esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive In-Transit Co-Processing
### Adaptive Condition Window  
The time required to perform importance calculations and data transfers largely depends on the filters and the target block actions, which are based on the specified filter conditions. Setting static filter conditions, i.e., each condition has a non-changing, constant value, could work in some situations. However, simulation data is rarely the same between any two time steps. As such, the execution time and memory usage could vary substantially. Instead, it could be preferable to adaptively change the parameters of the filter conditionsbased on some criterion. This criterion could be based on, for example, the execution time, memory usage, or the remaining allocated time on a compute cluster. We refer to this kind of filter condition as an adaptive condition.  
In our approach, we have to consider multiple filters, all of which could include adaptive conditions. The key issue is how to adaptively modify the conditions without affecting the intended flow of the analysis. Our solution is to use an adaptive condition window, \(\omega\), by which multiple adaptive condition values can vary predictably. The condition window is a value that slides between 0.0 and 1.0, at 0.05 intervals. The condition window can slide one interval towards 0.0 or 1.0 after each time step. All filters have a defined range (upper bound \(u\) and lower bound \(l\)) for their condition value. The filter condition values are reevaluated after each executed time step based on input to the program, using the expression  
\[l+(u-l)\cdot\omega. \tag{2}\]  
The upper and lower bounds can be set to the same value, in which case the condition value of that filter is constant.  
For example, a researcher could determine that as much important data as possible should be saved to permanent storage after each time step. However, there is a strict time limit for the length of the I/O operation. The researcher would first decide on probes and filters that accurately can identify important data for the specific use case. The \(l\) and \(u\) variables would then be set to 0.0, 1.0, respectively. Initially, \(\omega\) is set to 0.0. However, after each time step, \(\omega\) can increase by 0.05 based on if the I/O time exceeds the specified time limit. Over time, \(\omega\) moves to the highest value possible such that the I/O operation does not exceed the time limit. As a result, the researcher can maximize the amount of data that can be stored for each time step. This behavior further scales to work with multiple probes and filters, meaning that many decisions can be made about different aspects of the simulation data.  
The adaptive condition window ensures that the condition values of all filters can be modified in a controlled manner, thus retaining the intended analysis flow. One condition window is used per pipeline, meaning that different condition windows can be used for different data sets if required.
###### Abstract  
In the last few years, Low-Power Wide-Area Network (LPWAN) technologies have been proposed for Machine-Type Communications (MTC). In this paper, we evaluate wireless relay technologies that can improve LPWAN coverage for smart meter communication applications. We provide a realistic coverage analysis using a realistic correlated shadow-fading map and path-loss calculation for the environment. Our analysis shows significant reductions in the number of MTC devices in outage by deploying either small cells or Device-to-Device (D2D) communications. In addition, we analyzed the energy consumption of the MTC devices for different data packet sizes and Maximum Coupling Loss (MCL) values. Finally, we study how compression techniques can extend the battery lifetime of MTC devices.  
compression; small cell; NB-IoT; energy consumption modeling; huffman; Lempel-Ziv-Weleklo; latency; LPWAN +
Footnote †: journal: _Sensors_  
discussed in [6]. Compared to other LPWAN technologies, using NB-IoT is advantageous due to the low-cost chipset, better building penetration and lower power consumption due to the simpler waveform. Due to these advantages will be the best option for static IoT devices such as smart meters. In [7], a prototype system including NB-IoT devices, an IoT cloud platform, and an application server has been tested. Other important aspects of NB-IoT which needs to be addressed are the capacity and coverage which are discussed in [8]. Finally, in [9], the authors conducted NB-IoT network performance analysis in a real-world indoor environment. The small cell concept has been defined as low-power access points that operate in licensed spectrum to improve cellular coverage and capacity and can be deployed in homes and enterprises [3]. Small cells can enhance MTC device coverage and provide a backhaul link over an internet connection to the core network.  
This paper significantly extends the initial research work in [3]. We have improved our simple evaluation in that paper using more realistic model for small cell propagation using a more precise path-loss models and using a realistic correlated shadow-fading map. Moreover, we also study device-to-device (D2D) communications to improve MTC device range. Furthermore, the energy consumption of the MTC devices using NB-IoT technology has been analyzed precisely, and compression techniques have been proposed to increase the lifetime of the battery. The remainder of this paper is structured as follows. Section 2 discusses system modeling and the communications scenario. Section 3 discusses simulation results for the impact of deploying small cells or D2D methods to improve coverage area for MTC devices. In addition, Energy consumption analysis is also given that considers the benefits of compression techniques. Section 4, discusses results from testbed for implementation of compression algorithms and impact of these algorithms on improving latency using wireless cellular technologies. Finally, Section 5 presents the paper conclusions.
# Challenge-Response Pair Mechanisms and Multi-Factor Authentication Schemes to Protect Private Keys
## 2 Description of the Protocol Protecting Private Keys with MFA
### Generation of Ephemeral Keys  
We assume that the public-private key pair \(\mathrm{Pk}/\mathrm{Sk}\) is generated with a second random number \(\mathrm{RN}_{2}\) with a protocol such as the one described in Section 3. To protect the private key \(\mathrm{Sk}\), the user randomly picks a third number \(\mathrm{RN}_{3}\), concatenated with the password to generate a set of challenges pointing at m positions in the combined reference table CT. An m-bit long ephemeral key \(\mathrm{K}\) is computed from the responses. The resulting key \(\mathrm{K}\) is used to encrypt the private key \(\mathrm{Sk}\) needed by the PKI scheme to sign transactions. The terminal device can be a crypto wallet, a smartcard, a token, a smartphone, or a personal computer. After encryption, the terminal device keeps in storage both random numbers \(\mathrm{RN}_{1}\), \(\mathrm{RN}_{3}\), and the encrypted private key \(\mathrm{SK}\). All other information is erased, which includes all reference tables CT-T, CT-Bio, CT-VT, CT, and the ephemeral key \(\mathrm{K}\). The user can recover the private key \(\mathrm{SK}\) with \(\mathrm{RN}_{1}\), \(\mathrm{RN}_{3}\), and the encrypted key \(\mathrm{SK}\), as shown in Figure 3, by plugging in the physical token, presenting their visual image, and computing the virtual token. Liveness tests can be inserted in the protocol to avoid third parties passing the visual test with an image of the user. The protocol based on biometric images is \"template-less\", which protects the privacy of the user. The user can prepare backup hardware devices by downloading the same software stack, as well as \(\mathrm{RN}_{1}\), \(\mathrm{RN}_{3}\), and the encrypted key \(\mathrm{SK}\). Such a scheme can, for example, protect a user against the loss of a crypto wallet, without compromising security, as the same MFA is always needed to retrieve the private key \(\mathrm{Sk}\). At every cycle, the user can easily pick a new random number \(\mathrm{RN}_{3}\) to generate a new ephemeral key \(\mathrm{K}\), thereby encrypting the private key \(\mathrm{Sk}\) with a one-time use key. This prevents attacks such as side channel attacks finding the ephemeral key during decryption cycles.  
Figure 2: Example of computation to obtain the reference table “CT” from the three reference tables generated from the token “CT-T”, the biometric image “CT-Bio”, and the virtual token “CT-VT”.
# Covert Communication for Integrated Satellite-Terrestrial Relay Networks with Cooperative Jamming
## 2 System Model and Problem Formulation
### Signal Model  
In the first time slot, \(S\) randomly selects the transmission model between two models: Model one, something transmission with power \(P_{S}\); and Model zero, nothing transmission. Moreover, whatever model \(S\) chooses, \(R\) always confuses \(E\) by transmitting the jamming signals with power \(P_{R}\) so as to achieve reliable covert communication between \(S\) and \(B\). Moreover, we note that the corresponding self-interference cancellation technique of \(R\) enables its self-interference to be removed partially. Hence, the received signal of \(R\) in the first time slot can be given by  
\[y_{R}(k)=\left\{\begin{array}{cc}r(k)+n_{R}(k),&H_{0}\\ \sqrt{P_{S}}h_{SR}x_{S}(k)+r(k)+n_{R}(k),&H_{1}\end{array}\right. \tag{1}\]  
Figure 1: The ISTRN with relay-assisted cooperative jamming.  
where \(H_{1}\) and \(H_{0}\) represent that \(S\) chooses Model one and Model zero, respectively. \(x_{S}(k)\) denotes the normalized signal from \(S\); \(k=1,2,...,n\); \(n\) is the number of channel used in each time slot; and \(E\Big{\{}|x_{S}(k)|^{2}\Big{\}}=1\). Moreover, \(n_{R}(k)\) represents the AWGN at \(R\) and \(n_{R}(k)\sim\mathcal{CN}(0,\sigma_{R}^{2})\). To simplify the subsequent analysis, it is assumed that \(\sigma_{i}^{2}=\sigma^{2}\) for \(i\in\{R,B,E\}\). In addition, \(r(k)\) represents the residual self-interference derived from the jamming signal \(x_{j}(k)\), which also meets \(E\Big{\{}|x_{j}(k)|^{2}\Big{\}}=1\). Moreover, \(r(k)\sim\mathcal{CN}\big{(}0,\mu P_{R}\sigma^{2}\big{)}\), and \(0<\mu<1\) denotes the residual factor.  
Then, \(y_{R}(k)\) is amplified by \(R\) with the coefficient \(G\) when \(H_{1}\) is true in the second time slot. It is worth noting that \(R\) always employs automatic gain control to guarantee that its transmit power is \(P_{R}\), where the gain \(G=P_{R}/\left(P_{S}|h_{SR}|^{2}+\mu P_{R}\sigma^{2}+\sigma^{2}\right)\). Even if \(S\) stops working or remains silent, \(R\) still forwards the jamming signals continuously. Moreover, it is assumed that \(B\) is aware of the jamming code book. Therefore, the continuous jamming signals can be successfully decoded and eliminated at \(B\).
## 3 Proposed Cross-Working Condition RUL Prediction Method  
Since the distribution discrepancies between training and testing data under cross-working conditions caused suboptimal prediction precision of rolling bearing RUL, aiming at this problem, an enhanced cross-working condition RUL prediction method via initial degradation detection-enabled joint transfer metric network is proposed and shown in Figure 1. The method includes an initial degradation detection module and a joint transfer RUL prediction module. Initial degradation detection is an essential action before RUL prediction, which is realized based on a novel RAPP health indicator created by DACAEN. Different from the reconstruction error of DCAE, RAPP compares the discrepancy between input and reconstruction of DACAEN not only in the input space, but also in the hidden spaces. The proposed joint transfer metric network focuses on feature learning of cross-working conditions, which is optimized along with regression loss and proposed joint domain adaptation loss. The former loss guarantees mapping ability from the training domain, and joint domain adaptation loss is designed for learning data distribution discrepancy reduction via VRD and the cross-domain invariant features via RSD. The proposed method can achieve higher prediction accuracy and better generalization ability than typical and advanced transfer RUL prediction methods under cross-working conditions.  
Figure 1: Structural diagram of the initial degradation detection-enabled joint transfer metric network.
###### Abstract  
Cloud detection is a critical preprocessing step for optical remote sensing imagery. However, traditional CNN-based methods have limitations in global feature modeling, while Transformer models, despite their strong global modeling capability, struggle to capture fine-grained local details effectively. To tackle these challenges, this study introduces a dual-path neural network framework that synergistically combines convolutional neural networks (CNNs) and architectures. By capitalizing on their complementary strengths, this work proposed a dual-branch feature extraction architecture that utilizes two different feature aggregation modes to effectively aggregate features of CNN and Transformer at different levels. Specifically, two novel modules are introduced: the Dual-branch Lightweight Aggregation Module (DLAM), which fuses CNN and Transformer features in the early encoding stage and emphasizes key information through a feature weight allocation mechanism; and the Dual-branch Attention Aggregation Module (DAAM), which further integrates local and global features in the late encoding stage, improving the model's differentiation performance between cloud and cloud shadow areas. The collaboration between DLAM and DAAM enables the model to efficiently learn multi-scale and spatially hierarchical information, thereby improving detection performance. The experimental findings validate the superior performance of our model over state-of-the-art methods on diverse remote sensing datasets, attaining enhanced accuracy in cloud detection.  
Keywords:image segmentation; remote sensing; transformer; deep learning
## 3 Results
### The RMS of Station Residual Position Time Series  
Figure 4 shows the residual position time series of station YNSM, which are derived from BDS, GPS, and BDS/GPS data, respectively. Three residual time series show similar steady temporal behaviors, especially for the GPS and BDS/GPS results, which are nearly complete coincidence. The BDS residual time series has the large temporal dispersion and contains several abnormal jumps, which is not found in GPS or BDS/GPS results.  
The RMS of GPS residual position time series for station YNSM is 2.5, 2.1, and 5.1 mm on north, east, and up components, respectively. The corresponding RMS of BDS/GPS result is 2.1, 1.9, and 4.3 mm, respectively, which indicates better inner accuracy of station positions from the combined estimation for BDS and GPS results. The RMS of BDS result is 3.4, 3.9, and 12.6 mm, respectively, which are significantly lower than GPS or BDS/GPS results. We present the average RMS of 44 CMONOC stations in Table 1. Comparing to the GPS results, the average RMS of BDS/GPS residual position time series shows a reduction of 20% and 13.5% for horizontal and vertical components, respectively. The average RMS is 6.1, 7.1, and 22.1 mm for BDS results on north, east, and up components,  
Figure 4: The residual position time series at station YNSM for BDS, GPS, and GPS/BDS combined solutions, respectively.  
respectively. The result indicates the accuracy of BDS positioning is holistically lower than that of GPS and BDS/GPS results.
## 3 UAV Flying Base Station Properties
### Involving Distributed and Edge Computing  
A UAV flying base station is inherently smaller than the stationary terrestrial base station because of its mobility and battery requirements. The smaller size and the energy constraint of a flying base station limits the number of users that a flying base station can serve, compared to a stationary terrestrial base station, which in turn increases the number of flying base stations on the edge and reduces the cell coverage size per base station. The architecture and control involving a group of base stations require greater sophistication on the networking edge and involve greater distributed computing and networking to enable dynamic, adaptive, and agile control for the improved connectivity provision of base  
Figure 3: Flying base station control parameters affecting its power consumption. The UAV flying base station shares the power from a singular source of a battery to support multiple functionalities of the UAV drone (movement) and the base station (wireless communication).  
stations. Therefore, flying base station control involves distributed networking to support the ad hoc and peer-to-peer networking, in addition to the centralized backhaul-accessible networking. Such capabilities forgoing the centralized backhaul networking can enable networking redundancy to control the flying base station operations (so that the flying base station control and operations have higher reliability) and can implement wireless relay to enable greater geographical coverage for the connectivity.  
**Security implications:** The dynamic, flexible, and ad hoc communications to control the flying base stations' operations are high-risk communications because such operations are high-impact and mission-critical. The failure of such communications in terms of availability and integrity can disable and disrupt the cellular connectivity provision to the user. Therefore, the flying base station's communications present a higher security risk than many other UAV ad hoc communication applications.
## 3 Results
### Influence of the Degradation Level on Model Parameters Inference  
Having established that the choice of the models used to infer the stiffness parameter \(k_{i}\) appears to not be relevant for a low-level of degradation, the aim is now to investigate what happens in possible further scenarios in which a higher level of damage is exhibited. This study is important when working in the field of model calibration because it explains the importance of linking the choice of the discrepancy model distribution--which is needed as input in the calibration process--to the degradation amount and the characteristics of the external force (e.g., amplitude and frequency).  
To this aim, the input excitation was rescaled. Specifically, two cases are examined as follows: (i) one in which the input has been rescaled with a PGA of 6 m/s\({}^{2}\) (medium level of degradation); (ii) one in which the input has been rescaled with a PGA of 9 m/s\({}^{2}\) (high level of degradation).
# Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity
## 3 Emerging Technologies in Smart Healthcare
### Blockchain  
In the Internet of Things era, smart devices, sensors, and IoT applications are connected to others based on the Internet infrastructure. This leads to real challenges regarding IoT applications in terms of privacy and security. Indeed, attackers can illegally access and hijack the system or steal data through the security vulnerabilities of IoT devices [60], operating systems [61], Internet gateways [62], remote computing servers [63], or denial of service (DDOS) attacks [64]. In the smart IoHT domain, medical data records play a particularly crucial role and are sensitive [65].  
In reality, blockchain technology is an advanced database management mechanism that enables transparent information sharing across a distributed network environment. Data are stored in blocks that are linked together in a consistent chronological sequence. Hence, the user cannot delete or modify the chain without the consensus of all users. As a result, blockchain technology creates an immutable ledger that keeps track of data records [66]. In the smart IoHT domain, blockchain technology is applied to ensure privacy and security in managing patient medical records. In recent years, integrating blockchain into healthcare applications has attracted strong interest from the academic community and achieved some positive results. In [67], to enhance privacy and security, the authors integrated blockchain into MEC-based IoT applications. To solve the delay caused by blockchain, they used a lightweight block verification algorithm. To ensure a suitable edge environment, they also applied a novel DRL-based AI technique. Their results showed that the proposed scheme significantly improved performance, privacy, and security compared to existing solutions. In [68], the authors designed a novel secure mobile edge computing framework for healthcare systems, namely BEdgeHealth. Indeed, they integrated blockchain into MEC-IoHT systems to enhance privacy and security for health data record-sharing and improve QoS. The real-world evaluation results showed that compared to existing computing solutions, the BEdgeHealth framework robustly improved QoS, privacy, and security.  
Figure 6: An illustration of AI-based smart health systems.  
In our opinion, IoHT applications will be impossible if privacy and security are not guaranteed, and blockchain technology is the key to solving this problem. Figure 7 presents an illustration of Blockchain-based smart health systems.
# Detection of Welding Defects Tracked by YOLOv4 Algorithm
## 4 Experiment  
### Dataset  
The dataset used in this experiment consists of images of internal defects in aluminum alloy welds, captured using the X-ray detection method. This is not a public dataset. The dataset includes three object classes: pores, slag inclusion, and incomplete penetration, totaling 1005 images. Given the limited number of images (1005), which is insufficient for effective training of deep learning models, data augmentation is necessary to enhance the dataset. By rotating, cropping, and adjusting the contrast of the images, the dataset is expanded to include more feature information. For example, Figure 7a-c illustrate the augmentation process for incomplete penetration defects. After using the popular annotation tool LabelMe, the dataset now comprises 5165 labeled images, as shown in the schematic diagram in Figure 8. The dataset is randomly divided into three subsets: 3616 images for training, 516 images for validation, and 1033 images for testing. Figure 9 illustrates the data distribution for different object classes across the training dataset, validation dataset, and test dataset. It is important to note that the data used for the subsequent comparison experiments were obtained from the test set. Utilizing this augmented dataset improves the robustness of the model during training.  
Figure 6: YOLOv4-cs2 framework diagram.  
Figure 7: Data enhancement: (**a**) rotating, (**b**) cropping, and (**c**) increasing contrast.