esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Underlying Software Components
#### 3.2.1 Dockers  
Containerization is a technique used to abstract applications from the environments that they execute. Docker containers wrap up the software and its dependencies into a standardized unit for software development that includes everything it needs to run [33]. Containers are a lightweight alternative to virtual machines (VMs) for multi-tenancy within a single host. Here, computing devices were modeled as containers and managed using the Docker automation framework. This was accomplished in two parts: firstly, for resource allocation, and secondly, for software configuration.  
The key advantages of using Docker for distributed applications included lower CPU overhead, version control, portability, and network performance improvement [34]. Docker developed a distributed service platform by considering the fault tolerance of services [35; 36]. Dockers-based gateways and architecture have flexibility with scalability through the microservices paradigm [22; 37].  
This research took advantage of Docker's features. The idea was to combine Docker container services and microservices strategies for modular, scalable edge computing frameworks for IoT smart buildings.
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 3 Classification Method
### Classification Based on Recommendation Techniques
#### 3.1.5 Context-Aware RS  
The concepts of RSs we discussed in this paper focus on suggesting items relevant to the users' needs that are not concerned about any other factor influencing the recommendation process. These types of RSs are traditional or two-dimensional (user, item) RSs, as their rating functions consider only two entities (users and items) to predict ratings. In a context-aware recommendation, other factors known as context are integrated into the rating function as necessary and sufficient conditions for making a meaningful recommendation [23]. The rating function discussed earlier can be modified to \(f:u\times i\times C\to r\), where \(C\) is the domain of the contextual information required for the recommendation. The word context has many meanings depending on the domain of discussion, such as medicine, cognitive science, psychology, computer science, and social science, to mention a few. No specific definition can satisfy the various research disciplines; therefore, the real meaning of context is domain-dependent. In the RS domain, context refers to any factor(s) or additional information that can be used to generate a more intelligent and effective recommendation [24]. [PERSON] et al. in [25] classified the term context used for building context-aware RSs into three different classes:  
* User Context: i.e., the location, companions, social situation, or even the user profile;
* Computing Context: i.e., communication cost and bandwidth, the strength of the connectivity, and available resources, such as a computer, printer, workstation, etc.;
* Physical context: i.e., weather conditions, noise, traffic levels, etc.  
Context-aware RSs are generally called multidimensional RSs [26] because, given \(n\) dimensions of contexts to \(c_{1}\), \(c_{2},\ldots,\ c_{n}\), the rating functions can then be defined as \(f:u\times i\times c_{1}\times c_{2}\times\ldots\times c_{n}\to r\).  
There are three main approaches to modeling context-aware RSs: prefiltering, post-filtering, and contextual modeling paradigms, which differ only in the contexts in which the context is used before making the final recommendation.  
The prefiltering paradigm applies the context to filter out all items that do not satisfy the context to have contextualized data and passes the result to the next level for the usual two-dimensional _user_\(\times\)_item_ recommendation. Conversely, a post-filtering approach performs the traditional two-dimensional _user_\(\times\)_item_ predictions before filtering the items that do not conform to the contextual information. On the other hand, contextual modeling reduces context filtering and two-dimensional _user_\(\times\)_item_ computations to just a brief explanation of the three paradigms of context-aware recommendation techniques. A more detailed description can be found in the work of [PERSON] and [PERSON] in [26].
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 4 Performance Results
### Indoor Experiment
#### 4.2.1 Indoor Passive Detection of a Low-Altitude Signal Source  
The indoor data was obtained in a research laboratory with dimensions 10 m \(\times\) 15 m \(\times\) 5 m using a Phantom 4 Pro UAV. The normalized time-frequency diagrams after filtering are shown in Figure 17 for distances of 5 m and 10 m between the UAV and receiver. For these distances, the actual directions of the arrival and start frequencies were 117.60\({}^{\circ}\), 2.401758 GHz; and 98.67\({}^{\circ}\), 2.401758 GHz.  
Figure 18 presents the false alarm probability and missing alarm probability for different numbers of datasets and indoor distances. For the AM and improved cross-correlation threshold classification method, the false alarm probabilities for 1000 datasets were 0.09 and 0.0621, respectively. With 1000 datasets and a distance of 5 m, the missing alarm probabilities were 0.0542 and 0.0171 for the AM and proposed method, respectively, as shown in Figure 18b. These results indicate that the proposed method provides better performance.  
The results for the improved cross-correlation method at different indoor distances are given in Figure 19. This shows that the cross-correlation values are greater in the target area compared to where there is only clutter and noise. Figures 20 and 21 present the frequency and DOA estimation results using the proposed method, respectively. The frequency estimates are 2.401737 GHz and 2.401741 GHz  
Figure 17: Normalized time–frequency diagrams after filtering at indoor distances of (**a**) 5 m and (**b**) 10 m.  
Figure 18: (**a**) The false alarm probability with different numbers of indoor datasets, and (**b**) the missing alarm probability with different numbers of indoor datasets and two distances.
# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 5 Experimental Results and Discussion
### Results
Similar to the rx2 scene, the object detection algorithm recognized the couch, curtain, and dining table as a bed, respectively, and, thus, incorrectly instantiated three beds. Moreover, the point cloud belonging to the same bed was divided into two by the Euclidean cluster segmentation, so one bed was instantiated incorrectly. The precisions and recalls of the toilet, microwave, and oven were 100%. The precision of the sink was 100%, and the recall was 66.67%. This scene contained 2 twosinks, but in the observation data, one sink was partially occluded by other objects, resulting in a sparse point cloud, and could not be instantiated correctly. As shown in Figure 16d, the free grids near each object contain the semantics of the object, which can facilitate the robot to select the goal point. However, since the goal space of bed6 is partly located in one room and partly located in another room, this will lead to an inaccurate selection of the goal. Specifically, the robot may choose a free grid in the room without the bed as the goal, causing the robot to not complete the navigation task successfully. Figure 16: The results of the pure scene: (**a**) original point cloud, (**b**) point cloud with bounding rectangles, (**c**) the ground truth, and (**d**) the object semantic grid map. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **Bed** & **Couch** & **Sink** & **Toilet** & **Microwave** & **Oven** & **Total** \\ \hline \#ground truth & 3 & 3 & 3 & 2 & 1 & 1 & 13 \\ \#instance & 7 & 2 & 2 & 2 & 1 & 1 & 15 \\ \#correct instance & 3 & 2 & 2 & 2 & 1 & 1 & 11 \\ precision (\%) & 42.86 & 100 & 100 & 100 & 100 & 100 & 73.33 \\ recall (\%) & 100 & 66.67 & 66.67 & 100 & 100 & 100 & 84.62 \\ \hline \hline \end{tabular} \# represents the number of.  
\end{table} 
Table 3: The statistical results of the pure scene. The experimental results of the auto scene are shown in Figure 17, and the statistical results are shown in Table 4. The precision is 58.33%, and the recall is 50%. The system instantiated 12 objects, including four beds, one couch, one toilet, one sink, one microwave, and four ovens. Among them, seven objects were correctly instantiated--namely, three beds, one toilet, one sink, one microwave, and one oven. For each object, although the bed recall was 100%, the object detection algorithm recognized part of the observations that belonged to the couch as a bed, so one bed was incorrectly instantiated, resulting in the precision as 75%. Since no couch was instantiated correctly, the corresponding indexes were 0%. The precision of the sink was 100%, and the recall was 33.33%. The precision of the toilet was 100%, and the recall was 50%. As shown in Figure 17d, each object can generate a goal space. However, due to the inaccurate generation of the bounding box of the bed4, there are too-few free grids in the nonaffordance space, which may cause the robot to not select the goal point socially.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 3: Multi-Scale Input
#### 2.3.1 Local- and City-Scales  
[PERSON] et al. (2016) assessed how urban vegetation configuration (grass, shrub and tree) influences urban temperatures in Perth, Australia. The authors used multi-temporal, very fine spatial (20-cm) and vertical (1-mm) resolution aerial imagery (provided by Urban MonitorTM from the Commonwealth Scientific and Industrial Research Organisation), as well as MODIS land surface temperature data (from the Terra satellite and Aqua satellite) for the 2003 to 2016 time period. In the study, local detailed and multiple time-period inputs were incorporated into urban climate modelling at local- and city-scales.  
The benefit of urban forests in mitigating runoff in highly urbanised central Beijing (the capital city of China) was measured in another study using IKONOS images with four spectral bands captured in July 2012 ([PERSON] et al., 2016). In this study, spatial data pertaining to land cover types (including the tree canopy of an urban forest) were extracted and used as input of a hydrologic model to simulate various runoff scenarios from small sites to the entire study area.  
More generally, a spatially explicit multi-scale indicator was developed to integrate social data (residents' perception of multiple ecosystem services provided by urban forests) with remote sensingdata (from high-resolution satellite images and LiDAR) to understand how the provision of ecosystem services is distributed in a city and predict how people perceive their provision across the entire urban landscape [5]. In particular, this modelling process and the indicator proposed can be easily scaled up to large regional scale and beyond.
# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 2 Background  
FSL can be formally described as follows [45]. Let \(X_{base}=\{x_{i},y_{i}\}\) be a labelled training set where \(x_{i}\) denotes the raw features of sample \(i\); \(y_{i}\) is its corresponding label, \(i=1\ldots N_{base}\); and \(N_{base}\) is the number of classes in this training set. This dataset \(X_{base}\) is usually referred to as the _meta-training_ or _base_ dataset. Let \(Y_{base}\) be the set of classes for \(X_{base}\). In the FSL context, there is a test set \(X_{test}=\{x_{i},y_{i}\}\) where \(i=1\ldots N_{test}\) and \(N_{test}\) is the number of classes in \(Y_{test}\), i.e., the set of classes for \(X_{test}\). Hence, the following situation is valid: \(Y_{base}\cap Y_{test}=\emptyset\). In other words, the classes in the test dataset presented during the inference phase are completely unseen (new) to the model trained on the base dataset. Thus, randomly sampled few-shot tasks are created based on the test set where each \(K\)-way \(N_{S}\)-shot task involves sampling \(N_{S}\)-labelled samples from each of the \(K\) different classes, also chosen at random. Set \(S\) is the support set with these labelled samples with size \(|S|=N_{S}\times K\). Moreover, a few-shot task has also a query set \(Q\) with \(|Q|=N_{Q}\times K\) unlabelled (unseen) examples from each of the \(K\) classes. FSL techniques leverage base set-trained models and labelled support sets to quickly adapt to new tasks. Their performance is assessed on unlabelled query sets.  
Figure 1 shows an example of an arrangement for a 2-way 1-shot few-shot task based on images of the RESISC-45 set [52]. The classes _forest_ and _beach_ are unknown to the model at inference time, meaning the model had no prior knowledge of samples from these classes during the training phase. In this case, there are two unseen classes (2-way) and only one labelled sample per class (1-shot) in the support set. The query set consists of three samples  
randomly selected from each unseen class. The expectation is that an FSL approach, which has not seen any samples from the classes _forest_ and _beach_ during training, can correctly classify them during the inference phase.
## 2 Materials and Methods
### Confusion Matrix Calculation
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Description** & **Values Swept** & **Units** \\ \hline \multirow{2}{*}{_radius_} & Maximum distance to look for earthquakes & 50, 100 & km \\  & around the center of the pixels \((x,y)\). & & \\ \hline \multirow{2}{*}{\(eq\_mag\)} & Magnitude interval to filter earthquakes studied. & \([4,4.9]\), \([5,5.9]\), \([6,\infty]\) & \(\text{m}_{\text{b}}\), \(\text{M}_{\text{W}}\), \(\text{M}_{\text{W}r}\) \\  & & \([6,\infty]\) & \\ \hline \multirow{2}{*}{\(d\_margin\)} & Earthquakes occurring within the period. & \([-6,-3]\), \([-3,0]\), \([0,+3]\), \([+3,+6]\) & day \\  & & \([+3,+6]\) & \\ \hline \multirow{2}{*}{\(s4\_th\)} & Inferior \(\Delta S_{4}\) threshold to consider the pixel as an anomalous change in \(S_{4}\). & 0, 0.003, \(\cdots,0.03\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the parameters swept to generate different confusion matrixes. The _s4_th_ parameter is used as a tuning parameter for the binary classification table. A smaller threshold would make the algorithm detect more \(\Delta S_{4}\) positives, which includes TP (correct predictions), but also FP (false alarms). The study will be used to optimize this threshold to maximize the effectiveness of the algorithm.
## 2 System Model and Problem Formulation  
### System Model  
Figure 1 illustrates two scenarios involving the UAV, the receiver, and jammer. In the first scenario, the receiver is a stationary RSU, while, in the second scenario, the receiver is a moving vehicle. Our paper focuses on the analysis and discussion of these two distinct scenarios. The positions of the receivers and the jammer are measured using Cartesian coordinates, which can be denoted by \(C_{R}^{{}^{\prime}}=[c_{x_{R}}^{{}^{\prime}},c_{y_{R}}^{{}^{\prime}}]\) and \(C_{J}^{{}^{\prime}}=[c_{x_{J}}^{{}^{\prime}},c_{y_{J}}^{{}^{\prime}}]\), respectively. In terms of the moving UAV, let \(C_{U}=[c_{x_{U}},c_{y_{U}},c_{z_{U}}]\) denote its position. While the jammer persistently emits jamming signals towards the UAV, the UAV simultaneously transmits messages to the receiver. At the beginning of each time slot, the UAV selects the communication center frequency \(f_{U}^{c}\), and initiates the transmission of a signal to the receiver. Subsequently, the jammer, upon sensing the UAV's selected frequency \(f_{U}^{c}\), chooses the center frequency \(f_{J}^{c}\) for its jamming signal.  
The transmission power of the UAV, denoted as \(P_{U}\), remains constant throughout the communication process. In contrast, the channel gain between the UAV and the receiver, represented as \(g_{U}\), is a function of various factors, including the distance between the communicating entities and the overall quality of the communication channel.  
To accurately model the jamming interference within the communication environment, let \(J(f)\) serve as the Power Spectrum Density (PSD) function explicitly associated with the jammer's transmitted signals. Likewise, \(g_{I}\) symbolizes the channel gain between the jammer and the UAV, which is an important metric in the overall system performance. Additionally, environmental noise plays a pivotal role and is also incorporated into the system model. This particular form of noise is quantitatively represented by the PSD function \(N(f)\), and its inclusion in the model serves to offer a more comprehensive and realistic understanding of the intricate dynamics of real-world communication scenarios.  
Figure 1: An A2G network in the presence of a jammer.
## 2 Materials and Methods
### Microbiological Analysis  
To avoid contamination, samples were prepared in a horizontal laminar airflow cabinet (Aeolus H, Telstar(r), Terrassa, Cat., Spain). A total of 10 g of each sample were weighed and placed aseptically into a sterile blender bag (BagLight PolySilk, Interscience, Saint-Nom-la-Breeteche, IdF, France) and homogenized with 90 mL of quarter-strength Ringer's solution (Biokar Diagnostics, Allonne, Oise, France) at room temperature. After 90 s in a stomacher apparatus (BagMixer(r) 400 P, Interscience, Saint-Nom-la-Breeteche, IdF, France), appropriate serial dilutions were spread-plated or dispersed by the pour plate method, according to the protocol, in Petri dishes \(\O\)90 mm (Frilabo, Oeiras, PEs, Portugal). Only plates containing at least 10 colony-forming units (CFUs) and up to 300 characteristic and/or non-characteristic CFUs were considered for each dilution. If the values were below or above this range, they were reported to be \(<\)LOQ (limit of quantification) or \(>\)3.0 \(\times\) 10\({}^{5}\) CFU\(\cdot\)g\({}^{-1}\) (5.4771 log CFU\(\cdot\)g\({}^{-1}\)), respectively.  
_Escherichia coli_, coagulase-positive Staphylococcus cocci and _Vibrio_ spp. Were quantified according to the methods described in ISO 16649-2, ISO 6888-2 and ISO 21872, respectively [44; 45; 46]. For the detection of _Salmonella_ spp. And _Listeria monocytogenes_, the methods described in ISO 6579 and ISO 11290-2, respectively, were used [47; 48]. Total coliforms were quantified according to an internal method based on the technical datasheet for the Compass(r) Ec Agar [49], and heterotrophic marine bacteria (HMB) were quantified according to an internal method based on the datasheet instructions for the Condalab Marine Agar [50].  
There is an absence of comprehensive European regulations specifically addressing seaweed. However, France is one of the few countries that has established regulations in this regard, defining a limit of 10\({}^{5}\) CFU\(\cdot\)g\({}^{-1}\) (5 log CFU\(\cdot\)g\({}^{-1}\)) for aerobe mesophiles (30 \({}^{\circ}\)C) in products containing dried seaweed [51]. Furthermore, Portuguese legislation sets the limit for \"minimally processed\" fresh products at 10\({}^{6}\) CFU\(\cdot\)g\({}^{-1}\) (6 log CFU\(\cdot\)g\({}^{-1}\)) [52]. Therefore, the Intermediate value of 5.4771 log CFU\(\cdot\)g\({}^{-1}\) was adopted as the threshold for the safe consumption of the studied seaweeds.
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
###### Abstract  
In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases) is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the antenna calibration and the errors of the angle determination dispositive. Distance bias is calculated from the delay of the signal produced by the refractivity index of the atmosphere, and from clock errors, while the altitude bias is calculated taking into account the atmosphere conditions (pressure and temperature). It will be shown, using simulated and real data, that adapting a classical bias estimation process to use the complete parametrized model results in improved accuracy in the bias estimation.  
Keywords:bias estimation; radar modeling; measurement error; air traffic control; sensor fusion +
Footnote †: journal: Sensors
###### Abstract  
This paper proposes a novel and secure blockchain hyperledger sawtooth-enabled consortium analytical model for smart educational accreditation credential evaluation. Indeed, candidate academic credentials are generated, verified, and validated by the universities and transmitted to the Higher Education Department (HED). The objective is to enable the procedure of credential verification and analyze tamper-proof forged records before validation. For this reason, we designed and created an accreditation analytical model to investigate individual collected credentials from universities and examine candidates' records of credibility using machine learning techniques and maintain all these aspects of analysis and addresses in the distributed storage with a secure hash-encryption (SHA-256) blockchain consortium network, which runs on a peer-to-peer (P2P) structure. In this proposed analytical model, we deployed a blockchain distributed mechanism to investigate the examiner and analyst processes of accreditation credential protection and storage criteria, which are referred to as chaincodes or smart contracts. These chaincodes automate the distributed credential schedule, generation, verification, validation, and monitoring of the overall model nodes' transactions. The chaincodes include candidate registration with the associated university (candidateReg(0)), certificate-related accreditation credentials update (ClssuanceTrans(0)), and every node's transactions preservation in the immutable storage (ULdgerAV(0)) for further investigations. This model simulates the educational benchmark dataset. The result shows the merit of our model. Through extensive simulations, the blockchain-enabled analytical model provides robust performance in terms of credential management and accreditation credibility problems.  
blockchain; hyperledger sawtooth; machine learning; artificial neural network; consortium network; certificate credentials accreditation +
Footnote †: journal: Applied Sciences