esa-sceva/satcom-chunk-collection huggingface dataset:
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 3 Preliminaries
### Computational Problems on Elliptic Curve  
The elliptic curve cryptographic system is considered to be secure due to the existence of three computationally hard problems, which are listed below:  
* Elliptic Curve Discrete Logarithmic Problem--For two given points on the elliptic curves \(Q^{\prime}\in E\big{(}F_{q}\big{)}\) and \(R^{\prime}\in E\big{(}F_{q}\big{)}\), it is computationally not feasible to find an integer \(k\) such that \(Q^{\prime}=kR^{\prime}\)[56].
* Elliptic Curve Diffie-Hellman Problem--For a given point \(Q^{\prime}\in E\big{(}F_{q}\big{)}\) and two more points, \(R^{\prime}=aQ\) and \(S^{\prime}=bQ\), on the same elliptic curve \(E\big{(}F_{q}\big{)}\), it is computationally hard to determine a point \(P^{\prime}=abQ^{\prime}\)[57].
* Elliptic Curve Decision Diffie-Hellman Problem--For a given point \(Q^{\prime}\in E\big{(}F_{q}\big{)}\) and three more points, \(R^{\prime}=aQ^{\prime}\), \(S^{\prime}=bQ^{\prime}\), and \(T^{\prime}=cQ^{\prime}\), on the same elliptic curve \(E\big{(}F_{q}\big{)}\), it is computationally not feasible to conclude whether \(T^{\prime}=abQ^{\prime}\)[58].
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 4 Case Study
### Configuration of Models  
For the three BTSs presented in Table 3, operating in the same network coverage area, the masts heights were determined to be 30, 50 and 70 m, and other parameters such as transmit power and mechanical azimuth were also considered. A physical test drive was conducted in a case study area to obtain relevant data on signal strength and inspect the equipment and components present at each BTS site. Interviews of technical and managerial staff in the mobile telecommunication industry were also performed to gather related information required for the case study. The test drive technique involves using a vehicle, digital electromagnetic set, frequency spectrum analyzer, GPS and a laptop. Specifically, the test drive was undertaken to ascertain the signal quality of the area such as signal intensity, quality and interference, while GPS location coordinates were recorded for map calibration. The results from the drive test showing the various frequencies and antenna gain over different ranges of distances for one of the BTSs are shown in Figures 5-7. In engineering, both measurement and modelling are a necessity to achieve a good, reliable outcome [16; 17]. For this particular BTS, a channel model giving a good approximate fit to the measurements taken was obtained with \(dBm_{1}=-27.75\) and \(\gamma_{k,1}=2.99\). Similar measurements were also obtained for masts 2 and 3. Costs for each of the BTS sites were
# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
## 5 Intelligent Trajectory Prediction Algorithm Based on Intention Inference
### Process of Intelligent Trajectory Prediction Based on Intention Inference  
The process of trajectory prediction based on intention inference is shown in Figure 11.  
1. In order to improve the efficiency of network learning, the observed data are normalized first and then input into the initial and last state modification network to obtain modified initial and last state of observation, which can improve the accuracy of predicted trajectory and modified filtering trajectory;
2. Obtain the filtering data \(p_{f}=[K_{D}^{f}|\beta^{f}|,K_{L}^{f}]\) by the trajectory tracking algorithm;
3. Input the filter control parameters into the parameter modification network to obtain more accurate filtering control parameters \([K_{D}^{m},\beta^{m},K_{L}^{m}]\);
4. Input the value of modified filtering control parameters into the parameter value prediction network, and obtain the predicted control parameter values \([K_{D}^{p},|\beta^{p}|,K_{L}^{p}]\) according to the temporal law of \([K_{D}^{m},|\beta^{m}|,K_{L}^{m}]\);
5. Based on the importance map, trajectories association models and others, obtain the influence factors such as cross-range, longitudinal range, time-to-go, and relative importance, then input these factors into the intention inference network, identify the intention of target, and obtain the threat value;
6. According to the intention of the target, combine the sign change rule of bank angle with the predicted control parameter value, and extrapolate the predicted trajectory by the integral-iteration method.
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 5 Proof-of-Concept
### Sensor Node Instrumentation and Monitoring Information Processing  
The proof-of-concept implementation presented here aims at demonstrating the applicability of the proposed architecture to several software development scenarios. Specifically, the architecture should support OS-based applications as well as \"bare metal\" applications, as in the current case. With this in mind, the application was developed based on a function-queue-scheduling software architecture. Similar to what happens in OS-based approaches, the functions are added to a queue of function pointers, and called when appropriate. The scheduler is responsible for getting the next function in the queue, which will then be executed. On completion, the microcontroller is put in low power mode (LPM) to save energy.  
The main topic of this section is, nevertheless, the sensor node monitoring agent and its interactions with the BSP, the main application, and the network (Figure 3). The sensor node monitoring agent is a library developed in C/C++ that collects the sensor node state data, stores it, and triggers a periodic event to send the monitoring data over the network. By manually inserting a simple call to the sensor node monitoring agent at the beginning of each function to be monitored (see Figure 3a (1)), it is possible to record the state of the sensor node's firmware and hardware. The monitoring information is then stored in a ring buffer (Figure 3a (2)), until it is sent over the network. In addition, the hardware state data is collected directly by the sensor node monitoring agent library from the BSP (Figure 3a (3)).  
Last but not least, as illustrated in the Figure 3b, when there is sufficient data to fill an IEEE 802.15.4 data packet payload, the sensor node monitoring agent requests a publish service to send the data available in the ring buffer (4). If the network has enough resources to send the data at the requested rate, an authorization is received (5), and the sending process starts (6-7). In case the network does not have enough resources, the microcontroller will receive a service denial, and the sending of the log will be postponed. When no more data is available in the ring buffer, the sensor node monitoring agent requests the deletion of the service in order to free the network resources (8).
# Spectrum Allocation Based on an Improved Gravitational Search Algorithm
###### Abstract
Although channel assignment based on metaheuristic algorithms has been studied for a long time, the study still faces challenges, such as algorithms easily falling into local optima and slow convergence speed. Gravitational search algorithm (GSA) [19] is a novel metaheuristic algorithm, which has been confirmed to be able to achieve higher performance in solving various non-linear problems compared with some well-known swarm algorithms [20]. In this paper, we use graph coloring theory to formulate the spectrum assignment problem, and we also design a new version of GSA (IGSA) to maximize the spectrum utilization. In IGSA, first, Chebyshev map [21] is adopted to initialize the population of IGSA to reduce the risk of trapping to the local maximum. Second, pattern search algorithm (PSA) [22] is introduced to ameliorate the inefficiency fine searchability of GSA. Besides, we investigate interference cancellation according to the interference-free principle. Based on the IGSA, we allocate the available licensed channel to SUs without interference to PUs while maximizing system utility.
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 3 Motivation  
### Proving the Naturalness Assumption via Language Models in Software Defect Prediction  
The use of language models greatly speeds up the advancements in natural language processing. In terms of the software engineering domain, there is usually a few years gap between the time a new language model is proposed and the time it is used in this domain. For example, the use of Word2 Vec and Glove is still popular in software defect prediction. Similarly, since the breakthrough of the natural language processing domain (the BERT model) is proposed in 2019, it is not until last year before CodeBERT, a pre-trained programming language model using the BERT architecture, is proposed, and the model is open to the public.  
It is yet unclear whether using a pre-trained language model will improve the prediction performance of software defect prediction. Since naturalness has been regarded as an essential part of code characteristics [45; 46], researchers would assume that a large-scale programming language model would be more competent in identifying \"unnatural\" code, i.e., code that does not follow the universal patterns identified from large code corpora. Recently, the naturalness assumption has been proven by IBM researchers by tagging AST nodes via pre-trained language models [47]. The naturalness assumption indicates that \"unnatural\" codes are more likely to be buggy when it comes to software defect prediction. However, it remains unclear whether the naturalness assumption is valid in software defect prediction.  
Figure 3: The overall architecture of BERT.
# Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis
## 1 Introduction
Although these methods can basically recover the low-dimensional manifold in high-dimensional noisy data, the LLE algorithm is not improved comprehensively and the experiments are not validated by real data in the practical application process. The principle of LLE algorithm can be roughly summed up into three steps. Firstly, construct the neighborhood graph of sample data. Then obtain the weight matrix by local linear structure in the neighborhood. Finally, get low-dimensional embedding of sample data by using the weight matrix. Currently, LLE algorithm determines the similarity of neighborhood points by measuring Euclidean distance among sample points [15]. Euclidean distance assumes that all components in the space are irrelevant. However, as for bearing vibration signal, the recent vibration signals are often correlated. It is obvious that if it still uses Euclidean distance in actual application of mechanical fault diagnosis, the true relationship between fault data can be ignored and resulting in unreasonable neighborhood construction. In addition, due to a variety of complex operating conditions factors in actual sampling process, it often contains noise pollution. If we cannot improve the noise reduction capability of LLE algorithm, the mapped structure of low-dimensional data will be distorted and the accuracy of the fault diagnosis will be seriously reduced. Therefore, aiming at above two problems, we proposed M-LLE algorithm, which improves the ability of LLE algorithm to resist noise. As for the non-stationary, non-linear and high-dimensional characteristics of bearing failure data, MD is used to instead of Euclidean distance in the construction of neighborhood. In the process of weight acquisition, the L1-norm is adopted to standardize the cost function which calculates the weight of M-LLE algorithm. We are taking the distance among sample points and the structural characteristics of manifold into account, so M-LLE algorithm can reduce interference of noise. In order to verify the effectiveness of M-LLE algorithm, we use artificial data sets and the actual bearing fault data to simulate the experiment. The experimental results show that the method proposed in this paper can effectively realize the intrinsic manifold acquisition of noisy high-dimensional data, and the algorithm can significantly improve the diagnostic accuracy in the process of bearing fault diagnosis.
## 3 Results  
### Forest and Non-Forest Identification Results  
The segmentation of forest and non-forest areas in the study area was satisfactory with the band combination of the original image for segmentation and after feature extraction, with OAs higher than 80% except for maximum likelihood (ML), as shown in Table 3. The distribution of forest and non-forest was relatively balanced, but random forest (RF), support vector machine (SVM), and ML still showed low values in the IoU of the forest. Both U-Net and Pix2 Pix models utilized GF-2 images as pre-training data for the segmentation process. The detailed analysis of the segmentation results, including local features, is shown in Figure 6. It should be noted that the algorithms we had chosen were universal algorithms in forest type identification. RF, SVM, and ML are algorithms in common use for remote sensing land use/land cover (LULC), while U-Net is a commonly used algorithm in deep learning semantic segmentation. Therefore, these methods have strong comparative significance.  
As the most commonly used algorithm for forest segmentation, RF was chosen as the baseline for comparison purposes. ML had the lowest overall accuracy (OA), with a rate of  
Figure 5: Forest types identification flow chart.  
79.05%, approximately four percentage points lower than the baseline. The MIoU of SVM was relatively close to the baseline, whereas the OA was slightly lower than the baseline. The OA of F-Pix2 Pix was 91.25%, and the MIoU was 82.93%, which were 8.12% and 21.48% higher than those of RF, respectively, with a 36.72% higher IoU in the forest. The deep learning models (U-Net and Pix2 Pix) performed significantly better than the other models in all metrics. In the comparison between GF and Landsat data, both OAs were higher than 90%, the MIoU was higher than 80%, GF-1 was slightly better than OLI, and both GF-1 and OLI had decreased segmentation accuracy compared to GF-2.
## 2 Methodology
### Integration of Explainable AI  
Interpretability was provided locally through the use of LIME, which approximated the model's behavior around a specific diagnosis using a simpler model. LIME helps healthcare professionals understand why a model diagnoses a disease for a specific patient based on their symptoms, which is very useful when diagnosing diseases where patient cases may differ significantly from one another. This localized explanation aids in identifying any irregularities or errors in the diagnosis, thereby increasing the diagnostic model's performance and dependability. LIME offers versatility and broad applicability in a range of diagnostic scenarios because it is model-agnostic and works with a variety of machine-learning models [28; 29]. ChatGPT uses its powerful natural language processing capabilities to analyze and understand complex medical data, significantly improving the diagnosis of febrile diseases [30; 31].  
To enhance the interpretability of our diagnostic model, we integrated LIME and ChatGPT, at different stages of the diagnostic process. First, LIME was applied to the machine learning models (Random Forest, XGBoost, and MLP) to generate local explanations for individual diagnoses. After a model predicted the probability of a patient having a particular febrile disease, LIME approximated the model's behavior by creating a simpler, interpretable surrogate model. This was achieved by slightly changing the input symptoms, monitoring how the ML model's prediction varied, and determining which symptoms had the most influence on the final diagnosis. This step ensured that healthcare professionals could understand the reasoning behind each prediction and verify its reliability. Next, ChatGPT was integrated as a secondary explainability layer to provide more comprehensive, natural-language explanations. The output probabilities and predicted disease labels from the ML models and the LIME explanations were structured into a prompt, which was fed into ChatGPT. ChatGPT then generated a human-understandable explanation by combining domain knowledge with the ML model's results. The structured interaction between ML models and ChatGPT ensured that healthcare workers received transparent explanations, improving trust in the system. By integrating LIME for feature attribution and ChatGPT for contextualized explanations, our approach ensures that the diagnostic process is interpretable, ultimately aiding in better clinical decision-making.
## 1 Introduction
Despite the significant attention paid to phosphate adsorption onto BC-LDHs and the individual implementation of these materials--namely, biochar and LDHs--in agricultural practices, there have been no extensive investigations into the role of such composites after their adsorption in plant systems. In our previous study [2], we evaluated the preparation of various types of BC-LDH composites, with an emphasis on the influence of biochar quantities and fabrication methods on their phosphate adsorption characteristics, and especially their adsorption mechanisms. However, we were unable to confirm the potential of reusing these nutrient-loaded materials as fertilizers. Although high-efficiency adsorbents have been developed for sustainable and affordable phosphate treatments, we still face the major challenge of the efficient utilization of this reclaimed phosphate nutrient. This is critical in preventing the depletion of limited phosphate resources. Considering these issues, we propose the novel idea of the direct utilization of post-adsorption BC-LDHs as agricultural fertilizers. In this ongoing study, a new class of fertilizer products was obtained from phosphate-containing BC-LDHs (P-BC-LDHs), and their effect on plants was assessed through a series of experiments that compared several P-BC-LDH application rates. To the best of our knowledge, the present study is the first to evaluate the effect of different dosages of P-BC-LDHs on plant growth by observing the seed germination and early growth responses of lettuce (_Lactuca sativa_ L). Additionally, the solubility behavior of phosphorus and magnesium nutrients in P-BC-LDHs and aluminum were compared for the first time. This study contributes toward filling knowledge gaps in this area as its results broaden the understanding of the actual interaction between P-BC-LDHs and plants, as well as determining the optimal dosage for its potential reuse as a beneficial phosphate fertilizer.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 3 Design Methodology, Layout and Circuit Simulation
### Layout and Circuit Simulation
#### 3.2.3 LDO  
Figure 16 shows the location of the LDO voltage regulator inside the microphotography of the whole self-powered tag. Moreover, a zoomed-in view of its layout is included. The silicon area consumption of this circuit is 220 \(\upmu\)m \(\times\) 105 \(\upmu\)m. The LDO is designed to deliver an output voltage of \(V_{DD_{2}}=0.6\)\(V\) and an output current of \(I_{L}=2.5\)\(\upmu\)A using a voltage supply of \(V_{DD}=0.9\) V. The element values and transistor dimensions that compose the LDO are listed in Table 5.  
Figure 17 depicts the post-layout simulations where the behavior of the LDO output voltage \(V_{DD}\) is observed. Figure 17a shows the line regulation characterization where the input voltage \(V_{DD}\) is swept from 1 V to 1.8 V and the load current is set under no-load, \(I_{L}=0.1\)\(\upmu\)A, and full-load, \(I_{L}=2.5\)\(\upmu\)A, conditions. The reported line regulation during no-load and full-load are 125 \(\upmu\)V/V and 375 \(\upmu\)V/V, respectively. Figure 17b illustrates the load regulation characterization where the load current \(I_{L}\) is swept from 0 \(\upmu\)A to 2.5 \(\upmu\)A and \(V_{DD}=0.9\) V. The reported load regulation is 360 \(\upmu\)V/\(\upmu\)A. Line and load regulation report excellent steady-state performance due to the open-loop gain of the LDO exhibits a large voltage gain \(\geq\)60 dB. Finally, Figure 17c shows the dynamic load regulation characterization where a current square wave of 0 \(\upmu\)A to 2.5 \(\upmu\)A amplitude and 25 kHz frequency is applied. The load regulation during low-to-high current transients is 62 \(\upmu\)V/\(\upmu\)A, while during high-to-low current transients it is 23 \(\upmu\)V/\(\upmu\)A. The power consumption of this block is 180 nW, sinking a quiescent current of 200 nA using a voltage supply \(V_{DD}=0.9\) V.  
Figure 16: Location of the LDO voltage regulator inside the microphotography of the whole self-powered tag and a zoomed-in view of its layout.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Element** & **Value** & **Element** & **Value** & **Element** & **Value** \\ \hline \(m_{1}\) & (4.12 \(\upmu\)m/1.8 \(\upmu\)m) \(\times\) 1 & \(m_{n_{1}}\) & (13.835 \(\upmu\)m/1.8 \(\upmu\)m) \(\times\) 8 & \(m_{4}\) & (5.355 \(\upmu\)m/18 \(\upmu\)m) \(\times\) 4 \\ \(m_{2}\) & (4.12 \(\upmu\)m/1.8 \(\upmu\)m) \(\times\) 3 & \(m_{pass}\) & (12.815 \(\upmu\)m/4.5 \(\upmu\)m) \(\times\) 4 & \(C_{M}\) & 2.5 pF \\ \(m_{cat}\) & (3.085 \(\upmu\)m /1.8 \(\upmu\)m) \(\times\) 4 & \(m_{3}\) & (32.99 \(\upmu\)m/1.5 \(\upmu\)m) \(\times\) 40 & \(C_{L}\) & 10 pF \\ \hline \end{tabular}
\end{table}
Table 5: Elements and transistor sizes of the LDO voltage regulator.  
Figure 15: Oscillator post-layout simulation. (**a**) Oscillation frequency at 25 \({}^{\circ}\)C. (**b**) Oscillation frequency temperature sweep.
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 6 Performance Analysis
### Communication Overhead
#### 6.2.2 Communication Overhead between the Cloud Server and the Computation Provider  
In order to perform the proposed secure aggregation operation for local model vectors, we exploit the partial homomorphic decryption capabilities of CPs. The CS sends partially decrypted ciphertexts to the CP and receives ciphertexts from the CP in the proposed PPFL-based system. The length of the partially decrypted ciphertext is also \(2\times K\) bits in [21]. As shown in Algorithm 1, the amount of information communicated between the CS and the CP increases as the number of clients and model parameters increases. Thus, the communication overhead between the CS and the CP can be represented as \(\mathcal{O}(N_{c}\cdot N_{w})\) bits.