esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To investigate the dynamic response of fractured rock under cyclic loading and unloading, a WHY-300/10 microcomputer-controlled electro-hydraulic servo universal testing machine was used to conduct uniaxial cyclic loading and unloading tests. Simultaneously, acoustic emission (AE) and a CCD high-speed camera were employed to monitor the fracturing characteristics of sandstone. The mechanical properties, energy evolution, AE characteristics, and deformation of 45\({}^{\circ}\) sandstone were analyzed. The results indicate that as the load cycle level increases, both the elastic modulus and deformation modulus exhibit a \"parabolic\" increase, with a rapid rise initially and a slower rate of increase later. The damping ratio generally shows a decreasing trend but tends to rise near the peak load. The total energy, elastic energy, dissipated energy, damping energy, and damage energy all follow exponential function increases with the load level. The b-value fluctuates significantly during the stable crack propagation phase, unstable crack propagation phase, and peak phase. When the FR (Felicity ratio > 1), the rock is relatively stable; when the FR (Felicity ratio < 1), the rock gradually extends towards an unstable state. The Felicity ratio can be used as a predictive tool for the precursors of rock failure. Shear fractures dominate during the compaction and peak phases, while tensile fractures dominate during the crack propagation phase, ultimately leading to a failure characterized by tensile fracture. High-speed camera observations revealed that deformation first occurs at the tips of the prefabricated cracks and gradually spreads and deflects toward the ends of the sandstone. This study provides theoretical support for exploring the mechanical behavior and mechanisms of fractured rock under cyclic loading and unloading, and it has significant practical implications.  
cyclic loading and unloading; acoustic emission; high speed camera; rock
# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 3 Methodology
### Top-Level Classification Block
#### 3.1.2 Classification Network  
The normalized feature vectors are processed by a recurrent network which has sequential input and classification type output. The network (Figure 2), contains two layers of fully connected (FC) cells with ReLU activation function, a stateful LSTM cell [35] and a skip connection of a FC+ReLU concatenated before the last fully connected cells, terminating with a SoftMax function. To smooth the output and filter out spikes that have no physiological importance, temporal average pooling is used before the final classification output is generated.
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 4 Experiments
### Parameter Setting  
We adopted ResNet as the backbone of MIHE-Net, with the original ResNet fully pretrained on ImageNet. The positive sample matching threshold was set to 0.5 and the detection head confidence was configured as 0.6. The proposed MIHE-Net was optimized using the Adam optimizer with a momentum of 0.9 and a weight decay coefficient of 0.0001. All models underwent 200 epochs of training, starting with an initial learning rate of 0.01, which was decayed to 0.001 after 80 epochs. A batch size of 32 was employed throughout the training process. To ensure the reproducibility of the experiments, we fixed the random seed to 42. Experiments were conducted on a server based on the PyTorch framework with four RTX 4090D GPUs. The Python version was 3.8.13 while the PyTorch version was 1.10.1, along with torchvision 0.11.2 compatible with PyTorch (for data loading and transformation), CUDA 11.3 supporting GPU-accelerated training, numpy 1.21.5 for array operations, OpenCV-python 4.5.5 for image processing, and scikit-learn 0.24.2 for calculating the evaluation metrics. RetinaNet, a simple yet effective one-stage detector, serves as the baseline; it is worth noting that introducing any module may increase computational complexity.  
To ensure the fairness and comparability of the experimental results, all comparative models were trained and tested in exactly the same hardware and software environments. Specifically, all models adopted the same dataset splitting strategy as mentioned above, with consistent training and test sets to avoid biases caused by different data divisions. In addition, all models use the same training hyperparameters, including the number of training epochs, initial learning rate and its decay strategy, batch size, optimizer parameters (momentum and weight decay), and fixed random seed. This ensures that the observed performance differences are solely due to the inherent characteristics of the model architectures rather than to changes in experimental settings.
## 3 Results and Discussion
### SEM Analysis  
Figure 2a,b present the SEM images of Fe\({}_{3}\)O\({}_{4}\) MNPs and [Poly(aniline-co-o-toluidine)/Fe\({}_{3}\)O\({}_{4}\)] MNPs, respectively. Upon analysis, the nanoparticle sizes appear to fall within the range of 29.95 nm to 49.60 nm. Significantly, the size distribution is narrow, pointing toward a controlled synthesis and a uniformity in the particle size. Furthermore, a close examination of the particle morphology shows a consistent and well-defined shape throughout the samples. This uniform morphology could potentially contribute to enhanced physical and chemical properties in the resulting composite materials, making them suitable for corrosion protection. The detailed features highlighted in the SEM images underline the material's potential for use as an effective corrosion protection material.
## 1 Introduction  
In distributed information systems, the dependencies and connectivity access relationships between data center infrastructure, servers, storage, networks, systems, databases, and applications become exceedingly complex and invisible due to factors such as resource pooling and insufficient cross-discipline operational and maintenance data sharing. Each professional monitoring system runs independently, resulting in a prominent silo effect and insufficient linkage mechanisms, making it difficult to accurately locate the underlying objects that cause exceptions under a particular application. Current research focuses on knowledge graph and indicator correlation analysis methods. However, knowledge graph technology has a high complexity and is only effective in local scenarios such as networks, and therefore, it is still not ready for practical use. Indicator correlation analysis lacks configuration correlation information between objects, thus its accuracy is limited. This paper proposes a basic fault location method based on dynamic operation maps and alarm common point analysis. First, a distributed information system dynamic operation map is constructed, including topology information, node mutual visits, application high-availability deployment, covering applications, systems, and networks, providing topological relationships for fault location. Meanwhile, the troubleshooting process triggered by a single abnormal business flow is studied, including database access flows, read/write I/O flows, and basic component mutual visits. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, physical and logical connection common points, and access flow common points, thus positively locating the possible root cause of the fault. This method is simpler and more efficient than knowledge graph methods and more accurate than indicator correlation analysis methods due to the addition of configuration correlation information.
## 2 Problem Description
Based on (7)-(9), the optimal time-triggered HJI equation becomes  
\[\
abla V^{*\mathsf{T}}(x)f(x)+x^{\mathsf{T}}Qx+u_{M}^{2}\bar{R}\ln \Bigl{(}\underline{1}-\tanh^{2}(\Psi)\Bigr{)}-\frac{1}{4}\gamma^{-2}\
abla V^{ *\mathsf{T}}h(x)h^{\mathsf{T}}(x)\
abla V^{*}(x)=0 \tag{11}\]  
From (11), it is difficult to solve its solution. In addition, since the dynamic system \(f(x)\) is unknown, it further increases the difficulty of solving the HJI equation. Thus, we prefer to introduce the IRL technology to obtain the solution to the HJI equation without requiring the system dynamic \(f(x)\). The learning process of IRL mainly includes the following steps in Algorithm 1. ```
1: Choose the initial admissible control laws \(u_{0}\), \(v_{0}\). 2: Policy Evaluation. During the interval time \([t-T,t]\), the value function \(V^{[i]}(x)\) is obtained by solving the following Bellman equation \[V^{[i]}\bigl{(}x(t-T)\bigr{)}=\int_{t-T}^{t}\big{(}x^{\mathsf{T}}Qx+M(u^{[i]} )-\gamma^{2}v^{[i]\mathsf{T}}v^{[i]}\big{)}d\tau+V^{[i]}(x(t))\] (12)
3: Policy Improvement. Update the control law and disturbance law by \[J(x(t),u,v)=\int_{t}^{\infty}U\bigl{(}x(\tau),u(\tau),v(\tau)\bigr{)}d\tau \tag{4}\]0 (13) \[J(x(t),u,v)=\int_{t}^{\infty}U\bigl{(}x(\tau),u(\tau),v(\tau)\bigr{)}d\tau \tag{4}\]1 (14)
```  
**Algorithm 1** IRL algorithm. **Remark 1**. : _The IRL algorithm is an improvement over the policy iterative (PI) technique. Compared with the standard Bellman Equation (4), the IRL-based Bellman Equation (12) does not involve system dynamics \(f(x)\) and \(g(x)\)._  
However, in the IRL algorithm, the control policy is updated at \(t\) and applied to the next interval \([t,t+T]\) of the system. It is so-called periodic sampling or time-triggered control (TTC). By the TTC, the control law and disturbance law are periodically updated, which may cause large numerous resource consumption and computation costs for the network system with limited bandwidth. Therefore, an event-triggered ADP is applied to solve the HJI equation for the \(H_{\infty}\) control problem.
## 3 Lightweight Cryptography for Smart Objects
### Lightweight Cryptographic Hash Functions
#### 3.3.1 DM-PRESENT and H-PRESENT  
The authors in [47] propose DM-PRESENT and H-PRESENT, two lightweight hash functions based on the PRESENT block cipher. DM-PRESENT is a 64-bit hash function and comes in two versions: DM-PRESENT-80 and DM-PRESENT-128, depending on which cipher (PRESENT-80 or PRESENT-128) is used as a basis. H-PRESENT (namely H-PRESENT-128) is a 128-bit hash function based on the PRESENT-128 block cipher. In their work, the authors also consider the problem of constructing longer hash functions based on the PRESENT block cipher in order to improve the security level.
# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 4 Integrating TinyML with Network Technologies
### LPWAN and TinyML on Embedded Devices  
Low power wide area networks (LPWANs) aim to reduce energy consumption but at the same time to increase distance coverage. As indicated in Figure 17, LPWAN networks tend to have low data rate ratio vs. power consumption but sufficiently high range while in some cases the range exceeds more than 10 kms.  
TinyML is an emerging paradigm that efficient ML algorithms can be deployed on wearable devices [159]. However, utilizing such networks in a federated learning (FL) scenario can lead to the creation of an ML model from decentralized data and in a distributed way. This is the fundamental concept of FL where a variety of edge devices collaborate so as to build a global model using only local copies of the data and then each device downloads a copy of the model and updates the local parameters [160]. Finally, the central server aggregates all model updates and proceeds with the training and evaluation without exchanging data to other parties. Table 7 depicts the comparison of LPWAN technologies as per various parameters. Furthermore, based on the literature of modern network technologies and embedded systems we present the scalability, de  
Figure 17: Comparison of network protocols.  
battery life, security and quality of service based on the network technology that is being utilized. In Table 8, these traits are further shown.  
As per the battery life, TinyML outperforms ordinary ML techniques as the models can run on embedded devices. Cost efficiency is better in TinyML as only one microcontroller is required compared to a PC. Scalability is higher in ordinary ML applications as more computing power is available. Robustness is higher on TinyML deployments as in the case when a node is removed, all information remains intact while on the ML case is server-side based. The deployment is better on ML models as there are many paradigms available online and more widely used. The performance metric is higher on ML cases as TinyML technology has emerged and there are not much models. Lastly, security is higher on TinyML deployments as the information remains within the embedded device and there are no exchanges between third parties.
# Digital Forensics Readiness in Big Data Networks:
## 5 Proposed Framework
### Research Question 2 Insights  
To address RQ 2, an IR-script was developed. This script offers a comprehensive solution for streamlining digital forensics investigations when executed either on a Linux-Hadoop cluster's NameNode or an image of the NameNode. It is designed to be user-friendly for investigators of all experience levels, regardless of the diverse configurations of Linux-Hadoop environments. Developed using Bash shell scripting, standard Linux utilities, and Hadoop-specific commands, the script automates the retrieval and cataloguing of crucial supporting forensic information, making it easier to initiate investigations by abstracting the complexities of Hadoop environments. Additionally, the script is a valuable tool for network administrators and other IT personnel in their day-to-day tasks and troubleshooting, as will be demonstrated in its usability and functional evaluation.  
The data artefacts extracted by the script include information about the Linux operating system, such as OS information, uptime, load averages, promiscuous mode status, potential rootkits, file system configurations, mount points, partitions, open ports, disc usage, and network settings. It also captures critical specifics of the Hadoop cluster, including the Hadoop version, configuration files, HDFS version, Hadoop log directories, environment variables, SSH configuration, DNS configuration, detailed cluster metrics, and active processes. Furthermore, the script is equipped with functionalities to detect installed IDS within the Linux-Hadoop environment and extract relevant log information. The script significantly enhances the efficiency and effectiveness of forensic investigations in complex Hadoop systems, proving invaluable in both the early and later stages of the investigation. By automating these tasks, the script enhances the investigation process, ensuring consistent and accurate data collection across diverse Hadoop configurations.  
Moreover, the IR-script was developed in two versions: one that displays output in CSV format for further analysis in other tools, and another that produces well-titled documents and execution logs in text format. The text format improves readability and transparency by organising the retrieved information into accessible documents titled 'Hadoop_Info.txt', 'OS_Info.txt', and'script_log.txt'. Alternatively, the CSV format produces 'Hadoop_Info.csv', 'OS_Info.csv', and'script_log.csv' for further analysis. The choice between formats depends on the investigator's needs. These output formats provide investigators with well-documented supporting evidential information essential for the further analysis of the Linux-Hadoop environment. The configuration code for the IR-script is provided as part of the Supplementary Materials provided for this paper.
# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 4 Experimental Evaluation
### Validation on Other Datasets  
In order to fully validate the generality and robustness of the proposed method, 962 tiny object images are selected as experimental data in the public dataset TinyPerson, of which 696 images are used as the training set and 266 images are used as the validation set, and the training and validation sets are strictly independent. The proposed methodology was compared experimentally with the original YOLOv8s algorithm, YOLOv7-tiny [58] algorithm, YOLOv5s algorithm, and YOLOv3-spp algorithm. The outcomes are presented in Tables 3 and 4 for reference.  
Table 3 evaluates the detection accuracies through comparative analysis achieved by the different methods. The proposed method outperforms YOLOv3-spp, YOLOv5s, and YOLOv7-tiny in terms of mAP0.5 and mAP0.5:0.95 while maintaining real-time performance. This indicates that the proposed method exhibits superior recognition performance and robustness across various types of tiny objects.  
Table 4 compares the recognition results of the proposed method for each category. The empirical evidence substantiates that the proposed methodology exhibits commendable recognition accuracy across diverse categories.  
More intuitively, four labeled images with two categories, Earth_person and Sea_person, are shown in Figure 7. The recognition results of YOLOv3-spp algorithm (Figure 7a), YOLOv5s algorithm (Figure 7b), YOLOv8s algorithm (Figure 7c), and SP-YOLOv8s (Figure 7d) are labeled in the figure. SP-YOLOv8s algorithm has a better recognition accuracy than YOLOv3-spp, YOLOv5s, and YOLOv8s.
## 4 Application Example  
An application example is analyzed to illustrate the use of the proposed model and demonstrate its capabilities in temporary facility layout planning. The input information for the planning includes: (1) the relative importance judged by the selected experts in the in-depth investigation; and (2) the performance assessment for each criterion in the given alternatives. The proposed framework is applied to a real-life multistory building project. On the jobsite, the construction needs the assistance of a set of temporary facilities, which include the storage facilities, workshops, tower cranes, parking lots, field offices, equipment storage, material storage facilities, and garage building. In this case, as shown in Figure 3, three potential layout planning alternatives are predetermined. The detailed positions and spatial relationships for each alternative are illustrated in Figure 3, and the rectangles with different numbers represent different facilities. As one of the most important temporary facilities on site, tower crane positioning is a critical temporary facility task which directly influences the performance of the assisted activities. For example, in Alternative 1, the tower crane is placed to the west of the constructing building, while it is placed to the southwest of the building in Alternative 2. In Alternative 3, it is placed to the northwest of the building. The activities supported by the tower cranes in the three alternatives are different, and the activity performance also varies with the alternative changing. Furthermore, there are another twelve temporary facilities to be positioned on the jobsite. It remains a complex decision-making task to select the optimal layout plan and to properly optimize the locations of temporary facilities. In order to support planners and decision-makers in the challenging decision task, the proposed framework was applied to identify the optimal solution for temporary facility layout in this case. The relative importance among different criteria was judged by eight selected construction superintendents, managers and experts. The work experiences and background of the selected experts are shown in Table 7.  
Through the detailed descriptions of the proposed ANP-based framework, the relative importance of the criteria was assessed through a questionnaire investigation. The average value of the results from eight experts was utilized as the input of the model. The model is solved through the Super Decisions software to analyze and evaluate the judgment matrices [70]. Then, the unweighted supermatrix and limit supermatrix were obtained from the evaluation, as shown in Tables 5 and 6. The priorities were
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing
## 2 Transient Model of Annular Pressure Caused by Hydration Heat during Well Cementing
### Initial and Boundary Conditions  
1 The significant period of cement hydration starts after it is placed in the wellbore, and the initial degree of cement hydration is regarded as zero:  
\[\left.\alpha\right|_{t=0}=0 \tag{21}\]
2 The initial temperature in the formation can be described by the thermal gradient:  
\[T_{e}(z)=T_{g}+zg_{G} \tag{22}\]
3 The formation at infinity determines the initial temperature:  
\[T_{i,M}=T_{e,i} \tag{23}\]
4 Radial heat transfer occurs on the border between the wellbore and the formation:  
\[\left.\frac{\partial T}{\partial t}\right|_{x=r_{w}}\rho_{c}c_{e}r_{w}\Delta x \Delta\theta\Delta z=\left.\Delta\theta r_{w}U_{a}(T_{a}-\left.T\right|_{x=r_ {w}})\Delta z+\left.\frac{\partial T}{\partial x}\right|_{x=r_{w}}k_{e} \Delta x\Delta\theta\Delta z \tag{24}\]\(\mathfrak{3}\) Before the cement-setting process, the cement is pumped and circulated into the wellbore. The initial temperature of the cement can be described by the temperature prediction model during the cement circulation stage [23]:  
\[\frac{1}{v_{c}}\frac{\partial T_{c}}{\partial t}+\frac{\partial T_{c}}{\partial z }=\frac{2\pi r_{ci}U_{c}}{c_{f}w_{c}}(T_{a}-T_{c}) \tag{25}\]  
\[\frac{1}{v_{a}}\frac{\partial T_{a}}{\partial t}-\frac{\partial T_{a}}{ \partial z}=\frac{2\pi r_{w}U_{a}}{c_{f}w_{a}}(T_{c,0}-T_{a})-\frac{2\pi r_{ci }U_{c}}{c_{f}w_{a}}(T_{a}-T_{c}) \tag{26}\]
## 5 Discussion
### Practical Implications for Crisis Response
#### 5.3.2 Cost Optimization for Essential Components  
To address the high costs of essential components, our framework integrates strategies that balance performance and affordability:  
1. Hardware cost flexibility: Optimized for mid-tier GPUs (e.g., NVIDIA RTX 3060, Section 4.6) and compatible with cloud pay-as-you-go services, reducing upfront investments by scaling costs to actual usage.
2. Open-source toolchain: Critical components (PyTorch, OpenCV) use open-source software, eliminating licensing fees while maintaining efficiency gains (e.g., 50% reduced computational load, Section 4.6).
3. Modular deployment: Incremental adoption starts with core text-based functionalities on standard CPUs, scaling to multimodal capabilities as resources allow, lowering initial investment barriers. These strategies reduce costs at every stage without sacrificing framework robustness.