esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 3 Experimental Design
### Relevant 4G LTE Performance Metrics
#### 3.2.1 Reference Signal Received Power  
In bandwidth, the average power that carries reference signals across resource elements to specific cells is referred to as the reference signal received power (RSRP). In other words, it represents a metric that can efficiently tell how much quality of service a terminal is allocated. In terms of its structure, eighty-four resource elements make up a resource block. In the frequency domain, this amounts to 180 KHz. In the time domain, these blocks reach a threshold of 0.5 ms. Since the network is targeted towards operating terminals, in the time-frequency grid, users are allotted resource blocks according to their proximity to the base station and the capability of their equipment. In general, higher values of RSRP measured from a user are an indicator of a higher network quality experience. Measured in decibel-milliwatts (dBm), the RSRP shows how many resource blocks (RB) a user receives to transfer data. High bit rates have also been noticed where high modulation is used on account of a high number of resource blocks, made available due to improvements in the RSRP measurements on a terminal. In handovers and cell reselection cases, the RSRP can be  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Statistical Measure** & **RSRP** **(dBm)** & **RSRQ (dB)** & **PCC/SINR** & **RSSI** & **DL EARFCN** & **PCC PHY** & **PDCP DL** \\  & **(dBm)** & **(bps)** & **(dBm)** & **(bps)** & **DL (bps)** & **(bps)** \\ \hline Range & 62.74 & 21.94 & 41.76 & 51.05 & 4524.00 & 23,805.70 & 19,223.97 \\ Minimum value & \(-117.99\) & \(-22.38\) & \(-11.76\) & \(-85.05\) & 1876.00 & 80.00 & 0.00 \\ Maximum value & \(-55.25\) & \(-0.44\) & 30.00 & \(-34.00\) & 6400.00 & 23,885.70 & 19,223.97 \\ Median value & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ Mean value & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ Standard deviation & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ Variance & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) \\ Skewness & 0.66 & \(-1.92\) & \(-0.18\) & 1.02 & 4.10 & 0.57 & 0.66 \\ Number of Missing values & 9 & 9 & 9 & 9 & 9 & 85 & 79 \\ Percentage of missing values & 0.66\% & 0.66\% & 0.66\% & 0.66\% & 0.66\% & 6.20\% & 5.76\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical data of the measurements from Ikorodu.  
used in conjunction with the reference signal received quality (RSRQ) to determine when a user terminal switches from a signal antenna to receiver antennae.  
Mathematically, RSRP is defined in Equation (1) as:  
\[\text{RSRP }(\text{dBm})=\frac{P_{0}}{N} \tag{1}\]  
where \(P_{r}=\text{ received signal power and }N=\text{ number of resource blocks}\).
# The Role of the CFO of an Industrial Company: An Analysis of the Impact of Blockchain Technology
## 4 Analysis of the Results
#### 4.2.1 Reduction in Operational Expenditures  
As previously discussed, the blockchain offers a variety of cost-efficiencies regarding internal and inter-organizational accounting, reporting, auditing as well as the high potential of business process automation through smart contracts and emerging technologies. These advantages also materialize in the context of Industry 4.0, with the incorporation of both machine and sensor data recorded on the blockchain. Following the executed expert interviews, this enables the subsequent cost-cutting measures regarding operational expenditures.  
With reference to Expert 15, the installation of sensors into e.g., chemical silos enables real-time insight into the associated filling level. Here, for instance, the silo operator as well as the customer run a separate blockchain node to sign and verify the metered telemetry data. On the one hand, this allows for a highly accurate and ongoing stocktaking of the silo content. This simplifies the otherwise regular, cost- and time-intensive inventory process and provides insight into consumption data, which offer blockchain-enabled full integrity and transparency with fully automated replenishment of raw materials. On the other hand, this enables highly accurate forecasts of expected consumption which prevents excess production. Resulting from the high degree of transparency and trust among the operator and customer, a silo can be embedded into the payment transaction processing by additionally using the blockchain as a payment layer. Consequently, if a customer consumes chemicals out of the operator's silo, the silo itself automatically and directly executes the invoicing and audits the incoming payments. Thus, instead of the silo operator's accounting department, the silo itself sends the invoice and checks incoming payments based on the underlying smart contract predefining the order lifecycle. This also eliminates the need for invoice verification and payment instruction on the part of the customer. According to Expert 15, machines therefore have the potential to become independent economic actors referred to as \"profit centers\". In this scenario, machines are equipped with their own digital wallets, which make it possible for the machines to send and receive funds. This happens without requiring an additional intermediary for the transaction processing, such as a human-operated accounting department. This process considerably reduces administrative expenditures per transaction by the facilitated machine-to-machine payments and prospectively enables a decentral governance of organizations.
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
## 2 Related Works
### Deep Reinforcement Learning for Navigation  
Besides the well-known works in Games, the DRL algorithms also have been successfully applied to a wide range of problems, such as manipulators [25] and mobile robots [26; 27]. In this section, we discuss the research about the mobile robot navigation tasks.  
With the progress of the deep learning techniques, the powerful representation capabilities shed a new light on learning control policies directly from raw sensor inputs with the reinforcement learning framework. In recent years, there have been lots of proposed methods to tackle the autonomous navigation tasks with deep reinforcement learning algorithms. To address the navigation problems in reinforcement learning ways, these methods formulate the navigation process as the Markov decision process(MDP) 111 or partially observable Markov decision process(POMDP), and stack the observations from sensor readings as the states. Then, the methods will find the optimal policy module that is capable of guiding the robot to the target position.  
[PERSON] et al. [28] and [PERSON] et al. [29] used the maximum entropy inverse reinforcement learning (IRL) methods to learn interaction models for pedestrians from demonstration in occupied environments. [PERSON] et al. [30] used the image of the target object and the current observation as the input to the Siamese actor-critic model. Then, they formulated their task as a target-driven navigation problem, and evaluated the performance in an indoor simulator [31]. [PERSON] et al. [32] proposed a deep reinforcement learning algorithm based on the successor features, which can transfer knowledge from previous navigation problems to new situations. By using additional supervision signals from auxiliary tasks, [PERSON] et al. [33] greatly boosted the training and improved the task performance of their DRL algorithm in 3D mazes navigation tasks. Unlike addressing the navigation tasks in the static environment, [PERSON] et al. [34] developed a time-efficient navigation method in dynamic environments with pedestrians. Moreover, [PERSON] et al. [35; 36] extended the robot navigation task to the multi-robot case, and focused on addressing the collision avoidance problem.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 1 Introduction
Three lightweight models (ENet) [42], context-guided network (CGNet) [45], and efficient symmetric network (ESNet) [46]) and two heavyweight models (deep dual-resolution network (DDRNet) [47] and DeepLabV3+ [48]) were compared for damage segmentation from structural images to investigate the best model that could be embedded in edge computing devices with less computational power. 2. A concrete dataset that contains four types of concrete damage, i.e., cracks, efflorescence, spalling, and rebar exposure, was constructed for training and testing of the semantic segmentation models. Images were collected from online and real concrete structures in South Korea. 3. The lightweight segmentation models were benchmarked for the detection of multiple types of concrete damage, and the tradeoff between the number of model parameters and accuracy was investigated. After fair training of the models using the same training images, the inference performance on the test images was evaluated in terms of detectability and computation. Detectability was measured using mean intersection over union (mIoU) and F1-score, and the computation was measured using floating-point operations (FLOPs), as well as inference speed on a computer with GPUs.
# A Survey on Cybersecurity in IoT
## 3 Data Integrity and Privacy
### Advanced Cryptographic Techniques for Data Integrity
#### 3.1.1 Homomorphic Encryption: Technical Depth and Mathematical Framework
Homomorphic encryption, particularly fully homomorphic encryption, represents a significant advancement in cryptographic techniques by enabling secure computation on encrypted data. Its mathematical rigor ensures the preservation of confidentiality throughout the data lifecycle, providing a robust foundation for privacy-preserving applications [97].
## 4 Results and Discussion  
### Experiment Test for Improved SPH Cutting Model  
We established an improved SPH cutting model which incorporated the improved SPH algorithm, TANH constitutive model and contact algorithm. The improved cutting model is validated by comparing results from [PERSON] et al [37]. The following Figure 12 is the comparison between the simulated cutting process and the experiment (chips morphology formation is recorded by a camera in the experimental cutting process) both under the same cutting parameters (cutting depth, 0.1 mm; cutting speed, 235 m/min; and edge radius, 20 \(\upmu\)m, rank angle 0\({}^{\circ}\), clearance angle 11\({}^{\circ}\)). It  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiment Number**} & \multicolumn{3}{c}{**Cutting Parameter Level**} \\ \cline{2-4}  & **A** & **B** & **C** \\ \cline{2-4}  & **Rake Angle** & **Cutting Speed** & **Feed** \\ \hline
2 & 1 & 2 & 2 \\
4 & 2 & 1 & 3 \\
5 & 2 & 2 & 1 \\
6 & 2 & 3 & 2 \\
7 & 3 & 1 & 2 \\
8 & 3 & 2 & 3 \\
9 & 3 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: L\({}_{9}\) orthogonal array in simulated experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Symbol** & **Cutting Parameters** & **Level1** & **Level2** & **Level3** \\ \hline A & Rake angle & 0\({}^{\circ}\) & 3\({}^{\circ}\) & 7\({}^{\circ}\) \\ B & Cutting speed & 240 m/s & 120 m/s & 60 m/s \\ C & Feed & 0.07 mm & 0.1 mm & 0.13 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Three levels for cutting parameters.  
shows that the simulation results, such as strain localization along a curved shear and the curvature change of a slipped shear band (new segment), match the experiments very well.  
In addition, the accuracy of the cutting model can be verified by the segmented chip morphology and cutting force. We can compare three parameters of chip morphology between the simulation and experiments, such as segmentation width (W), maximum segmentation height (h1) and minimum segmentation height (h2), as shown in Figure 12. The comparisons of simulation and experimental chip morphology are presented in Figures 13-15. Table 5 shows the comparison of relative error in simulation and experimental data for segmentation characteristics.  
Figure 12: Two stages of a numerical and experimental segmented chip formation: (**a**) the beginning of strain localization and shearing in the primary; (**c**) primary shear zone begins to slip [37]; (**b**,**d**) the simulation results.  
Figure 13: Maximum segmentation height (h1) under different cutting speeds.  
Regarding the verification of the model, the h1, h2, and segment width and forces have been proven to be effective and efficient factors for verifying cutting models [15; 19; 20; 37]. From the comparison of the simulation and the experiment in terms of segmentation width, maximum segmentation height (h1) and minimum segmentation height (h2), cutting force and thrust force, we can conclude the improved SPH cutting model simulates the cutting process accurately and effectively.
## 5 Fuzzy Logic Approaches in Trust Management
#### 5.1.1 IoT
presented a fuzzy-based trust management mechanism for Internet of Medical Networks (IoMT) devices. The mechanism evaluates the trustworthiness of IoT nodes through fuzzy logic processing, identifying compromised or Sybil nodes. Thisapproach ensures only trustworthy nodes participate in the network, reducing the risk of Sybil attacks and enhancing overall security [31]. [PERSON] et al. proposed a fuzzy algorithm to assess trust levels in geographical data routing in IoT applications. The fuzzy algorithm accommodates uncertainties and imprecisions inherent in trust assessment, allowing for a more nuanced and flexible evaluation. This approach can effectively handle the dynamic and diverse nature of IoT networks, where nodes may exhibit varying levels of trustworthiness based on their behaviors and interactions. The use of fuzzy logic optimization for route selection, considering factors like QoS, trust, energy, distance, delay, and overhead, led to improved efficiency in geographical data routing in IoT applications [32]. In addition to the fuzzy logic algorithm, the paper also integrates Harris Hawk's Optimization (HHO) for optimization purposes, demonstrating its superiority in terms of energy efficiency, network lifespan, and overall effectiveness. Comparative analysis and statistical analysis demonstrate the robustness and reliability of the proposed fuzzy logic-based routing protocol. The deployment of the higher convergent HHO model for fine-tuning the membership function resulted in improved convergence and scalability, making the proposed approach suitable for long data communication networks and bulky IoT device networks. The combination improves trust evaluation and routing protocol performance by considering factors like trust, energy, distance, delay, overhead, and QoS [32].
## 4 Ice  
For the last few decades, terrestrial ice has undergone large change and its survey is of a prime importance for climate study. Altimetry is one of the most powerful tools for ice sheet mass balance, sea ice thickness and iceberg detection studies. An altimeter operating in Ka- instead of Ku-band is far from being insignificant for these studies. The decrease of penetration depth within the snow, from 2 to 12 m for the Ku-band to less than 1 m for the Ka-band ensures the measurement of the snow surface. Moreover, the higher waveform rate and narrowed footprint of SARAL/AltiKa allows to better sample the small scale features and to better track the elevation transition. It is suitable to map the surface undulations or large crevasses above ice sheet or to detect leads on sea ice or small icebergs. Moreover, SARAL/AltiKa being on the same historical 35-day orbit than ERS-1-2 and Envisat allows to extend the survey for more than 20 years [35].  
For sea ice, this phenomenon also seems to play a role but the major SARAL/AltiKa contribution yields from the reduced antenna aperture and in higher spatial resolution than previous altimeters. Indeed, the first echo is more likely to be at nadir decreasing influence of the off-nadir signal that often affects the retrieval measurement. Some studies in this issue confirm the ability of SARAL/AltiKa to detect leads and coastal polynyas as well as to represent spatial and temporal dynamics of water openings. Moreover, a meaningful contribution is the high sensitivity of the Ka-band backscatter to the snow and ice state, due to the altimeter wavelength. A few dB change may occur in a few weeks for any kind of snow or ice surface, even in the central part of the Antarctica ice sheet. It is observed in [36] a significant decrease of up to 15 dB at the end of ice season over Lake Baikal. The field measurements acquired along the SARAL/AltiKa tracks in Spring 2013 and Spring 2014 allow to suggest an important role of ice metamorphism and re-crystallisation.  
Figure 21: Water level change of Ngoring-Co Lake from a multi-satellite data processing. The lake was at equilibrium between 1992 and 2005 and has suddenly grown since 2006 to reach in a short time a new equilibrium, which slightly decreased after 2013 (extracted from Hydroweb database [[http://hydroweb.theia-land.fr/](http://hydroweb.theia-land.fr/)]([http://hydroweb.theia-land.fr/](http://hydroweb.theia-land.fr/)) and from SARAL/AltiKa data processing for the period 2013–2016).
# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 2 Materials and Methods
### Automated M-Mode Classification
#### 2.2.1 Semantic Segmentation of Lung Ultrasound  
For the segmentation of the lung, pleura, and rib, we used the fully convolutional architecture U-Net [16], Figure 3. Our model contains three encoder and three decoder blocks. Each block uses a 2D convolutional layer, 2D batch normalisation, and a rectified linear unit layer, in that order, twice, followed by either 2D max-pooling in the case of the encoder block or 2D transposed convolution in the case of the decoder block. The first two encoder blocks connect to a deeper layer and skip-connect to the corresponding decoder layer with the same resolution. The third and deepest encoder block connects directly to the deepest decoder block. Decoder blocks take the skip-connected output from the encoder block and the output from the decoder's deeper layer and produce the input for the higher-resolution decoder block. Finally, the prediction is made using a softmax layer  
Figure 3: The architecture of the automated M-mode classification. 1. Segmentation of the tissue from a single LUS frame; 2. selection of a temporal slice based on the semantic segmentation; and 3. classification of the slices with a ResNet 18.  
Figure 2: Creation of semantic masks for the pleura, lung, and rib from labelled data. **Top**: Bounding boxes labelled by volunteers. **Bottom**: Semantic masks generated from the bounding boxes.  
Loss is calculated as weighted pixel-wise cross-entropy. The weight is determined as a class distribution from a random subsample of the dataset. In the pleura segmentation, decreasing the weight of pleura segments by 80% improved the segmentation results.  
The input image resolution is 480 \(\times\) 480 pixels. We used four types of data augmentation: Vertical and horizontal flipping, random rotation, translation, and scaling. We trained the neural networks for 30 epochs with early stopping that occurred when, after 10 epochs, the validation loss did not drop below the lowest previously recorded value. We randomly divided the dataset into three parts: 50% for training and 25% each for validation and testing.  
To improve the resulting segmentation, we applied post-processing for the pleura, lung, and rib:  
* The connected regions in the prediction mask are detected;
* The largest connected region is kept, and the rest are discarded. In the case of the rib class, the two largest connected regions are kept;
* The holes in the largest connected region are filled.
## 3 Overview of Hyperspectral Imaging Spectroscopy (HSI)
### Hyperspectral Image Processing  
Images are subjected to preprocessing procedures to remove distortions. Image correction, segmentation, identifying regions of interest (ROIs), and feature extraction are the four significant steps in this processing. The image will be calibrated first with white and dark references to eliminate illumination non-uniformity and the influence of dark current [73]. When the light source is turned off and the camera lens's opaque covering is entirely closed, a dark image with 0% reflectance is usually obtained. A spectral image of a uniform, high-reflectance white calibration ceramic tile is used to generate a white reference image (with 99% reflectance).
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 3 Problem Statement
### Single Path or Multi-Path Routing
#### 3.1.3 Multi-Hop Multi-Path (MMP)  
MMP routing can control all paths available between a source and a destination. This is a crucial approach to high availability, rapid failover in the case of network failures, increased end-to-end bandwidth, dynamic traffic optimization, and resilience to DDoS attacks. SCION enables native network-wide multi-path routing, allowing traffic differentiation based on latency and bandwidth requirements.
## 3 DST-LEO Satellite-Traffic Prediction Model  
### LEO Constellation Traffic Prediction Architecture  
In this section, the aim was to enable the DST-LEO satellite-traffic prediction model to learn various communication patterns and potential features of the low Earth orbit (LEO) constellation network traffic and predict the traffic volume of each link in both short-and long-term future periods. Therefore, the authors used two datasets to verify the rationality and superior performance of the model. The authors introduced a spatio-temporal attention mechanism into the model to help it capture the unique patterns and features in inter-satellite links. At the data processing level, the authors introduced data separation, enabling the model to learn more targeted feature representations in the GCN layer and the LSTM layer according to the respective characteristics of the traffic amplitude changes and link connectivity.  
The traffic prediction tasks in low Earth orbit (LEO) constellations are quite different from those in terrestrial networks. In terrestrial networks, the graph topology structure introduced by the graph convolutional network (GCN) remains unchanged. However, in inter-satellite scenarios, due to the dynamic topology of satellites, the satellites will change the topology structure as they move. In this case, the GCN needs to be combined with a spatio-temporal attention mechanism to play a greater role. In addition, LEO constellations carry a large number of high-frequency data streams, and the amplitude of traffic changes is relatively large. A simple LSTM model is insufficient to capture the unique patterns and features. Multiple bi-LSTM layers, combined with GCN, specific data processing methods, and the spatio-temporal attention mechanism, can more effectively and deeply explore the laws of traffic information. To achieve accurate traffic prediction, the authors proposed a traffic prediction architecture for LEO constellations, which can be divided into the following parts, as shown in Figure 3: (1) Collect the traffic of inter-satellite links in the bearer network. (2) Process the traffic into a matrix-based dataset for model training. (3) Separate the collected traffic volume data and link the connectivity data through multi-source data separation and input them into the model, respectively. Then, unify the outputs to a specific dimension through data fusion. Enhance the model's understanding of features through the spatio-temporal attention mechanism, and finally, achieve the prediction of LEO constellation network traffic through the fully-connected layer.