}}\right)+b \tag{1}\]  
where \(a\) and \(b\) are adjustable coefficients, we found a linear dependence (R\({}^{2}\) > 0.96 in each case).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Sample** & **AV 1 (mg KOH/g)** & **PV 1 (mmol O\({}_{2}\)/kg)** & **IT 1 (min)** \\ \hline L & 0.86 \(\pm\) 0.06 b & 2.7 \(\pm\) 0.2 a & 70.9 \(\pm\) 0.8 a \\ RO & 0.58 \(\pm\) 0.05 a & 3.4 \(\pm\) 0.1 b & 72.0 \(\pm\) 0.6 a \\ LR70\_4 & 8.7 \(\pm\) 0.1 d & 5.7 \(\pm\) 0.2 e & 27.2 \(\pm\) 1.4 d \\ LR70\_8 & 11.0 \(\pm\) 0.6 e & 4.8 \(\pm\) 0.3 d & 34.7 \(\pm\) 0.2 c \\ LR70\_24 & 5.4 \(\pm\) 0.2 c & 4.1 \(\pm\) 0.3 c & 53.0 \(\pm\) 1.8 b \\ \hline \hline \end{tabular}  
* Data expressed as means \(\pm\) S.D. The different lower-case letters (a–e) in the same column indicate significantly different values (\(p\) < 0.05).  
\end{table}
Table 3: Acid value, peroxide value, and oxidation induction time of raw materials and mixtures after interestrification.  
Under an assumption that the degree of reaction is a constant value independent of the heating rate when a DSC curve reaches its peak, activation energy (\(E_{a}\)) was calculated for each sample (Equation (2)):  
\[E_{a}=-2.19\ R\ \frac{d\log\beta}{d\Big{(}\frac{1}{T}\Big{)}} \tag{2}\]  
where \(\beta\)--the heating rate (\({}^{\circ}\)C/min), \(R\) is the gas constant and \(T\)--temperature (K).  
According to the Equation (3), other kinetic parameters, such as the pre-exponential factor (\(Z\)) of fat oxidation, was calculated:  
\[Z=\ \frac{\beta E_{a}e^{\frac{E_{R}}{RT}}}{RT^{2}} \tag{3}\]  
The regression analyses of the data and kinetic parameters for the SL are listed in Table 5.  
The activation energies calculated from the experimental data ranged from 89.40 kJ/mol to 92.63 kJ/mol and the pre-exponential factors ranged from 1.93 \(\times\) 1010/min to 4.41 \(\times\) 1010/min. There were no statistically significant differences among the activation energies in the mixture interesterified for a different period of time.
# P-Distill: Efficient and Effective Prompt Tuning Using Knowledge Distillation
## 2 Preliminaries
### Prompt-Based Learning Methods  
Prompt-based learning methods have emerged as an efficient alternative to full-model fine-tuning, especially for PLMs [6; 10]. These methods use prompts to guide the model predictions for specific tasks. Several approaches [11; 12] employ discrete prompts, which are fixed templates added to the input. For example, in sentiment analysis, a template might be \"This text [_Input Text_] expresses a [_MASK_] sentiment\". However, discrete prompts are limited in that their performances significantly depend on template selection. Advanced approaches, such as prefix-tuning [13] and P-tuning [10], use continuous prompts that are trainable embeddings independent of the model vocabulary. Particularly, P-tuning v2 [6] attaches continuous prompts to each layer of the model, thereby influencing its behavior and enhancing its performance in downstream tasks. These continuous prompts are integrated into the attention mechanism of the transformer model as follows:  
\[Att(x)=softmax\bigg{(}\frac{Q(P_{k}:K)^{T}}{\sqrt{d_{k}}}\bigg{)}(P_{v}:V), \tag{2}\]  
where \(P_{k}\in R^{n_{p}\times d}\) and \(P_{v}\in R^{n_{p}\times d}\) are the continuous prompts added to the key and value vectors, respectively, the colon denotes the concatenation of these prompts with the key and value matrices. The dimension \(n_{p}\) indicates the lengths of the prompts, and \(d\) represents the dimensions of the key and value vectors. This integration enables the model to influence layers closer to the output, significantly affecting the final predictions.  
A significant disadvantage of P-tuning v2 is the necessity for long continuous prompts to achieve maximum performance, resulting in a reduced input token space. Figure 1 demonstrates the influence of continuous prompt length on model performance across diverse NLP tasks, indicating that more complicated tasks often need longer prompts.  
Figure 1: Performance variation in P-tuning v2 across tasks based on length of continuous prompts.  
This discovery inspired our investigation into prompt compression, in which we wanted to maintain model efficacy while minimizing prompt length. In Section 3, we present P-Distill, an innovative method that use knowledge distillation to address the inefficiencies linked to long prompts.