esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Directions
### Future Directions
#### 6.2.1 Algorithm Optimization Directions  
Improve the algorithm's adaptive capability. Research how to automatically determine optimal parameters based on network characteristics and data properties, reducing manual tuning requirements. Consider introducing machine learning methods to learn parameter setting patterns from historical data.  
Enhance computational efficiency to support real-time applications. Current offline processing mode limits application scope, necessitating research on stream processing algorithms to support incremental trajectory data processing. Explore parallel computing, distributed processing, and other technologies to address large-scale data challenges.  
Strengthen algorithm robustness. Research how to handle severely degraded data quality situations, such as extremely low sampling rates and frequent GPS signal loss. Consider introducing data repair and interpolation techniques to improve algorithm fault tolerance.
## 2 Materials and Methods
### Fabrication of the NSH PD  
Appositely designed substrates, fabricated by Fondazione Bruno Kessler [28], were employed to obtain the NHS PD. They consist of a crystalline (100) n-doped Si region (150 \(\upmu\)m thick, electrical resistivity \(\rho_{\text{Si}}\) = 0.53 \(\Omega\) cm, number of doping atoms N\({}_{\text{D}}\) = 10\({}^{16}\) cm\({}^{-3}\)) with an Ohmic Au/Cr contact on the back and two Cr/Pt interdigitated (not connected) electrodes on the top. These metal contacts were deposited on a SiO\({}_{2}\) template layer to result electrically isolated from the Si substrate, and they are conceived with a multilayer geometry consisting of 50 \(\upmu\)m wide Pt/Cr combs. Three configurations of interdigitated top contacts were investigated, each different from the other in the number of fingers N, the distance \(d\) between them (N = 3, \(d\) = 950 \(\upmu\)m; N = 6, \(d\) = 450 \(\upmu\)m; N = 10, \(d\) = 250 \(\upmu\)m), and consequently, the active area region formed by the NSH (0.087 cm\({}^{2}\), 0.084 cm\({}^{2}\), 0.079 cm\({}^{2}\), respectively). These multifinger electrode geometries were specifically designed to enhance the charge collection by the external circuit.  
The last step for the manufacture of the device involves the deposition of the SWCNT film, through the dry transfer printing method described above, in the upper part of the substrate so that it adheres to the n-doped Si region and to the Pt/Cr interdigitated electrodes. An etching with hydrofluoric acid was performed before the SWCNT film transfer to remove native oxide on the Si surface. Therefore, this top-down type of realization scheme provides a PD which has three external controls that are the gate electrode (G), forming an Ohmic contact with Si, and the multi-finger source (S) and drain (D) electrodes in contact with the SWCNT film. A scheme of the devices is reported in Figure 1c.  
The present devices have two operating configurations that can be exploited in light detection: photovoltaic (PV) and the photoconductive (PC) modes. In the former, the photogenerated charges, crossing the NSH, are collected by the bottom and the top electrodes (i.e., G and the short-circuited S and D contacts). In the latter, charges travel across the SWCNT film and are collected by the S and D electrodes.
## 5 Case Study
### Test Campaigns  
Once the preliminary analyses had been performed, the system had been installed. First data collection was performed, and collected data were analyzed to identify issues and faults. Data were collected during a three-month period, i.e., the representative period, as it is the minimum period of time to encounter all possible activities conducted by the users of the department. A qualitative analysis was conducted on the collected dataset to identify rough errors. Figure 7 shows a graph of collected data about a bathroom during one day. Stuck values are identified since 4 p.m., because the occupancy raises but never decreases. It is a stuck value because the bathroom can host only one person at a time; therefore, a continuous occupancy of four people represents without any doubt a blunder in the detection. The solution to this specific problem is provided in Table 4.  
After faults of the system were detected, a first test campaign was performed to understand the causes and propose improvements for the system. Detected data were tested in real time by two operators, as described in the method, to identify the causes of system faults. An example of the visualization of real time data in the online platform is shown in Figure 8.  
Figure 6: Comparison between the simulation of the virtual sensor field-of-view in the BIM model (**left**) and the actual field-of-view of the installed sensor (**right**). This first test campaign was followed by a second test campaign, to properly calibrate the system and ensure detected data quality, as shown in Figure 9. The two test campaigns were carried out during different periods of the year. This represented a key aspect for the recognition of lighting contrast issues. The first test campaign was performed in June 2020, with data collection for a three-month period from November 2019 to January 2020. The first test campaign was performed after the end of the first Italian shutdown period due to COVID-19 pandemic (early March-early June 2020). The second test campaign was performed in November 2020, with data collected for another three-month period from July to October 2020, excluding August, during which the building is usually under-occupied due to summer holidays. After the shutdown period March-June 2020, administrative and research activities have been resumed. Therefore, all the data collected for system test and calibration can be considered reliable. Figure 8: Visualization of real time data in the online platform during test campaign. Figure 7: Graph of collected data regarding occupancy overtime in a bathroom (O-T). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Detected Issue, Evaluation Criteria, and Fault Classification** & **Cause Identification and Effects on the System** & **Proposed Hierarchized Adjustments** \\ \hline  & Camera-based sensors not working & Verification of sensor integrity, functioning, and connection \\ \hline  & & Perform a real time test to identify and verify the optimal boundary definition to minimize optical distortion between the \\  & & are not monitored \\
## 2 Related Work
### Security in Communication Systems
#### 2.1.1 Dimensions  
Within X.805, 8 security dimensions are defined which have to be kept in mind when designing a security-related system:  
* Access Control* Authentication
* Non-repudiation
* Data confidentiality
* Communication security
* Data integrity
* Availability
* Privacy
###### Abstract  
Hybrid inspection robots have been attracting increasing interest in recent years, and are suitable for inspecting long-distance overhead power transmission lines (OPTLs), combining the advantages of flying robots (e.g., UAVs) and climbing robots (e.g., multiple-arm robots). Due to the complex work conditions (e.g., power line slopes, complex backgrounds, wind interference), landing on OPTL is one of the most difficult challenges faced by hybrid inspection robots. To address this problem, this study proposes a novel autonomous landing method for a developed flying-walking power line inspection robot (FPLIR) based on prior structure data. The proposed method includes three main steps: (1) A color image of the target power line is segmented using a real-time semantic segmentation network, fusing the depth image to estimate the position of the power line. (2) The safe landing area (SLA) is determined using prior structure data, applying the trajectory planning method with geometric constraints to generate the dynamic landing trajectory. (3) The landing trajectory is tracked using real-time model predictive control (MPC), controlling FPLIR to land on the OPTL. The feasibility of the proposed method was verified in the ROS Gazebo environment. The RMSE values of the position along three axes were 0.1205, 0.0976 and 0.0953, respectively, while the RMSE values of the velocity along these axes were 0.0426, 0.0345 and 0.0781. Additionally, experiments in a real environment using FPLIR were performed to verify the validity of the proposed method. The experimental results showed that the errors of position and velocity for the FPLIR landing on the lines were \(6.18\times 10^{-2}\) m and \(2.16\times 10^{-2}\) m/s. The simulation results as well as the experimental findings both satisfy the practical requirements. The proposed method provides a foundation for the intelligent inspection of OPTL in the future.  
FPLIR; autonomous landing; prior structure data; model predictive control; trajectory planning +
Footnote †: journal: Applied Sciences
# RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
## 6 Experimental Assessment
### Clock Synchronization Accuracy Assessment  
The assessed scenario, shown in Figure 7, consists of four stationary nodes connected in a line topology. The parameters are presented in Table 1.  
To realize the line topology, the node visibility is artificially limited using a whitelist filter in the BLE interface. The filter drops the transmissions arriving from the nodes that are not in the whitelist.  
The Node 0 acts as the timing master for the other nodes. When the network nodes are in the online state, the Node 0 periodically triggers an interruption on the Nodes 1, 2, and 3. When the interruption arrives to Nodes 1, 2, and 3, each node captures its timestamp and sends it to Node 0 through a RESEMBLE message. Messages are forwarded to the Node 0 according to the flooding mechanism defined in the Mesh BLE specifications. The Node 0 collects all timestamps, calculates the clock skew and transmits it to a computer connected through a USB port to the Node 0.  
Figure 8 shows the clock skew distribution between Node 1 (N1) and Node 0 (N0).  
Node 1 is one hop away from Node 0. Among the measured clock skew values, the most recurring ones are between \(-2\) and \(2\) ms, while only in one sample the clock skew (N1-N0) reached \(-3\) ms.  
Figure 9 shows the clock skew distribution between Node 2 (N2) and Node 0 (N0).  
The clock skew at the distance of two hops from N0 increases, as Node 2 is synchronized by Node 1. Most of the time, the clock skew is between \(-3\) and \(3\) ms, while clock skew (from N2 to N0) reached \(-3\) ms in only two samples.  
Figure 10 shows the clock skew distribution between Node 3 (N3) and Node 0 (N0).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Node count & 4 \\ Timeslot count & 8 \\ Synchronization period & 10 s \\ Clock sampling period & 5 s \\ Experiment duration & 30 min \\ Result sample count & 360 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testbed parameters.  
Figure 7: Scenario for clock synchronization accuracy assessment.  
As was expected, the clock skew at a distance of three hops from Node 0 increases and presents the highest variability. In fact, most times, the clock skew is between \(-3\) and \(3\) ms, while in one sample, the clock skew (from N3 to N0) reached \(-3\) ms.  
Figure 11 shows the MaxAbsSkew between all the network nodes.  
Figure 8: Clock skew distribution (from N1 to N0).  
Figure 10: Clock skew distribution (from N3 to N0).  
Figure 9: Clock skew distribution (from N2 to N0).  
In these experiments, the MaxAbsSkew never exceeded 6 ms.
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 4 Analysis of Authentication Factors
### Related Research Conducted on MFA
As a third factor, the approach uses a PC ID so the user cannot log in from a different PC. The PC needs to be allowed by the admin beforehand. The advantage of this approach is that it adds a layer of security, is easy to use, and has low-cost requirements. This recall-based approach is somewhat similar to the knowledge-based approach and has the same disadvantages. The user needs to remember what image was selected during sign-up, which increases the cognitive load on the user and degrades the user experience. There is a trade-off between ease of use and achieving security. Hence, this approach does not fit into our system, wherein we want to accomplish both ease of use and security. [PERSON] et al. [93] proposed a fingerprint-recognition-based approach. The fingerprint template stored in the database is in the form of a 3D spiral curve. Even if the database is compromised, it can not extract helpful information from the stored template. Once the template is compromised, it creates a new template for the same fingerprint by changing the key-set values. The only limitation is when the fingerprints are stolen from another source and the spoofed ones are used with this system. This system uses an external device and hinders the user experience, which does not suit our defined requirements. [PERSON] et al. [94] utilized a chatbot capable of generating authentication challenges for the system. It is also in a situation to evaluate user responses, along with the necessary functionalities for the smooth operation of the framework system. An extensive analysis of most of the prominent measures researched for MFA from 2016 to 2022 has been carried out. Table 4 presents a detailed analysis of the advantages and limitations of these mentioned methods. The cost factor associated with the implementation of MFA depends on the type of additional hardware and software required for MFA implementation. This is in addition to the cost of the system implementation or even the infrastructure being hired from a CSP [95]. This research considers our current requirement of not including any additional hardware or software for the functioning of MFA. Moreover, from these analytical results, it can be inferred that the _Impersonation Attack_ scenario for authentication, in particular, Cloud Authentication, requires due deliberation and further research.
# Machine Learning with Evolutionary Parameter Tuning for Singing Registers Classification
## 2 Materials and Methods
### SHapley Additive exPlanations  
SHAP is a remarkable method used to interpret the output of ML models [38]. The latter is based on Shapley values, borrowed from cooperative game theory, which allocates credit for an ML model prediction to each feature or group of feature values. Such an approach ensures that the contribution of each feature is fairly distributed, thus providing a clear and consistent explanation of the considered ML model behavior.  
SHAP operates by decomposing ML model output into the contributions of each feature. In particular, the contribution of each feature to the model prediction is represented by a SHAP value. Such values aim to explain the importance of each feature in determining the model output. Mathematically, the SHAP value \(\phi_{i}\) for a feature \(i\) is defined as:  
\[\phi_{i}=\sum_{S\subseteq N\setminus\{i\}}\frac{|S|!(|N|-|S|-1)!}{|N|!}[f(S \cup\{i\})-f(S)] \tag{9}\]  
where \(N\) is the set of all features, \(S\) is a subset of \(N\) that does not include feature \(i\), \(|S|\) is the number of features in subset \(S\), \(f(S)\) is the model prediction using the features in subset \(S\).  
The above-reported formula ensures that the contribution of each feature is fairly distributed based on its impact on the model prediction. Indeed, the factorial terms \(|S|!\) and \((|N|-|S|-1)!\) account for the different permutations of features, ensuring that each feature contribution is weighted appropriately. As a result, by breaking down a model output into the contributions of individual features, SHAP provides a clear and intuitive explanation of how the model makes its predictions.  
It is worth noting that one of the key strengths of SHAP is its neutrality towards models. The latter means that SHAP values can be computed for any ML model, regardless of its complexity or type. Indeed, this model-agnostic nature allows SHAP to generate consistent explanations across different models, making it a versatile tool for interpreting complex model behaviors. As a final result, SHAP values can be leveraged to convey the significance of each feature to a human user. Finally, this method can be especially beneficial in fields like finance, healthcare, and any area where comprehending a model's decision-making process is essential.
# Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security
## 3 Proposed Method
### Optimal Key-Based Signcryption Stage  
With the aid of the signcryption method and the aforementioned perfect key system, the picture will be safe. A public key primitive called \"signcryption\" uses the recipient's public key and the value to determine the hash value, combining the capabilities of encryption and digital signatures into a single operation (S). The result of the hash function (\(O_{H}\)) as a 128-bit evaluation.  
1. Choose the \(\left[1\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots \ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\ldots\right]\) range for the sender value \((S)\).
2. The recipient's best public key (\(opt-B_{K2}\)) is used to find the sender's hash function, which converts the sender's 128-bit plain image into two 64-bit hash outputs (\(O_{H1}\) and \(O_{H2}\)). \[O_{H}=Hash\Big{(}B_{K2}^{S}mod\ P_{N}\Big{)}\] (20)
3. The image is encrypted using the encryption \(E\) process from this hash, and the result is a cypher image (\(C_{I}\)) with a 64-bit output value. \[C_{I}=Enc_{O_{H1}(Image)}\] (21)
4. The sender then uses the restricted keyed hash function KHf's (\(O_{H2}\)) value to obtain the image's hash. Additionally, this output value is considered to be (\(M_{I}\)). \[M_{I}=KH_{f}O_{H2}(Image)\] (22)
5. Using the public key and prime factor values, the sender finally calculates the encrypted image, which is represented by: \[P_{I}=\frac{S}{(M_{I}+A_{R1})modP_{f}}\] (23)Sending the three different values independently across secure transmission channels would strengthen security and sign-encrypt the image, which the sender owns (\(C_{I},M_{I}\) and \(P_{I}\)). These three values are then transmitted to the recipient.
# Adaptive Natural Gradient Method for Learning of Stochastic Neural Networks in Mini-Batch Mode
## Natural Gradient Learning
### Adaptive Online Natural Gradient Learning  
The Fisher information matrix cannot be explicitly calculated unless true input distribution \(q(\mathbf{x})\) is given, which is not plausible in real applications. To solve the problem, [PERSON] et al. [11] proposed an adaptive method of obtaining an approximation of the natural gradient during training, and give an update rule for a simple model with single output, which is written as  
\[\hat{G}_{t+1}^{-1}=(1+\epsilon_{t})\hat{G}_{t}^{-1}-\epsilon_{t}\left(\hat{G}_{t }^{-1}\frac{\partial f}{\partial\theta}\right)\left(\hat{G}_{t}^{-1}\frac{ \partial f}{\partial\theta}\right)^{T}, \tag{15}\]  
where \(t\) denotes the number of updates and \(\epsilon_{t}\) is a user-defined parameter with small value. In the computational experiments done in [PERSON] et al. [20] and [PERSON] et al. [21], the formula, \(\epsilon_{t}=1/t\), is recommended. Using the adaptive estimation for inverse of Fisher information matrix, the parameter update rule of online adaptive natural gradient method can be given as  
\[\mathbf{\theta}_{t+1}=\mathbf{\theta}_{t}-\eta\hat{G}_{t+1}^{-1}\frac{\partial l(\mathbf{ x},\mathbf{y},\ \mathbf{\theta}_{t})}{\partial\mathbf{\theta}}. \tag{16}\]  
[PERSON] et al. [21] extended it to various stochastic models, and showed its practical efficiency through a number of experiments using benchmark data. In addition, there have been several theoretical works, which analyze average dynamics of adaptive natural gradient learning and show its efficiency [19; 23].  
However, the online adaptive natural gradient method has still several disadvantages when it is applied to big data analysis. First of all, it is not proper to the problems with a large data set because the adaptive calculation of natural gradient should be done in online learning mode. In addition, the user-defined parameter \(\epsilon_{t}\) in Equation (15) can make learning instable especially when the method is applied to batch mode learning in which a fixed data set is repeatedly used. To overcome the limitations, [PERSON] et al. [27] proposed to use an adaptive step size and showed that the stability and convergence speed are improved, but it still uses online learning mode, which is not desirable in the case of a large data set. On the other hand, in our preliminary work [22], we proposed a new adaptive natural gradient for mini-batch mode and give an explicit algorithm for a simple stochastic neural network model used in [20]. In this paper, we extended our preliminary work to the various stochastic models used in [21], and give explicit algorithms for the two representative stochastic models: The additive Gaussian noise model and the logistic regression model.
## 1 Introduction
However, in many cases, the models used are not self-explanatory, thus allowing the results obtained to become of complex. Therefore, intelligent models that are capable of bringing interpretable results are fundamental for the dissemination of knowledge about a particular scientific context. The fuzzy neural networks act as a model capable of bringing the interpretability to the results extracted from a database. These models should act in synergy to obtain positive results that are easy to understand [12]. This work proposes the use of the fuzzy neural network proposed by [PERSON] and [PERSON] [13] to act in the Brazilian meteorological system dataset in the forecast of rains and temperatures in the capital city of Minas Gerais, which has a high number of river sources and can generate shortages in the country if the volumes of rainfall or the temperature increases uncontrollably. It uses the concept of fuzzy rules based on equally spaced membership functions and fuzzy neurons for the construction of a fuzzy inference system. Connected to this system is a neural aggregation network that can perform the necessary approximation of the expected responses. The main novelty of this paper is the extraction of fuzzy rules based on real data of temperature and rainfall in the Belo Horizonte, in addition to the use of a hybrid model to act efficiently in predicting the best results. This is the first work in the literature that seeks to predict rainfall and temperature indices in this crucial state of Brazil. This paper proposes the efficiency of the model in fuzzy rules extraction. Prediction tests were performed with intelligent models that are commonly used in the literature for predicting rains and temperatures. The remainder of the paper is organized as follows: In Section 2, the main concepts and themes presented in the paper are presented to the reader, including aspects related to the state of Minas Gerais and concepts about meteorology and intelligent models. In Section 4, we discuss aspects of the architecture and the model used in the paper for extracting knowledge from the database. In Section 5, the tests are described, the methodology used, and the models chosen to compare the results. In Section 6, the conclusions and the expected future works are presented to the reader.