esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Annual Rainfall Prediction
### RF-IMF Prediction Model  
In this proposed model for annual rainfall prediction, as the first step, the original time series was decomposed into IMFs and the Pearson correlation coefficient(PCC) [28] was calculated among the different IMFs and the original time series. PCC is defined as the ratio between the covariance of two signals and the product of their standard deviations. The values of coefficients obtained are listed in Table 1.  
PCC values show that adjacent IMF values are highly correlated; thus, the random forest model for predicting the future IMFs was modeled with adjacent IMFs as inputs. The flowchart (Figure 1) depicts the modeling of the RF-IMF forecasting model. Figure 7 depicts the intrinsic mode functions obtained via empirical mode decomposition of the original time series. Blue colour indicates testing dataset and pink colour is the predicted IMF's. Here, IMF1 to IMF3 are the high-frequency components, IMF4 and IMF5 are the medium-frequency components and IMF6, the low-frequency trend signal.  
The IMFs from 1871 to 2000 came from the testing dataset, while the IMFs from 2001 to 2020 were their predicted values. The predicted IMFs from 2001 to 2020 were combined to obtain the corresponding year's annual rainfall. Figure 8 shows the bar chart of the predicted values versus the actual rainfall for the RF-IMF model. The bar chart and trend lines clearly indicate that the error values have been reduced and succeeded in predicting the trend in annual rainfall.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **x(t)** & **imf\({}_{1}\)** & **imf\({}_{2}\)** & **imf\({}_{3}\)** & **imf\({}_{4}\)** & **imf\({}_{5}\)** & **imf\({}_{6}\)** \\ \hline x(t) & 1.00 & 0.77 & 0.36 & 0.28 & 0.18 & 0.23 & 0.03 \\ imf\({}_{1}\) & 0.77 & 1.00 & 0.11 & \(-\)0.03 & \(-\)0.05 & \(-\)0.08 & \(-\)0.05 \\ imf\({}_{2}\) & 0.36 & 0.11 & 1.00 & 0.09 & 0.00 & 0.00 & 0.03 \\ imf\({}_{3}\) & 0.28 & \(-\)0.03 & 0.09 & 1.00 & 0.03 & \(-\)0.10 & 0.00 \\ imf\({}_{4}\) & 0.18 & \(-\)0.05 & 0.00 & 0.03 & 1.00 & 0.08 & 0.00 \\ imf\({}_{5}\) & 0.23 & \(-\)0.08 & 2.00 & \(-\)0.10 & 0.08 & 1.00 & 0.17 \\ imf\({}_{6}\) & 0.03 & \(-\)0.0 & 0.00 & 0.00 & 0.03 & 0.17 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 1: Pearson correlation coefficient matrix among original signal and the IMFs.  
Figure 7: Plot of six IMFs from 1871 to 2020.
# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
###### Abstract  
Satellite computing represents a recent computational paradigm in the development of low Earth orbit (LEO) satellites. It aims to augment the capabilities of LEO satellites beyond their current transparent relay functions by enabling real-time processing, thereby providing low-latency computational services to end users. In LEO constellations, a significant deployment of computationally capable satellites is orchestrated to offer enhanced computational resources. Challenges arise in the optimal allocation of terminal services to the most suitable satellite due to overlapping coverage among neighboring satellites, compounded by constraints on satellite energy and computational resources. The satellite service allocation (SSA) problem is recognized as NP-hard, yet assessing allocation methods through results allows for the application of deep reinforcement learning (DRL) to obtain improved solutions, partially addressing the SSA challenge. In this paper, we introduce a satellite computing capability model to quantify satellite computational resources. A DRL model is proposed to address service demands, computational resources, and resolve service allocation conflicts, strategically placing each service on appropriate servers. Through simulation experiments, numerical results demonstrate the superiority of our proposed method over baseline approaches in service allocation and satellite resource utilization, showcasing advancements in this field.  
LEO satellite network; satellite computing; satellite service allocation; deep reinforcement learning +
Footnote †: journal: _aerospace_
## 5 Discussion
### Limitations of the Present Study
Other metrics specifically deal with central fixation bias, either by penalizing it (Shuffled AUC) or by quantifying the similarity beyond central fixation bias (Information Gain). Thus, human-CNN similarity should decrease if it mainly stems from biases in human scene viewing. Finally, the Earth Mover's Distance considers how far the areas selected by humans and XAI are spatially removed from each other, allowing for a more fine-grained comparison of the attended areas. Given these complementary capabilities of different metrics, systematically comparing their results could enhance our understanding of the specific factors driving the similarity or dissimilarity between humans and CNN. Finally, an important conceptual limitation lies in the very nature of attention maps as a purely spatial method. At best, attention maps can tell us _where_ humans or CNN attend, but not _how_ they use the information presented in this area (cf. [45]). Thus, despite a high overlap in the attended areas, humans and CNN might attend to completely different features. For instance, CNN rely on object texture more strongly than on shape, while humans do the reverse [73; 74]. Still, the attention maps of humans and CNN would highlight the same object. One way to consider this when comparing humans and CNN is to generate different attention maps for different CNN layers that process different types of information [10; 49]. For humans, an analogous indicator of different information processing activities is fixation duration, with longer durations reflecting a higher processing depth [75]. Thus, differentiating between levels of processing both for CNN and humans seems desirable and technically feasible.
## 3 The Proposed Network Model
### Information Extraction Layer
For each connection block, the mapping of the \(i\)th layer \(S_{i}\) can be expressed as:  
\[S_{i}=\sigma(w_{i}[S_{1},S_{2},\dots,S_{i-1}]+b_{i}) \tag{11}\]  
\(w_{i}\) and \(b_{i}\) respectively represent the weights and offsets of the \(i\)th layer, \(S_{1},S_{2},\dots,S_{i-1}\) represent the feature maps extracted by each layer before the \(i\)th layer, and \(\sigma(\cdot)\) is a set of Relu activation functions. We define \(F_{n-1}\) as the input of the nth subnetwork, then \(F_{n}=F^{\prime}+F_{n-1}\)  
The difference in convolution kernel size and arrangement order leads to different feature maps being extracted from each module. In order to obtain more abundant features at the same level, multiple modules are connected in a multi-path manner. In this paper, we set I to 3, and then by changing the kernel size and convolution order, we can obtain modules with multiple functions to extract various feature maps. The convolution kernel size of each line is set to \(5\times 5\) and \(3\times 3\) respectively. The operation in the dense connection module introduces residual learning, which can further improve the information flow in the network, promote the transmission of information and gradients, and prevent the disappearance and explosion of gradients.
## 4 Impossibility Results
### Synchronization and State Observation Problems
#### 4.1.2 Global Snapshot Recording (GSR)  
The GSR problem is specified as follows.  
* **Safety specification of GSR** states that a recorded global state should include the recording of the local state of each process, that all in-transit messages in each channel should be recorded, and that such a global state should be consistent.
* **Liveness specification of GSR** states that once a recording of a global snapshot is initiated, its recording should be eventually completed.  
Theorem 4.3: _In a system with even one Byzantine process, the distributed GSR problem is subject to the same limitations (exposure to false positives and false negatives) as the CD problem, resulting in safety and liveness violations._  
Proof: Solving GSR requires satisfying  
\[\Phi_{GSR}(e_{i}^{\vartheta})\stackrel{{ def}}{{=}}\bigwedge_{h}(e _{h}^{x_{h}}\to e_{i}^{y_{h}}|_{E}=e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{F}=1) \wedge\phi(e_{i}^{\vartheta}),\]  
where \(e_{h}^{x_{h}}\) is an event where the process records its local state, \((\forall h)\,y_{h}\leq\vartheta\), \(\phi\) is a predicate capturing the other requirements of the GSR problem (the recorded local states at the various processes are consistent, the channel states recording is complete) besides the first predicate, \(e_{i}^{\vartheta}\) is an event where the completion of the global state recording is detected, and \(\to\) is defined on snapshot recording algorithm messages.  
As \(e_{h}^{x_{h}}\) is an internal event, from Theorem 4.1 for the CD problem, detecting \(e_{h}^{x_{h}}\to e_{i}^{y_{h}}\) is susceptible to false positives and false negatives. Thus, it cannot be guaranteed that the predicate \(e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{E}=e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{F}\) in the formula \(\Phi_{GSR}\) can be satisfied. Hence, GSR cannot be solved.  
A false positive of the CD problem can result in a safety violation--an inconsistent global state, supposing the false positive event is where the process is supposed to record its local state as per the algorithm but does not, receives application messages, and later records the local state--in the GSR problem. Alternately, in the definition of \(\Phi_{GSR}\), let \(e_{h}^{x}\) be an event where the process completes the recording of its local state and states of incoming channels. A false positive of the CD problem can result in a safety violation--an incomplete global state, with some local states and channel states not recorded--in the GSR problem. A false negative of the CD problem is a liveness violation--a global snapshot recording detection never occurs--in the GSR problem.
## 5 Discussion  
The capability of imaging reconstruction of the fabricated metamaterial aperture antenna was explored based on the measured data. The specific parameters of the MAA's coherent computational imaging system are shown in Table 2. Figure 9a shows the original sparse target scene, which includes two scatterers, and Figure 9b shows this scene reconstructed via the pseudo-inverse method. It can be seen in Figure 9 that the designed MAA could be effectively used to reconstruct the target scenes. In conclusion, when using the complementary electric-field-coupled (CELC) element and the complementary Jerusalem cross (CJC) element with a random distribution ratio of 4 to 1, the radiation efficiency of the MAA can achieve 60%, and the far-field patterns has good orthogonality, which could be effectively used to reconstruct the target scatterers scene.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Field of View(Elevation) & \(-60\)–\(60^{\circ}\) \\ Frequency sampling interval & 0.1 GHz \\ Elevation sampling interval & \(1^{\circ}\) \\ Azimuth sampling interval & \(1^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters of the metamaterial aperture antenna.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Field of View(Elevation) & \(-60\)–\(60^{\circ}\) \\ Frequency sampling interval & 0.1 GHz \\ Elevation sampling interval & \(1^{\circ}\) \\ Azimuth sampling interval & \(1^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured field patterns of the metamaterial aperture antenna at different frequencies.  
Since the capability of imaging reconstruction of the fabricated metamaterial aperture antenna is explored based on the measured far-field data, the range of the target is in the far-field region. In this case, since the dimension of the fabricated metamaterial aperture antenna is 250 mm \(\times\) 250 mm \(\times\) 0.5 mm, and the center frequency is 35 GHz, the far-field region is \(r\geq\) (2 \(\times\) 0.25 m \(\times\) 0.25 m)/\(\lambda_{0}\) = 14.5 m (\(\lambda_{0}\) is the wavelength at the center frequency in free space). As a result, the true target is assumed to be 15 m away from the metamaterial aperture antenna.  
The capability of imaging reconstruction of the new proposed metamaterial aperture antenna was determined based on simulated targets at the present stage [21]. As for larger targets, we also employed a simulated metal sheet target to validate the capability of imaging reconstruction of the new proposed MAA, as shown in Figure 10 below. In Figure 10, we can see that the measured far-field data could be used to reconstruct the location and shape of the metal sheet more precisely, but not perfectly. Going forward, more practical experiments with a more precise reconstruction of larger targets will be crucial.
## 9 Real-World Challenges in AI Implementation: Regulatory and Ethical Considerations
### Ethical Considerations  
AI deployment also raises significant ethical concerns, particularly regarding fairness, accountability, and bias in decision-making processes. In applications like traffic management and intrusion detection, AI models may inadvertently reinforce biases present in the training data, leading to unfair outcomes for certain groups of individuals.  
To mitigate these concerns, I suggest the following:  
* Fairness-Aware Learning: Techniques such as adversarial debiasing and fairness constraints during model training can help reduce biases in AI systems. Ensuring that models do not disproportionately favor or disadvantage any specific group is essential for building trust in AI applications.
* Explainable AI (XAI): AI models, especially deep learning systems, are often considered \"black-box\" models, making it difficult to understand their decision-making process. By incorporating explainable AI techniques, such as LIMEs (Local Interpretable Model-Agnostic Explanations) or SHAPs (Shapley Additive Explanations), we can provide transparency and accountability in AI decisions, which is crucial for high-stakes applications like traffic management and network security.
* Accountability and Oversight: Clear accountability frameworks should be established for AI models deployed in critical applications. Ensuring human operators remain in the loop and override AI decisions when necessary is essential to mitigate risks.  
Addressing these regulatory and ethical challenges is key to the widespread adoption of AI technologies in communication networks and urban management. By implementing privacy-preserving techniques, ensuring fairness, and enhancing transparency, we can ensure that AI solutions are both effective and responsible. Future work should focus on further developing these approaches and ensuring their integration into real-world deployments.
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 2 Related Works
### Deep Learning-Based Log Anomaly Detection
#### 2.2.3 Deep Learning For Log Anomaly Detection  
The extracted features are input into the deep learning model to proceed with learning, and the normalities and abnormalities of the logs are classified based on the learned model. In this paper, by applying the following algorithms of models applied in the previous studies, we analyze the applicability of federated learning and compare the performance of each model.  
1. The Long Short Term Memory (LSTM) network is a series of recurrent neural network algorithms for time-series data processing and text recognition, and studies have applied them to log data with text. [PERSON] et al. proposed Deeplog, which models sequential patterns by applying the LSTM and identifies anomalies in the log [14]. [PERSON] et al. proposed LogRobust, which detects anomalous logs by applying the attention mechanism-based bidirectional LSTM [15]. [PERSON] et al. proposed LogAnomaly, which identifies anomalous logs based on sequential and quantitative log information [16].
2. The One-dimensional Convolutional Neural Network(CNN1D) algorithm can be applied to sequential data, unlike the two and three-dimensional CNN algorithms, because it calculates in one direction, and this method derives excellent performance in text classification through model design [17]. In addition, [PERSON] et al. proposed a 1D CNN-based log anomaly detection model that uses fewer parameters than the models of the existing family of recurrent neural networks [18].
3. Recently, a hybrid model that combines two or more deep learning models has been proposed, and various studies have been conducted, especially in text classification and prediction problems, such as those concerning log data [19; 20; 21]. Among them, in text classification research, a hybrid CNN model was effective in extracting local features, such as words, and an LSTM model was effective in extracting features for contextual information or word order to derive higher performance than the existing single models [19]. Therefore, we perform anomaly detection on log data by combining the LSTM model with the proposed CNN1D model and comparing the performance results with those of existing models.
## 2 Materials and Methods
### Data Extraction  
The articles were checked for eligibility based on the set criteria, following this, the number of articles included for further analysis decreased to 19. In the second phase, the journal names and author details were concealed and then distributed between the two authors (M.A & L.A) who were not involved in the initial search. To check the degree of consistency between these two authors, inter-rater reliability was assessed on a sample of articles before the allotment of the finalized articles. [PERSON]'s kappa showed an 88% agreement between the 2 authors. At this phase, the authors critically assessed the articles based on Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2) guidelines [24]. This tool is used for quality of studies reporting on diagnostic tools, conducted based on four domains (patient selection, index test, reference standard, and flow and timing) which are assessed in terms of risk of bias and applicability concerns. Following this, the  
\begin{table}
\begin{tabular}{c c} \hline \hline Research question & What are the AI applications designed for OGS, and its performance in diagnosis, planning and prediction of the prognosis of OGS \\ \hline Population & Patients who underwent investigations for OGS (Maxillary Osteotomy, Mandibular Osteotomy, Bilateral Sagittal Split Osteotomy (BSSO), Genioplasty, Le Fort 1 Osteotomy) \\ \hline Intervention & AI applications for diagnosis, treatment planning and prediction of the prognosis of OGS \\ \hline Comparison & Specialist opinions, Reference standards \\ \hline \multirow{4}{*}{Outcome} & Measurable or predictive outcomes such as Accuracy, Sensitivity, Specificity, ROC = Receiver Operating Characteristic curve, AUC = Area Under the Curve, ICC = Intraclass Correlation Coefficient, Statistical Significance, F1 Scores, vDSC: Volumetric Dice Similarity Coefficient, sDSC: Surface Dice Similarity Coefficient \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the PICO (P = Population, I = Intervention, C = Comparison, O = Outcome) elements.  
authors further disagreed regarding the inclusion of one article, which had not clearly mentioned quantifiable outcome measures of performance. An opinion was obtained from [PERSON] and then the article was excluded. Finally, 18 articles were subjected to qualitative synthesis (Figure 1).
## 3 Results and Discussion
### In Vivo Measurements  
Table 1 represents the summary of the in vivo experiments. Only data regarding the finger tapping with the contralateral hand were proposed because of the absence of any activation during finger tapping with the ipsilateral hand due to the lateralization of the motor area. For measurements at short SDD, contrast and CNR are higher for gated measurements, confirming that the time gated approach is necessary in order to perform time resolved functional measurements at short SDD. For long SDD measurements, this finding was not confirmed. In fact, only for subject 1 and for HHb in subject 2 did we have a slight improvement in contrast and CNR.  
Figures 3 and 4 show the changes in hemoglobin species (Hb) concentrations averaged over the five repetitions of finger tapping with the contralateral hand for subject 1 and 2, respectively. Error bars represent the standard deviation over the five repetitions. Normally, brain activation is identified by a task related increase in O\({}_{2}\)Hb concentration and a corresponding decrease in HHb. We can clearly see this pattern in the gated column of both Figures 3 and 4, demonstrating the capability of a time gated system in performing functional measurements independently of the SDD, while in the non-gated approach this trend can be recognized only for long SDD.  
Figure 3: In vivo measurements of subject 1. Changes in O\({}_{2}\)Hb (red) and HHb (blue) averaged over the five repetitions of finger tapping with the contralateral hand for the gated and non-gated approach (left and right column, respectively) at the short and long distance (upper and bottom lines, respectively). Error bars represent the standard deviation over the five repetitions.
## 4 Workflow-Based Access Control with Authorization Policies
### Delegated Policy Evaluation
#### 4.3.2 Authorizing Delegated Policy Evaluation  
The previous need-to-know example illustrated how delegated policy evaluation can help with minimizing data breaches by outsourcing the policy evaluation rather than by sharing sensitive data across multiple microservices.  
However, delegated policy evaluation may also impose risks as highlighted in the first example of the motivating use case. The Mobile Health Tracker platform submits aggregated healthcare results and contextual information (time and location) to the Medical Records system when certain (aggregated) health parameters are above or below critical thresholds. Such an example is illustrated in Figure 13.  
From the point of view of the Medical Records microservice, this policy will again be evaluated through delegation to the Mobile Health Tracker microservice. For each new health parameter, the authorization policy should compute the average of the blood glucose values of the last 24 h. When this average value is above a threshold, defined by the physician, then access to all health parameters of the past 24 h is granted. However, if the physician can easily change the threshold, he/she might be able to retrieve all health parameters.  
Our framework addresses this challenge by ensuring that the delegated evaluation of policies is subject to authorization, as well. More specifically, delegated policies that compute values on sensitive health parameters must be digitally signed by each owner of any resource being accessed. As the health parameters are owned by the patient, he/she must authorize the evaluation of the policy by digitally signing the policy. Our proof-of-concept implementation relies on identity-based cryptography [24] in which a publicly known string representing an individual is used as a public key so that each party can easily verify the signature based on the '$[userid]' identifier. All authorizations of a policy are included in the corresponding field of the delegated policy.  
Figure 12: Need-to-know authorization policy for the Medical Records microservice.