esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Q-Learning for Two-Hop Communications of Drone Base Stations
## 2 Related Work
The states are represented by the image of the environments composed of raw pixel values, and the 8 moving directions at a fixed altitude and speed defined the action space. A DQN model is proposed for UAV navigation through the massive MIMO in [26]. In this model, there exist multiple UAVs that can fly in four different directions. The results show the out-performance of the proposed model over other strategies in terms of coverage and convergence. A Q-leaning based model is proposed in [27] for the intelligent navigation of a UAV, which is equipped with a wireless power transmitter. The goal is to maximize the transmission energy efficiency to the receiving nodes. The transmitter UAV has nine different actions to choose, and the locations of the receiving nodes are known in advance. The results are compared with random movement and static hovering at the center of the environment. The authors of [28; 29] used Q-learning to learn UAVs to navigate in an unknown environment to go from an arbitrary location to a target position in the shortest possible way. The results are verified using simulation and real implementation. To guarantee the convergence of the algorithms and to reduce the number of states, the environment is presented by a finite set of circles with equal radius, and the UAV altitude is constant. At each state, the UAV can take four possible actions. Given that providing backhaul is one of the main challenges in employing drone networks [30; 31], this problem is ignored in the presented works. The authors focused more on improving the performance of an access link between the users and the flying base stations. There are few works that consider the two-hop communication such as [32], where the coverage of a typical user in a UAV network is optimized considering both the backhaul link and the end-user link performance. In this work, the UAV adjusts its heights in order to improve the probability of coverage. Dual-hop or multiple hop communication in a UAV relaying system is addressed in [33; 34]. The average end-to-end throughput between a pair of fixed transmitter and receiver with the impact of interference in the environment are optimized in these works.
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
###### Abstract  
The building is an indispensable part of human life which provides a place for people to live, study, work, and engage in various cultural and social activities. People are exposed to earthquakes, and damaged buildings caused by earthquakes are one of the main threats. It is essential to retrieve the detailed information of affected buildings after earthquakes. Very high-resolution satellite imagery plays a key role in retrieving building damage information since it captures imagery quickly and effectively after the disaster. In this paper, the pretrained Visual Geometry Group (VGG)Net model was applied for identifying collapsed buildings induced by the 2010 Haiti earthquake using pre- and post-event remotely sensed space imagery, and the fine-tuned pretrained VGGNet model was compared with the VGGNet model trained from scratch. The effects of dataset augmentation and freezing different intermediate layers were also explored. The experimental results demonstrated that the fine-tuned VGGNet model outperformed the VGGNet model trained from scratch with increasing overall accuracy (OA) from 83.38% to 85.19% and Kappa from 60.69% to 67.14%. By taking advantage of dataset augmentation, OA and Kappa went up to 88.83% and 75.33% respectively, and the collapsed buildings were better recognized with a larger producer accuracy of 86.31%. The present study showed the potential of using the pretrained Convolutional Neural Network (CNN) model to identify collapsed buildings caused by earthquakes using very high-resolution satellite imagery.  
VGGNet; buildings; earthquake; dataset augmentation; pretrained CNNs +
Footnote †: journal: Applied Sciences
###### Abstract  
Quantitative estimation of the magnitude and variability of gross primary productivity (GPP) is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI) products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF) and a proper orthogonal decomposition (POD)-based ensemble four-dimensional (4D) variational assimilation method (PODEn4 DVar), incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of \(r=0.923\) and \(RMSD\) of 32.676 gC/m\({}^{2}\)/month compared with the LPJ-EnKF scheme (\(r=0.887\), \(RMSD=38.531\) gC/m\({}^{2}\)/month) and with no data assimilation (\(r=0.840\), \(RMSD=45.410\) gC/m\({}^{2}\)/month). Applying the PODEn4 DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003-2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems.  
g 2017 9 188 doi:10.3390/rs9030188
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
###### Abstract  
As edge computing environments become increasingly dynamic, the need for efficient job scheduling and proactive fault prevention is becoming paramount. In such environments, minimizing machine downtime and maintaining productivity are critical challenges. In this paper, we propose an integrated approach to scheduling optimization that combines deep learning-based fault prediction with Satisfiability Modulo Theories (SMT)-based scheduling techniques. The proposed system predicts fault probabilities for machines in real time by leveraging operational state features such as temperature, vibration, tool wear, and operating hours. These fault predictions are then used as inputs to the SMT solver, which dynamically optimizes job scheduling. The system ensures task completion within deadlines while minimizing fault risks and optimizing resource utilization. To achieve this, the deep learning model continuously updates fault probabilities through a rolling prediction mechanism, allowing the scheduling system to proactively adapt to changing machine conditions. The SMT solver incorporates these predictions into its optimization process, ensuring that the schedule dynamically reflects the latest system state. The proposed method has been evaluated in simulated production line scenarios, demonstrating significant reductions in machine faults, improved scheduling efficiency, and enhanced overall system reliability. By integrating predictive maintenance with optimization techniques, this research contributes to the development of robust and adaptive scheduling systems for dynamic production environments.  
c 202515771  
1
## 3 Proposed Procedure to Develop Predictive Models in Healthcare
### Application to Our Case Study
\begin{table}
\begin{tabular}{c c} \hline
**Approach** & **Main Parameters** \\ \hline
**Logistic regression** & Ridge value in the log-likelihood: from \(10^{-4}\) to \(10^{-12}\) (parameter change every \(10^{-2}\)). \\ \hline
**Decision tree (C4 pruned)** & Number of instances per leaf: 3/5/10/15/20. Confidence factor for pruning (Conf.): 0.15/0.25/0.35. \\ \hline
**Random forest** & Maximum depth of the tree: 3/4/5. Number of trees: 25/50/100/200. \\ \hline
**Support vector machine (SVM)** & Tolerance: \(10^{-3}\). Kernel function: radial basis function (RBF). Epsilon for round-off error: 10\({}^{-12}\). Complexity (C): 0.25/0.5/1/2/4. Gamma (kernel width): 0.01/0.25/0.5/1/2. \\ \hline
**Neural Network (MLP neural network)** & Type: multilayer perceptron (MLP). Learning Rate (LR, the amount the weights are updated): 0.2/0.3/0.4/0.5. Momentum (Mom., applied to the weights during updating): 0.1/0.2/0.3. Number of epochs for training: 500. Number of hidden layers: 15. Auto-built option in Weka set to true. \\ \hline
**K-Nearest Neighbors (KNN)** & Number of Neighbors (K): 1/3/5/7/9/11/13/15/20. Distance function: Euclidean distance, Manhattan distance. \\ \hline
**Gradient Boosting Algorithm (GBA)** & Iterations (Iter.): 10/20/50/100. Weight threshold (W.T.): 50/100/200. AdaBoost implementation provided by Weka. \\ \hline \end{tabular}
\end{table} 
Table 3: ML approaches considered and their main parameters. Figure 5: Parameter tuning of the seven algorithms selected.
# Study of Physicochemical and Gelatin Properties of Fish Gelatin from Different Sources
## 2 Materials and Methods
### Water-Holding and Oil-Holding Tests of Gelatin  
WHC and FBC were measured according to a previous work [20]. For this assay, 1 g of gelatin was added to 30 mL of deionized water at 25 \({}^{\circ}\)C and stirred well, followed by 2 rounds of standing for 15 min and then stirring for 5 s. After repeating this process 4 times, the sample was centrifuged for 20 min, the upper aqueous phase was poured out, the residual water on the surface was absorbed with filter paper, and the mass of the gel sample (g) was recorded. The water-holding capacity (WHC) of gelatin was calculated using the following formula:  
\[\mathrm{WHC}(\%)=\frac{\mathrm{m}_{\mathrm{w}}-\mathrm{m}}{\mathrm{m}}\times 1 00\% \tag{1}\]  
Note: m is the initial gelatin powder mass, g.  
Likewise, 1 g gelatin was added to a 50 mL centrifuge tube at 25 \({}^{\circ}\)C and 10 mL of soybean oil was added and stirred well, followed by 2 rounds of standing for 15 min and stirring for 5 s. After repeating this process 4 times, the sample was centrifuged for 20 min,the upper oil phase was removed, the residual oil on the surface was absorbed with filter paper, and the mass of the gel sample (g) was recorded. The oil-holding capacity (FBC) of gelatin was calculated using the following formula:  
\[\text{FBC}(\%)=\frac{\text{m}_{\text{F}}-\text{m}}{\text{m}}\times 100\% \tag{2}\]  
Note: m is the initial gelatin powder mass, g.  
Detection of the Secondary Structure of Self-Assembled and Enzymatic Gels Using Fourier Infrared Spectroscopy (ATR-FTIR)  
According to the improved method [21], 2 mg of self-assembled and enzymatic gel samples prepared at 4 \({}^{\circ}\)C were weighed, co-milled with 200 mg of potassium bromide, pressed and scanned using Fourier transform infrared spectroscopy at the interval of 400 cm\({}^{-1}\)-4000 cm\({}^{-1}\) at a scanning frequency of 16 times.
## 4 Performance evaluation
### Overview  
The dataset, which can be found at a public GitHub repository ([[https://github.com/wine-uoc/wisun_traces](https://github.com/wine-uoc/wisun_traces)]([https://github.com/wine-uoc/wisun_traces](https://github.com/wine-uoc/wisun_traces))) together with the Python scripts used to create the plots presented in this article, is composed of 11 files formatted in CSV (Coma Separated Value), one for each sensor that was deployed, and contains the data captured between 31 July and 10 November 2019 (103 days). However, due to external reasons (i.e., loss cellular connectivity), we do not have enough data for 4 days (20-21 August 2019 and 28-29 October 2019, respectively) and, thus, the data analyzed only encompasses 99 days of the total.  
The files in the dataset are named data_x.csv, where x is the last two bytes of the EUI-64 node address, and each file contains 13 columns. The first 4 columns are independent of the IEEE 802.15.4g SUN modulation and contain the following fields:  
* day: the day that the data packet was received in yyyy/mm/dd format;
* hour: the hour that the data packet was received in hh/mm/ss format;
* pkt_number: the number of the data packet received;
* pkt_retry: the number of the data packet retries (i.e., 0, 1, 2).  
The next 9 columns in each file contain 3 different fields that are repeated for each IEEE 802.15.4g SUN modulation (i.e., SUN-FSK, SUN-OQPSK and SUN-OFDM):  
* rssi: The RSSI value (dBm) for the current data packet received;
* cca: The CCA value (dBm) sampled before the current data packet was transmitted;
* cca_retries: The number of retries before the transmission passed the CCA check and the data packet was transmitted.
# Post-Quantum Authentication in the MQTT Protocol
## 1 Introduction
### Mqtt  
MQTT is a common communication protocol used in IoT. It is a simple, lightweight messaging protocol used to establish communication between multiple devices relying on the publish-subscribe model. The MQTT protocol is based on two types of parties: clients and brokers (sometimes referred to as servers). For our experiments, the IoT devices are clients and the gateway is the broker. All clients connect only to the broker. When first connecting, the client and broker perform a handshake, thus establishing their connection. Following this, there are two main types of messages exchanged: subscribe and publish. Clients can subscribe to certain topics (e.g., _temperature_ or _humidity_), and the broker will store a list of which clients subscribe to which topics. Clients can then send publish messages for a certain topic (e.g., _temperature_) and the broker will sort and forward the message to all clients subscribed to that topic. All clients can publish and subscribe to messages, although IoT devices often do not subscribe to many, if any, topics.  
An example MQTT interaction can be seen in Figure 1. There, we have a system that has a gateway as a broker, and a temperature sensor and a mobile device as clients. The temperature sensor does not subscribe to any topics, and only publishes messages in the topic _temperature_. Therefore, there is a one-way connection from the temperature sensor to the gateway. The mobile device then receives updates about the temperature as it has subscribed to the _temperature_ topic.  
MQTT uses TLS to provide authentication, so the handshake process is the same as the one used for TLS ([[https://www.ietf.org/rfc/rfc5246.txt](https://www.ietf.org/rfc/rfc5246.txt)]([https://www.ietf.org/rfc/rfc5246.txt](https://www.ietf.org/rfc/rfc5246.txt)) (accessed on 1 February 2023)). This adds more overhead and reduces the lightweight advantage of MQTT.
# Prediction and Comparative Analysis of Software Reliability Model Based on NHPP and Deep Learning
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Real**} & \multicolumn{3}{c}{**GO**} & \multicolumn{3}{c}{**DS**} & \multicolumn{3}{c}{**YID**} \\  & & **Prediction** & **Upper** & **Lower** & **Prediction** & **Upper** & **Lower** & **Prediction** & **Upper** & **Lower** \\ \hline
117 & 335 & **215.85** & **244.65** & **187.06** & **284.80** & **317.88** & **251.73** & **285.07** & **318.16** & **251.98** \\
118 & 350 & **217.69** & **246.61** & **188.77** & 289.52 & **322.87** & **256.17** & **289.81** & **323.18** & **256.45** \\
119 & 366 & **219.53** & **248.57** & **190.49** & **294.27** & **327.89** & **260.65** & **294.59** & **328.24** & **260.95** \\
120 & 378 & **221.37** & **250.53** & **192.21** & **299.06** & **332.95** & **265.16** & **299.41** & **333.33** & **265.50** \\
121 & 386 & **223.21** & **252.49** & **193.93** & **303.88** & **338.05** & **269.71** & **304.26** & **338.45** & **270.08** \\
122 & 398 & **225.05** & **254.45** & **195.65** & **308.74** & **343.18** & **274.30** & **309.16** & **343.62** & **274.69** \\
123 & 406 & **226.89** & **256.41** & **197.37** & **313.63** & **348.34** & **278.92** & **314.08** & **348.82** & **279.35** \\
124 & 412 & **228.73** & **258.37** & **199.08** & **318.56** & **353.54** & **283.58** & **319.05** & **354.05** & **284.04** \\
125 & 422 & **230.57** & **260.33** & **200.80** & **323.52** & **358.78** & **288.27** & **324.05** & **359.33** & **288.76** \\
126 & 432 & **232.40** & **262.28** & **202.53** & **328.52** & **364.05** & **293.00** & **329.08** & **364.64** & **293.53** \\
127 & 449 & **234.24** & **264.24** & **204.25** & **333.56** & **369.35** & **297.76** & **334.16** & **369.98** & **298.33** \\
128 & 455 & **236.08** & **266.20** & **205.97** & **338.63** & **374.69** & **302.56** & **339.27** & **375.37** & **303.16** \\
129 & 476 & **237.92** & **268.15** & **207.69** & **343.73** & **380.07** & **307.39** & **344.41** & **380.79** & **308.04** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: 95% Prediction confidence interval of software reliability models from 90% of Dataset 2. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Real**} & \multicolumn{3}{c}{**PNZ**} & \multicolumn{3}{c}{**PZ**} & \multicolumn{3}{c}{**TP**} \\  & & **Prediction** & **Upper** & **Lower** & **Prediction** & **Upper** & **Lower** & **Prediction** & **Upper** & **Lower** \\ \hline
## 6 Conclusions  
In summary, this study integrates Generalized Quadrature Spatial Modulation with Quasi-Orthogonal Space-Time Block Codes in Nakagami-m fading conditions and real-world system impairments, providing a reliable and effective solution for practical MIMO systems. Notably, Technique III, based on QR decomposition, demonstrates significant advantages in mitigating the effects of transmit antenna correlation and imperfect CSIR. For next-generation wireless communications operating in challenging propagation environments, the proposed design represents a viable and scalable option, achieving superior error performance with reduced computational complexity compared to traditional ML detection.
## 3 Method for Proactive Management of Requirement Changes
### Change Risk Assessment
#### 3.3.1 Prioritizing Requirements  
A risk portfolio with the dimensions likelihood of change and change impact through propagation is used to prioritize the requirements (cf. Figure 12). A risk portfolio as a prioritization technique was chosen because it is common in practice, can be easily interpreted and used for company-wide communication, and allows a relative comparison between requirements [workshop W4].  
In deviation from a conventional risk portfolio, a third aspect was added: collective change impact is represented by the diameter of a requirement. The third dimension is required to represent the requirement set completely and showing all results from risk assessment at the same time. Whereas the change likelihood is determined for all requirements, collective change impact is only evaluated for critical requirements (cf. Section 3.2.2). Hence, propagation impact is used for risk portfolio dimension.  
To prioritize the requirements, a threshold value is defined for each portfolio dimension. Based on the threshold values, the portfolio is divided into four quadrants, which are assigned a risk category (high, medium and low). The requirements are categorized and prioritized according to the associated quadrant. The threshold values can be adjusted depending on the risk affinity. As default, average values are used.
## 4 Experimentation and Results  
### Evaluation Indicators  
The confusion matrix is a table that is commonly used in classification problems to evaluate the performance of a classification algorithm. It compares the actual and predicted values of a dataset and provides a summary of the number of true positives, true negatives, false positives, and false negatives for each class in a classification problem. The true positives (_TP_) are the number of correctly predicted positive instances, while the true negatives (_TN_) are the number of correctly predicted negative instances. The false positives (_FP_) are the number of negative instances that were incorrectly predicted as positive, and the false negatives (_FN_) are the number of positive instances that were incorrectly predicted as negative. Overall, the confusion matrix and its associated performance metrics provide valuable information for evaluating the performance of classification algorithms and identifying areas for improvement.  
The following parameters and metrics are used for comparative analysis of data-driven models. In this study, the classifier correctly predicted _TP_ and _TN_ instances and incorrectly predicted _FP_ and _FN_ instances. The paper includes various performance metrics such as accuracy, sensitivity, specificity, and AUC score. It is a valuable tool for evaluating the performance of classification models and identifying areas where the model can be improved. To assess the unbiasing effectiveness of the proposed classification method, the _TP_, _FP_, _FN_, and _TN_ statistical indices are computed and used to calculate accuracy, sensitivity, and specificity, as follows:  
\[Accuracy=\frac{TP+FP}{TP+FP+TN+FN} \tag{6}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{7}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{8}\]  
The entire dataset contained 657 images. The models are trained and evaluated using 5-fold cross-validation technique. We used 30 epochs with a batch size of 16. The learning rate was fixed at 0.001 for all architectures with the Adam optimizer.