esa-sceva/satcom-chunk-collection huggingface dataset:
# Name-Based Security for Information-Centric Networking Architectures
## 3 System Design  
### System Entities and Namespace Structure  
Our system design considers real world entities that own some content (_content owners_) and want to share it with other users. A content owner owns some content name roots. Users are generally anonymous. However, there are cases where users have to be identified (e.g., in order to apply access control); in these cases, user names, similar to content name roots, are considered. Content items are stored in network locations that may or may not belong to the administrative realm of the content owner. Content items are requested and retrieved using standard ICN functions.  
Our design assumes that the namespace of content names is organized in Direct Acyclic Graphs (DAGs), where the root of each graph, i.e., the name root, is globally unique. The granularity and the semantics of the names are application-specific. Throughout the paper, for illustrative purposes, we consider the case of a file sharing application. Suppose a user in this application, that owns the content name root \"User A\": the namespace in that case can be organized as in Figure 2. In this example, \"User A/Videos/Holidays\" and \"User A/Videos/Graduation/Party/Chunk 1\" are two valid names that share the same root, as well as the same name \"prefix\". As we will discuss in the following sections, a security solution can be applied to a whole name prefix.
###### Abstract  
Indigenous microbial enhanced oil recovery technology by selective nutrient injection is a potential alternative that leads to oil production improvement in low-permeability oil reservoirs. Nutrient flooding in oil reservoirs can shift the balance of microorganisms within a population; an in-depth exploration of this phenomenon can enable us to selectively activate particularly beneficial microbial species for enhancing oil recovery. In this study, high-throughput sequencing was employed to analyse indigenous microorganisms (e.g., archaea and bacteria) in an oil production well (W226), compared to a control well (W202), in the Xingzichuan Oil Recovery Plant (Ansai, Shaanxi, China). According to alpha diversity analysis and community composition, the nutrient injection exhibited a significant impact on indigenous archaea at the genus level. The predominant archaeal genus _Methanolobus_ (more than 66%) in the control well shifted to _Methanocalulus_ (50.8%) and _Methanothermococcus_ (30.6%) genera in the oil production well. Conversely, the activators increased bacterial community richness but reduced its evenness. Bacterial community analysis at the genus level revealed that nutrient injections significantly increased specific populations with the potential to emulsify, lower interfacial tension, and lower oil viscosity, including the genera _Arcobacter_, _Halomonas_, _Thalassituus_. At the same time, some microbial species that are harmful for the oil recovery process (e.g., the sulphate-reducing bacteria _Desulfovirus_, _Desulfocurus_, _Desulfocarbo_, and _Desulfoglaeh_), were inhibited. In conclusion, nutrient flooding reduced the abundance of harmful microorganisms and increased beneficial functional microbial populations linked to beneficial functions, contributing to the enhancement of oil recovery in low-permeability oil reservoirs.  
Keywords:indigenous microbial community structure; high-throughput sequencing; microbial community structure; functional microorganisms; nutrient injection +
Footnote †: journal: Applied sciences
# An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8
To address the above problems in insulator fault diagnosis, this paper proposes an insulator fault diagnosis algorithm YOLOv8-DCP with enhanced feature extraction and feature fusion. In this study, the targets to be detected are classified into four categories: insulators and three types of faults. The category \"insulator\" refers to the entire insulator string, regardless of whether it has defects. The three types of faults are self-explosion, breakage, and flashover. The proposed algorithm allows for direct detection of insulator images to achieve fault diagnosis and localization. Comparative analysis with various mainstream algorithms demonstrates that the YOLOv8-DCP algorithm can effectively identify multiple types of insulator faults and accurately localize them. The main contributions of this paper are as follows:  
1. This paper proposes an algorithm based on multi-mechanism optimization of YOLOv8 to achieve high-precision multi-class fault diagnosis for insulators. The C2f module of YOLOv8 is improved by using dilatable residual module and dilated re-param block and named as CW-DRB feature extraction and feature fusion module to enhance the model's multi-scale feature extraction and fusion capabilities. 2. The feature content-aware module CARAFE is introduced to replace the original upsampling layer in YOLOv8, enhancing the model's feature reconstruction capability and preserving key feature information in the feature maps. Additionally, a small-object detection layer is added to the backbone network to improve the detection accuracy of small faults. 3. Image processing methods are used to expand the image samples in the data set and establish a data set containing multiple types of faults. By comparing the performance of the algorithm proposed in this paper with other algorithms, it is verified that the algorithm can maintain a high detection accuracy when there are multiple faults in the image and the fault scales are different. The performance is better than other algorithms, and the diagnosis accuracy of multiple types of insulator faults is effectively improved. The rest of this paper is organized as follows: Section 2: Model and Methods. Section 3: Experimental and Analysis. Section 4: Discussion. Section 5: Conclusion and Future Research Directions.
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 3 Proposed Scheme
### Embedding Procedure
#### 3.1.1 Partition of blocks
* The required condition for embedding watermark bit '0' is \[z<D/u,\] (3)
* Apply CRT to modify \(\vartheta\). If the condition in Equation (3) is not satisfied, then \(\vartheta\) is modified in step 8 until the required condition is satisfied. * Reconstruct DCT block with the modified DCT coefficient \(\hat{\vartheta}\) and apply inverse DCT to the block to construct the watermarked image block. * Repeat the steps (represented in bullet form) 1-9 for all the remaining blocks until all the watermark bits are embedded. Note that the range of possible values for DC and the AC coefficients are not the same. The range of the DC coefficient is from values 0 to 2040, while for AC coefficients, it will be from the \(-1020\) to \(+1020\) in monochrome images. According to CRT, the product of the pair-wise co-prime numbers (also called the dynamic range) must be greater than the largest possible number [25]. The block diagram for embedding procedure is shown in Figure 1.
## 4 Experiments and Findings  
In this section, we provide a detailed analysis of the experiments conducted and the results obtained using the CNN-GRU-AttNet model on two distinct datasets: CSI-HAR and StanWiFi. Our aim is to demonstrate the effectiveness of the proposed model. Furthermore, we perform a comparative evaluation by assessing the performance of five baseline deep learning models (CNN, LSTM, BiLSTM, GRU, and BiGRU), along with other contemporary models, on the same datasets. This comparative analysis allows us to gain insights into the relative strengths and weaknesses of different models in the context of the given datasets.
## 4 Recent Advances and Future Directions
### Distributed Machine Learning Frameworks
These innovations ensure that large-scale DNNs can adapt to the growing demands of big data analytics, providing robust, scalable, and efficient solutions [288; 289]. In addition to these frameworks, there has been a surge in the development of specialized hardware, such as graphics processing units (GPUs), tensor processing units (TPUs), and custom application-specific integrated circuits (ASICs), which are optimized for ML tasks. These hardware advancements, coupled with software innovations, have enabled the training of increasingly complex models, such as DNNs with billions of parameters. The integration of these technologies with cloud computing platforms like AWS, Google Cloud, and Azure has democratized access to high-performance computing, allowing researchers and businesses to scale their ML workflows without significant upfront infrastructure investments [290; 291; 292; 293]. Looking forward, the continued evolution of distributed ML frameworks is expected to focus on improving the efficiency and usability of these systems. Areas such as automatic scaling, fault tolerance, and better support for heterogeneous computing environments (including edge devices) are likely to see significant advancements. Additionally, the development of more sophisticated orchestration tools that can seamlessly manage the life cycle of ML models--from data ingestion to deployment--will be crucial in enabling the widespread adoption of big data-driven ML applications [294; 295; 296; 297].
## 2 Materials and Methods
### Fabrication of Intelligent Wireless Respiratory Monitoring System  
A commercial mask made of polypropylene spun-bonded non-woven fabric with a double breathing valve was bought. The self-powered triboelectric respiratory sensor was directly screen-printed onto the innermost non-woven textile surface of the mask to form an intelligent mask for real-time respiratory monitoring. A circuit board was built for data acquisition with a sampling frequency of 1000 point/s and wireless signal transmission, which includes a low-pass multi-channel filter, an AD623 amplifier [20], an ADS1256 analog-to-digital converter [21], and a BL600-SA-32 Bluetooth module [22]. A decision tree model of the machine learning algorithm was used to enhance the recognition accuracy of respiratory behaviors. An app was programmed for mobile phone terminals. The real-time respiratory signals and statistical results for various respiration behaviors were displayed in the app user interface.
# A Simple Model for Estimating the Hydraulic Conductivity of Unsaturated Soil
## 2 Methods
### Parameter Calibration  
Four parameters of the SWCC model in this study need to be calibrated, including \(a\), \(m\), \(n\) and \(C_{r}\). These parameters can be easily determined by fitting the measured SWCC with the least-squares method. In addition, three parameters in the proposed hydraulic conductivity model should be calibrated, including the saturated hydraulic conductivity \(k_{s}\), the air-entry value \(\Psi_{a}\) and the residual suction \(\Psi_{r}\). The saturated hydraulic conductivity \(k_{s}\) can be easily measured by the conventional constant head test or falling head test. In the present study, an approach for the calibration of \(\Psi_{a}\) and \(\Psi_{r}\) has been developed based on the studies by [PERSON] and [PERSON] [41; 42]. As can be seen in Figure 1a, \(\Psi_{a}\) is generally considered to be the intersection of the horizontal line corresponding to the saturated state and the tangent line through the inflection point. Also, the residual suction is generally determined by the intersection of the tangent line through the inflection point with the straight line through the SWCC at the high suction [41; 42]. The horizontal line corresponding to the saturated state is easily determined as follows:  
\[S_{r}=1.0 \tag{21}\]  
The inflection point is the point with the maximum slope of SWCC (in the logarithmic scale), which also corresponds to the zero value of the second-order derivative of SWCC. Hence, the suction of the inflection point can be obtained by solving Equation (22):  
\[\frac{d^{2}S_{r}}{d\left(\log\psi\right)^{2}}=\ln(10)\psi\left[\frac{d^{2}S_{ r}}{d\psi^{2}}\ln(10)\psi+\frac{dS_{r}}{d\psi}\ln(10)\right]=0 \tag{22}\]  
Since the correction function \(C(\Psi)\) mainly affects the high suction range, it is reasonable to use the three parameters of [PERSON] and [PERSON]'s equation without any correction function for the resolution of the inflection point suction, which greatly simplifies the solution of Equation (22). By substituting [PERSON] and [PERSON]'s equation without any correction function into Equation (22), a simplified equation can here be obtained:  
\[\frac{m+1}{\ln\left[e+\left(\psi/\alpha\right)^{n}\right]}\frac{\left(\psi/ \alpha\right)^{n}}{e+\left(\psi/\alpha\right)^{n}}+\frac{\left(\psi/\alpha \right)^{n}}{e+\left(\psi/\alpha\right)^{n}}-1=0 \tag{23}\]  
where \(a\), \(m\), \(n\) and \(C_{r}\) are model parameters in [PERSON] and [PERSON]'s equation; and \(e\) is the natural constant. By replacing the term \(e+\left(\psi/\alpha\right)^{n}\) by \(t\), Equation (23) can be simplified as:  
\[\left(m+1\right)\left(t-e\right)-e\ln t=0 \tag{24}\]  
Equation (24) can then be easily solved using the [PERSON] iteration method, with at most five iterative steps. The inflection point suction \(\Psi_{f}\) is, thereby, obtained.
# A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems
## 2 Related Work
Moreover, it reduces the hardware requirements compare to the HTTP protocol. CoAP efficiently enhances the transmission capabilities by simplifying the existing architecture of the HTTP protocol. Depending on the implementation, CoAP requires 8-10 lesser bytes compared to HTTP [25]. Moreover, it is very easy to translate and map the HTTP services and functionalities to CoAP [26]. Keeping the above advantages in mind, CoAP is being progressively used by various firms for enabling constrained oriented services in the WoT. However, the standardization of CoAP is still not finalized. Various firms such as INRIA, Lulea, Ericsson, etc. are testing CoAP in the context of implementation and interoperability issues. Moreover, very few products such as Sensinode's Nano service use CoAP in real time implementations. However which protocol will be used as a standard for WoT communication is not yet finalized. Moreover, if CoAP does not succeed in the future for complex applications and heterogeneous communications, then it is difficult to present it as a standard. Similarly, CoAP may fail like the WAP protocol that was designed for a similar purpose [27]. However, we cannot come up with a final answer in this regard yet because CoAP is still an emerging protocol for WoT applications. The integration of CoAP with UDP is still a challenging job. Several research studies are presented to efficiently integrate CoAP with the UDP sockets. For instance, [PERSON] et al. presented a model based on linking low-end devices with a cloud-based application server [28]. The proposed model was based on the RESTful programming and CoAP protocol. The entire proposed model is based on Java scripts controlling the flow of data from resources to the cloud servers. Similarly, a Peer to Peer (P2P) and parallel implementation of resources with the Web have been developed in [29; 30], respectively. Moreover, a Java Network Launching protocol is developed to support centralization of the application instead of distribution and installation. The Java web start applications are very simple and transparent to the end user. Moreover, an end user can perform downloading and installation in just one step. However, such applications directly depend on the respective browsers. Therefore, it is essential to provide the libraries of the browser which can be used to run such scripts and plugins. In addition, Javascript applications require high-speed data transfer and servers because running multiple scripts at once imposes a delay in the process of requesting the information from a sensor mode or smart object.
## 5 Discussions and Verification  
Temperature and discharging current are critical parameters in modelling battery dynamics and have been proven to have a significant influence on SoC and remaining capacity estimations. The battery may operate under varying temperatures, including seasonal changes and day/night cycles. The heat generated during the discharge processes will also influence the battery's surface temperatures. In this section, we describe the comprehensive model proposed being tested by the data collected, under varying conditions.  
The comprehensive model simultaneously considers the influence of the CE and remaining capacity. In order to verify the correctness of the model, the mean absolute percentage errors (MAPEs) of all tested cases are shown in Figure 9. Overall, the proposed model provides an acceptable estimation result with an MAPE of 1.55% for all the cases tested. As in Figure 9, under room and high temperatures, the proposed model gives a quite satisfying SoC estimation. The estimated SoC are close to the true value, with almost all of the estimation errors being less than 2%. For the SoC estimation under low temperatures, MAPEs vary from 1.06% to 3.71%. It is easily seen from Figure 9d that the estimated SoCs slightly deviate from the true values. As the battery dynamics at low temperature and large C rate are more complicated, SoC estimation is much more difficult. To improve the estimation performance at low temperatures, one practical solution is to increase the amount of training data, particularly for batteries working at low temperatures.  
Figure 9: The comparison between the experimental data and simulation results. The experimental data are random samplings in collected records; the simulation results were estimated by the proposed model. (**a**) 0.5 C rate discharge under discharge different temperatures; (**b**) 1 C rate discharge under different temperatures; (**c**) 2 C rate discharge under different temperatures; (**d**) 3 C rate discharge under different temperatures.
## 3 Network Model  
In this paper, nodes in the network fall into two categories: a large number of static sensor nodes and a small number of mobile nodes. Static sensor nodes work as publishers, while mobile nodes are special nodes (such as people, animals or vehicles) that work as subscribers. We assume that all sensor nodes are randomly deployed in a square region, which is divided into grids of identical size, as shown in Figure 1. Moreover, we further assume some additional characteristics in our modeling:  
1. Each of the sensor nodes has a unique ID number.
2. The mobility of all the mobile subscriber nodes in the given area is assumed to follow the Random Waypoint Model (RWP) [19].
3. Since the energy of mobile subscribers can be complemented timely, we assume both the energy and the memory queue of the mobile subscribers are adequate.  
Every node can directly communicate only with the nodes in the same gird or the nodes located in the neighboring grids sharing at least one side or one corner with the grid of this node.
5. The mobile subscriber nodes cannot directly communicate with each other.
6. All sensor nodes are aware of their locations.  
In DTSN, each connective Graph formed by some sensor nodes in the network is called a community. Since the communication range of the sensor node is finite, the whole network is not connective and is divided into several communities as a result. We can see that only sensors in the same community can communicate with each other. Moreover, sensor nodes in the same community share the same community ID. An example of communities is shown in Figure 1, where all the sensors in the network are divided into three communities. However, there are also sensor nodes that do not belong to any communities in the network, named loner nodes.
# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 3 Materials and Methods
### Dataset Preparation  
#### 3.1.1 Dataset Sources  
In this project, aerial images are collected and annotated, as there is a lack of public datasets to evaluate shadow detection algorithms in aerial images [10]. A new set of low-altitude aerial images is collected to be used instead of the AISD dataset, because the publicly available dataset excludes the labeling of small-shadowed regions. The images for the dataset in this project are collected from various publicly available drone images. The collected images are described below:  
The sample image is given in Figure 2. Ten images are selected from SenseFly Drone Dataset [34], specifically from the industrial estate area, where the majority of objects that can be seen are buildings, cars, and some trees.  
Another source is the Aerial Semantic Segmentation Dataset by [PERSON] [35]. The sample image is given in Figure 3. The images from this dataset were taken from lower heights compared to the previous dataset, where the shadows of objects are bigger and more visible. The images contain shadows from various objects, mainly people, buildings, and trees. Twenty images are selected from this dataset.  
The final source of the dataset is from Mendeley Thermal and Visible Aerial Imagery [36]. The sample image is given in Figure 4. Twenty images are selected, in which the shadow parts are mainly cast by trees with high brightness of shadows.  
Figure 3: Sample image of Aerial Semantic Segmentation Dataset by [PERSON].  
Figure 2: Sample image of SenseFly Drone Dataset.  
In image selection, the images with clear shadow regions are selected to ease annotation work, as shadow masks are not included in all datasets. For preliminary investigation, we also trained a shadow model using the non-drone dataset ISTD [29] and used the model for shadow detection in aerial images. This dataset provides shadow images and their corresponding binary shadow masks.
## 3 Four-Port MIMO Antenna
### Results and Discussion
#### 3.2.3 Electric Field Distribution  
Figure 13 shows the E-field distribution of proposed MIMO antenna operating over an ultra-wideband of 26.5-42.7 GHz. It can be observed that most of the electric field is distributed among the feedline and the bottom rectangular patch for non-operating MIMO elements.