esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 5 Advancements in Optical Wireless Communications
### Visible Light Communication (VLC)
There is no automatic flickering between frames when using VPPM. The CSK can cause flickering when different powers are applied to multiple light sources [295]. In indoor VLC systems, the user is within the light source's LoS. Because of a room's/office's objects blocking VLC LoS, the optical power significantly decreases; hence, VLC performance is consequently degraded. Markov chain-based modelling and the Kalman filter can be used to estimate the likelihood of LoS blocking and monitor user motion, respectively, to record and anticipate blocking patterns according to the random movements of users. Filtering, such as the blue filtering method for PC-LED VLC systems [296; 297; 298], can help to minimize noise influence significantly. In order for VLC to become a ubiquitous mobile technology, high-speed and continuous communication must be developed in the context of user mobility within and between VLC cells. VLC, which differs from RF in terms of user mobility, introduces significant issues. For instance, even in a tiny VLC cell, the user SNR varies dramatically as users move within the cell [299]. When developing various link-layer methods such as frame aggregation and rate adaptation, these quick alterations must be taken into account. It is vital to have appropriate coverage in all regions of an indoor space when employing LEDs for communication. User mobility requires the smooth handling of user devices horizontally (VLC to VLC) and vertically (VLC to RF) while linking VLC to RF networks. For example, the Tx's light signals must be detectable by the receiver anywhere in the building, which necessitates a wider Tx emission angle and a higher FoV [300]. For VLC systems to be commercially viable, especially in the consumer market, mobility and signal coverage issues must be addressed. A few of the potential applications of VLC, where the present research emerges, are Li-Fi, indoor high-speed internet connectivity, local ization systems, interplanetary communication, underwater communication, industrial automations, robotics control/guidance, aircraft cabins, hospitals, car-to-X communication, traffic systems, intelligent transportation systems, vehicle-to-vehicle communication (V2C), healthcare, museums, street lights, and virtual and augmented reality.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
###### Abstract  
The rapid growth of smartphone financial services raises the need for secure mobile authentication. Continuous authentication is a user-friendly way to strengthen the security of smartphones by implicitly monitoring a user's identity through sessions. Mobile continuous authentication can be viewed as an anomaly detection problem in which models discriminate between one genuine user and the rest of the impostors (anomalies). In practice, complete impostor profiles are hardly available due to the time and monetary cost, while leveraging genuine data alone yields poorly generalized models due to the lack of knowledge about impostors. To address this challenge, we recast continuous mobile authentication as a few-shot anomaly detection problem, aiming to enhance the inference robustness of unseen impostors by using partial knowledge of available impostor profiles. Specifically, we propose a novel deep learning-based model, namely a local feature pooling-based temporal convolution network (LFP-TCN), which directly models raw sequential mobile data, aggregating global and local feature information. In addition, we introduce a random pattern mixing augmentation to generate class-unconstrained impostor data for the training. The augmented pool enables characterizing various impostor patterns from limited impostor data. Finally, we demonstrate practical continuous authentication using score-level fusion, which prevents long-term dependency or increased model complexity due to extended re-authentication time. Experiments on two public benchmark datasets show the effectiveness of our method and its state-of-the-art performance.  
continuous authentication; touch-gesture biometrics; few-shot anomaly detection; data augmentation; temporal convolution network +
Footnote †: journal: Applied Physics Letters  
10 September 2022  
Accepted: 12 October 2022  
14 October 2022  
Multi-user authentication is a promising way to provide an additional security layer for mobile devices, which monitors user identities implicitly through their behavioral biometrics using device built-in sensor data [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15].  
Unlike physical biometrics, which utilizes human physical attributes such as face and fingerprint, behavioral biometrics exploits user behavioral patterns such as screen-touch gestures and gait patterns. Physical biometrics are generally more unique and stable than behavioral ones; however, they require users' cooperation to capture their biometric traits, such as touching a sensor with a finger or turning a face to a camera. In contrast, behavioral biometrics can capture users' biometric data tacitly with built-in sensors of mobile devices such as the touch screen, accelerometer, and gyroscope. Therefore, behavioral biometrics can actualize implicit continuous authentication, further enhancing the security of mobile devices without deteriorating the user experience.  
Article
## 3 Results and Discussion
### Feasibility of Applying Circular Economy Strategies to Different Building Types
#### 3.3.6 Operation and Maintenance Stage  
The operation and maintenance stage received the lowest score (11), ranking it as the phase with the least influence on adopting CE principles. Only one participant rated the stage with a medium impact on the adoption while the rest rated it as low. While interviewees acknowledged that this stage provides opportunities to implement CE principles such as energy management and water efficiency, they emphasized that its effectiveness heavily relies on decisions made during the earlier phases. Several interviewees shared the perspective that the regular maintenance of mechanical and electrical systems plays a crucial role in supporting CE principles in this stage. They emphasized that consistent upkeep ensures these components remain in good condition throughout their operational life and retains its value at the end of life. In this regard, A1 pointed out that \"_Having a proper maintenance programme in place is so important at this stage. For example, well-maintained HVAC systems can often upgrade or reuse them rather than having to replace them entirely. That way, they can be refurbished, resold, or repurposed, which fits perfectly with CE goals_\". C3 and CN1 shared a similar perspective, emphasizing building management teams and facility operators play essential roles in promoting operational practices with CE goals among users, such as recycling programs and energy efficiency initiatives.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 2 Water Supply System Architecture
### Pumping Station  
The pumping stations are generally located close to the water treatment station and a storage facility. The treated water is directly pumped into the storage facilities, such as reservoirs, tanks etc. In this station, energy is consumed the most. [PERSON] et al. [28] reported that pumping accounts for 70% of the energy used during WSSs operation. Similarly, the authority regulating Portuguese Water and Waste Services disclosed that WSS consumes approximately 10 GWh/year for pumping alone, with an estimated cost of around $138 million [29]. Pumps are one of the major components of a pumping station. Pumps (such as high lift pumps) may be used to pump water directly into transmission mains and distribution systems. To raise the pressure along the pipe, booster pumps can be situated anywhere along the system. Booster pump stations are generally situated remotely in areas with hilly topography where pressure areas are required [30].
## 2 Materials and Methods
### Proposed GP-Based Feature Construction Approach  
#### 2.3.1 Genetic Programming Basics  
Generally, the genetic algorithm (GA) is considered the main technique of the evolutionary computation paradigm, where potential solutions are codified into vectors called chromosomes [39]. On the other hand, genetic programming (GP) works similarly to GA, although the main difference lies in the type of encoding, which uses syntax trees instead of vectors. A tree intrinsically contains a computer program or mathematical expression [26]; hence, this property has been exploited to address the problem of automatic feature construction [27].  
The GP codification requires defining a terminal set containing the problem's independent variables and a function set containing mathematical functions and arithmetic operators. From both sets, an inverted tree-like representation is constructed in which leaf nodes are the inputs given by the terminal set, and inner nodes are the functions given by the function set that combine the inputs to generate an outcome in the root node at the top node. A didactic example is shown in Figure 2, where the terminal set is \(\{x,y,z\}\), and the function set is \(\{+,-,\times,\div,\min,\max\}\).  
GP is a population-based metaheuristic in which the evolutionary process is enabled by the genetic operators of selection, crossover, and mutation applied to the population's individuals. This way, the population evolves until reaching a predefined number of generations by minimizing (or maximizing) a fitness function determining the survival chances of every individual for the next generation [26].  
The selection operator chooses individuals from the population based on their fitness. A common approach is selection by tournament, where two or more individuals are chosen randomly from the population, and the fittest individual is the winner and, therefore, selected.  
Figure 2: An example of a syntax tree to codify a potential solution in GP.  
The crossover operator simulates sexual reproduction in the natural world, where two selected individuals interchange their information to create offspring. This operator cuts an individual's subtree at a random cut-off point and swaps it with another individual's cut subtree. Hence, two children are generated that possess characteristics of both parents.  
The mutation operator introduces a random change somewhere in a tree, which can be done using the subtree mutation strategy, where a cut-off point is randomly determined (similar to crossover), and the obtained subtree is replaced by a new randomly generated one.  
Crossover and mutation operators are applied according to some probability, where crossover usually has the highest probability of occurrence.
## 3 Proposed Model and Methodology
### Description of the Key Exchange Algorithm and Protocol
#### 3.3.2 Key Exchange Protocol  
The protocol defined in this model ensures a secure communication between IoT devices, integrating the previously described ECC algorithm in a structured process that can be applied in a varied range of scenarios. The protocol steps are as follows:  
1. Session initiation: The initiating device \(D_{i}\) begins the secure connection process, selecting the point \(G\) that it will use in the key calculation.
2. Generation and transmission of public keys: Each device calculates its public key and transmits it to the recipient through a secure communication channel.
3. Calculation of session keys: After receiving the public key, each device uses its private key to calculate the common session key.
4. Authentication confirmation: After establishing the common key, each device sends a confirmation message encrypted with the session key, thus verifying the correctness of the exchange and the authenticity of the other device.  
Starting from a contraction parameter \(a\) and defining two values, \(\alpha\) and \(\beta\), as the contraction limits depending on \(a\), we will have a reconstruction of the cryptographic parameter determination model which are defined over a finite field \(\mathbb{F}_{p}\), where \(p\) is a prime number, and a subspace \(\mathbb{F}_{p}^{a}\) is a contraction of \(\mathbb{F}_{p}\), defined by a parameter \(a\). The elliptic curve equation over \(\mathbb{F}_{p}^{a}\) is  
\[y^{2}\equiv(x^{|\alpha,\beta|})^{3}+ax^{|\alpha,\beta|}+b\pmod{p^{a}},\]  
where the arithmetic is constrained to the subspace \(\mathbb{F}_{p}^{a}\).  
The addition of two points \(P_{|\alpha,\beta|}=(x_{1}^{|\alpha,\beta|},y_{1}^{|\alpha,\beta|})\) and \(Q=(x_{2}^{|\alpha,\beta|},y_{2}^{|\alpha,\beta|})\) on the elliptic curve within the subspace follows similar rules, but all computations are reduced by modulo \(p^{a}\):  
\[x_{3}\equiv\lambda^{2}-x_{1}^{|\alpha,\beta|}-x_{2}^{|\alpha, \beta|} \pmod{p^{a}},\] \[y_{3}\equiv\lambda(x_{1}^{|\alpha,\beta|}-x_{3}^{|\alpha,\beta| })-y_{1}^{|\alpha,\beta|} \pmod{p^{a}},\]  
where \(\lambda\) (the slope of the line) depends on the relationship between the points \(P\) and \(Q\):  
* If \(P\
eq Q\), then \[\lambda\equiv\frac{y_{2}^{|\alpha,\beta|}-y_{1}^{|\alpha,\beta|}}{x_{2}-x_{1} }\pmod{p^{a}}.\]
* If \(P=Q\) (point doubling), then \[\lambda\equiv\frac{3x_{1}^{2}+a}{2y_{1}^{|\alpha,\beta|}}\pmod{p^{a}}.\]  
The contraction parameter \(a\) effectively reduces the dimensionality of the field from \(\mathbb{F}_{p}\) to a smaller subspace \(\mathbb{F}_{p}^{a}\). This contraction modifies the modular arithmetic as all calculations are performed using modulo \(p^{a}\).
## 5 Discussion and Future Research Directions
### Assessing Water Quality and Potability  
The field of water quality assessment and prediction stands to gain substantially from future research, particularly those focusing on leveraging the power of machine learning. As we consider the variety of algorithms and deep learning models, such as deep neural networks, gradient boosting machines, and XGBoost, we are able to significantly enhance our water quality monitoring systems. By integrating sensors, IoT technologies, 5G communication networks, and low-power sensors, we can augment real-time efficiency and accuracy.  
Furthermore, a comprehensive assessment of water quality necessitates consideration of multiple factors, such as urbanization, climate change, and industrial activities. By combining machine learning algorithms and data analysis techniques, we can devise more effective strategies for water conservation and treatment.  
Machine learning plays a crucial role in identifying and classifying various types of pollutants and waste in aquatic environments. This assessment, combined with an understanding of the impact on water quality and aquatic life, is key to preserving our ecosystems. In addition, the intersection of water quality and human health is an area of importance, where machine learning and data analysis can unveil patterns and trends in water-related diseases across different regions and communities.  
Beyond these, the scope for research extends to the utilization of unsupervised machine learning techniques to uncover unknown contaminants or detect new patterns in water quality data. This could potentially expose unforeseen contamination sources or novel correlations between variables.  
There is also scope for employing machine learning to enhance water treatment processes. This could encompass using machine learning to model the efficiency of different treatment methods under various conditions, or optimizing resource allocation in water treatment plants.  
In addition, the exploration of integrating machine learning with cutting-edge technologies, such as nanotechnology and biotechnology, in the realm of water treatment and quality assessment, could be fruitful. These collaborations could facilitate innovative and more efficient methods of water decontamination.  
In conclusion, the development of early warning systems employing machine learning algorithms and real-time sensor data holds promise in predicting and preventing water pollution events. This approach is essential in securing access to safe and sustainable drinking water for all, irrespective of their location in rural or urban areas.
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.2.1 Interference Signal Existence Check  
Interference signal existence check is a typical binary hypothesis test problem, and its purpose is to judge whether the received signal contains interference. The interference detection algorithm mainly constructs a detection statistic with numerical difference between the presence and absence of interference signals, and judges the existence according to the statistic and given decision threshold.  
Some typical detection statistics include carrier to noise ratio (CNR), sum of squares of CNR, average power, average value of each power spectrum, distribution difference of each frequency slice in the spectrum, ratio of maximum and secondary maximum eigenvalues of received signal correlation matrix, peak of cyclic spectrum, signal power ratio before and after interference suppression, peak frequency point power after median filtering, etc. [24; 25].  
The selection of decision threshold is mainly determined by the distribution of detection statistics and the detection performance requirements. However, the existing interference signal existence-checking algorithm can only judge whether there is interference in the received signal, and does not have the ability to estimate the interference position and interference parameters.
# Ion Mobility-Mass Spectrometry Approach for the Comparison of Sheep and Goat Milk Lipidomes
## 2 Materials and Methods
### Data Analysis  
The MassHunter Lipid Annotator (Agilent Technologies, Palo Alto, CA, USA) was used for MS/MS data analysis. The lipid annotator uses a process that employs probability theory and least-squares fit to annotate lipids based on an in silico MS/MS database for Q-TOF fragmentation modeled from lipid standards for every lipid class. Lipid species were annotated from MS/MS fragmentation spectra. Lipid annotation is reported as the sum composition corresponding to the total carbon atom number and double bonds (carbon index and double bond index). Default method parameters were used, with identification based on MS/MS fragmentation data. The criteria were a Q-score > 30, a mass threshold < 10 ppm, a fragment score threshold > 30, and a total score > 60, and a dominant constituent level was reported if the relative abundance differential was >10%.
# Clock Ensemble Algorithm Test in the Establishment of Space-Based Time Reference
## 4 Satellite Clock Bias Prediction  
The timescale is expressed as the bias of the satellite clock phase and the timescale itself. The prediction of the clock bias is often used in real-time scenarios. Thus, we tested the prediction accuracy of the three kinds of timescale generated by the NKT, the RKT, and the TKT algorithms as their evaluation criteria.
# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 1 Introduction  
Smart agriculture is gaining interest of machine learning researchers to solve the real and challenging problem of crop disease detection. Owing to leaf diseases, in the USA, more than USD 6 billion are lost by either crop management or yield reduction [1]. Excessive use of agro-chemicals without precise knowledge of crop disease is increasing production cost along with adversely affecting the environment [2].  
Several diseases may attack on crop leaves simultaneously. For instance, the _Phytophthora infestans_ and _Alternaria solani_ diseases commonly known as 'Late and Early Blight' affecting potato leaves. Late blight is a fungal disease which spreads very quickly in warm and humid weather conditions. Owing to this disease, potato tubers are affected, hence a swift tuber rot [3]. On the other hand, 'Early Blight' is also a fungal disease which damages crop foliar. Major factors of 'Early Blight' include relative humidity, temperature, and wetness duration of leaf [4]. At initial stages, differentiation of the most leaf diseases are difficult for common growers and they applied agro-chemicals excessively. The domain experts can differentiate infected crop leaves even at initial stages. Such expertsespecially in developing countries is not easily available for crop health assessment. The early detection of crop leaf diseases may prevent a huge economic loss. In this scenario, an intelligent and automatic computer-aided diagnostic system is inevitable to identify the leaf diseases precisely.  
In this paper, we present a new technique to classify crop leaf diseases by feature fusion followed by PCA based feature reduction and LDA for classification. Owing to the fusion of deep and handcrafted hybrid features, the proposed approach bypasses the major step of segmentation. Potato crop leaf images are used as a case study for system validation. The proposed approach maybe used as SaaS with an ordinary camera to acquire leaf images for better monitoring of crop health.  
The rest of the paper is organized as follows. In Section 2, the related work is presented. The proposed approach is described in Section 3. Moreover, Section 4 explains the experimental results. The discussion is presented in Section 5. Finally, Section 6 provides conclusion and future recommendation.
## 2 Methodology
### Description of the Algorithms
#### 2.3.2 CI algorithm  
Another globally recognized algorithm explored in this study is the CI algorithm [10]. The CI algorithm employs a three-band model, utilizing reflectance values from the blue, green, and red bands (443 nm, 555 nm, and 670 nm, respectively).  
\[\text{CI}=\text{Rrs}(555)-\left[\text{Rrs}(443)+\frac{555-443}{670-443}(\text{ Rrs}(670)-\text{Rrs}(443))\right]\,, \tag{3}\]  
This index is subsequently related to the mChl-\(a\) concentration.  
\[\log_{10}\left(\text{mChl}-a\right)=-\,0.409+191.6590\text{ CI} \tag{4}\]  
The CI algorithm is employed for retrievals below 0.15 mg m\({}^{-3}\), while the OCx algorithm is used for values exceeding 0.2 mg m\({}^{-3}\). For intermediate values, a weighted combination of both algorithms is utilized [25].