esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals
## 2 Data and Methods
### Noise Assessment Model  
The BDS, GPS, or Galileo single-system pseudorange and phase-nondifference observation equations are:  
\[P^{s}_{r,j}=\rho^{s}_{r}+(dt_{r}-dt^{s})+T^{s}_{r}+\beta_{j}I^{s}_{r,j}+(d_{r, j}+d^{s}_{j,j})+e^{s}_{r,j} \tag{5}\]  
\[\Phi^{s}_{r,j}=\rho^{s}_{r}+(dt_{r}-dt^{s})+T^{s}_{r}-\beta_{j}I^{s}_{r,j}+e^{ s}_{r,j}+\lambda_{j}(N^{s}_{r,j}+b_{r,j}+b^{s}_{j,j}) \tag{6}\]  
\[P^{s}_{r,j}-\Phi^{s}_{r,j}=2\beta_{j}I^{s}_{r}+(d_{r,j}+d^{s}_{j})-\lambda_{j }(N^{s}_{r,j}+b_{r,j}+b^{s}_{j}) \tag{7}\]  
\[\Delta(P^{s}_{r,j}-\Phi^{s}_{r,j})=\Delta e^{s}_{r,j} \tag{8}\]  
where \(P^{s}_{r,j}\) is the pseudorange observation, and \(\Phi^{s}_{r,j}\) is the carrier phase observation. \(\rho^{s}_{r}\) is the geometry term, and \(dt_{r}\) and \(dt^{s}\) are the receiver clock error and satellite clock error. \(T^{s}_{r}\) is the tropospheric delay, and \(I^{s}_{r,j}\) is the ionospheric delay. \(d_{r,j}\) and \(d^{s}_{j,j}\) are the hardware delays of the receiver and satellite in pseudorange observations, respectively. \(b_{r,j}\) and \(b^{s}_{j,j}\) represent the initial phase deviation and hardware delay of the receiver and satellite in the carrier phase observation. \(N^{s}_{r,j}\) is the ambiguity term in the carrier phase observation. \(e^{s}_{r,j}\) and \(e^{s}_{r,j}\) represent other unmodeled errors, such as observation noise and multipath effects in pseudorange and carrier phase observations, respectively.  
Using the pseudorange minus the carrier, the geometric term can be eliminated, and then using the inter-epoch difference, the ambiguity and ionospheric term can be eliminated without circle slips. Apart from this, the phase noise was small and negligible in comparison to the pseudorange noise; thus, the code noise can be evaluated by the above equation.
# Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications
###### Abstract  
One of the strategies to control the spread of infectious diseases is based on the use of specialized applications for smartphones. These apps offer the possibility, once individuals are detected to be infected, to trace their previous contacts in order to test and detect new possibly-infected individuals. This paper evaluates the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts. We study how these applications work in order to model the main aspects that can affect their performance: precision, utilization, tracing speed and implementation model (centralized vs. decentralized). Then, we propose an epidemic model to evaluate their efficiency in terms of controlling future outbreaks and the effort required (e.g., individuals quarantined). Our results show that smartphone contact tracing can only be effective when combined with other mild measures that can slightly reduce the reproductive number \(R_{0}\) (for example, social distancing). Furthermore, we have found that a centralized model is much more effective, requiring an application utilization percentage of about 50% to control an outbreak. On the contrary, a decentralized model would require a higher utilization to be effective.  
digital epidemiology; COVID-19; mobile computing; opportunistic networking; mobile crowdsensing; epidemic modeling +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Analytical Methods
#### 3.1.3. SS Removal  
SS removal effect of HABR during operational period of 56-131 days is shown in Figure 4. The variation range of SS in the influent was wide around 100-700 mg/L; in contrast, that in the effluent was limited to around 50-350 mg/L, which was beneficial for the steady operation of the subsequent system. During the operational period of 55-131 d, the average SS removal efficiency in the HABR system was 39.9%. Although SS reduction is not the main purpose of anaerobic treatment, it can decrease a portion of toxic substances possibly existing in the suspended matter.
## 1 Introduction
The larger the number of hidden layers, the more complex the data that can be modeled. When the number of hidden layers is two or more, it is called a deep neural network [22]. It is necessary to apply an artificial neural network model to recognize the shape of a plant through an image. [PERSON] et al. [23] proposed a method of classifying wheat grains, nodes, and leaves using a CNN (Convolution Neural Network) but analyzed wheat as a learning factor in a controlled indoor environment. To recognize the shape of plants in an environment where actual plants are grown, color-based 3-dimensional fruit recognition [24], fruit recognition using an LED reflected light and color model [25], color information obtained from an RGB-D camera, and recognition of the fruit stalk by classifying the surface normal vector and curvature into Support Vector Machines [26] have been reported. However, the shape of the plant based on color information could not be recognized because nodes and leaves had the same color information. Therefore, the purpose of this study was to develop an image processing algorithm that classifies plant parts, and the target of classification was paprika, which has various colors of fruits such as red, yellow, orange, and green. Sweet pepper is one of the most cultivated greenhouse crops in South Korea [27]. This study aimed to develop image processing algorithms to classify plant parts for automation of sweet pepper farming tasks such as weeding, pruning, and fruit thinning. Another aim of this study was to evaluate performances of these algorithms. Detailed study objectives are as follows:  
1. To recognize the shape of sweet pepper in a farm environment, classify each part, and develop an image processing algorithm to classify sweet pepper parts for Plenty(red sweet pepper), President (yellow sweet pepper), and Derby (orange sweet pepper) varieties. 2. To classify sweet pepper parts with the NDVI (Normalized Difference Vegetation Index) by dividing the targeted area using k-means clustering and morphological skeletonization followed by extraction of local features using SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features). 3. To evaluate performances of developed algorithms such as the BP (Backpropagation) algorithm and the SVM (Support Vector Machine) algorithm for classifying each part (leaf, node, stem, and fruit) compared to a deep neural network algorithm.
## 3 Ubiquitous Tourist System Based on MCDM and AR
### UTS-mAR Framework  
The proposed framework (UTS-mAR) comprises three main components: the context, intelligent model, and service. Figure 1 depicts these components in detail. The context component is defined in two categories that model the user and their environment. These categories contain various types of information. In the hotel recommender part, the user context stores the user position and preferences. The user preferences refer to the user's selection of restaurant, transportation, and attraction types. The environmental context is obtained from the geospatial data stored in the system. Geospatial data include spatial and non-spatial information. Spatial information includes the locations of hotels as well as hotel selection items, such as attractions, restaurants, and transportation. Non-spatial information refers to the cost and the number of hotel stars. In the AR visualization part, the user context includes general information on the user (such as their age), user health status (such as vision problems), and user position. The environmental context also contains information related to the test environment and the visited object. In our test system conditions, the environment was defined by the daytime parameter. In addition, the visited object information includes images, position, and general textual information related to the objects. The system process for preparing context information is explained in Section 4.1.  
The intelligent model is a system-analysis model, and is used to analyze the context for service delivery. In our system, the hotel recommender model is based on SF and MCDM to select the final hotel. The AR visualization model in our system uses context and AR technology to display the properties of information such as LOD and text size. The detail of the models is discussed in Section 3.2.  
The service component delivers the output of the system to the user. Thus, the proposed system provides services in the form of displaying the location of the selected hotel on Google Maps via the hotel recommender model and displaying the outputted textual information via AR using the AR visualization model.
# A Secure Median Implementation for the Federated Secure Computing Architecture
## 1 Introduction
### Related Work  
There are several protocols for computing rank-based statistics, which differ in their proposed architecture (clients, server), privacy guarantees (input privacy, output privacy), and core technologies (Homomorphic Encryption, pure MPC, differential privacy; for an introduction to these fundamental building blocks, see [3]). For example, [PERSON] et al. [7] and [PERSON] et al. [8] describe solutions for computing rank-based statistics in a star network, i.e., a large number of clients communicating with one or only a few central untrusted servers on which the evaluation is carried out while preserving the privacy of each client using garbled circuits or homomorphic encryption. In addition, [PERSON] and [PERSON] [9], proposes a differential privacy approach that involves the use of both garbled circuits and secret-sharing schemes to protect individual privacy, additionally using an exponential mechanism to mask the true output value and thus a security accuracy trade-off. In contrast, [PERSON] et al. [10] aims to determine the exact value of a distributed dataset. For this purpose, the authors designed an efficient algorithm for jointly computing rank-based statistics while keeping individual data inputs private (see Section 2 for details). Although their algorithm is described theoretically, practical implementations and real-world benchmarking are lacking.  
The real-world implementation and benchmarking of MPC have become increasingly prominent due to advancements in efficient solutions, better hardware availability, and software improvements that reduce the computational overhead of this technology [11]. This trend is particularly noticeable in biomedical research, including genome-wide association studies, diagnostics [12; 13], and drug screening [14]. In 2019, [PERSON] et al. [15] conducted the first MPC with real oncology patient data in Germany, using a Kaplan-Meier estimator. However, such implementations typically require extensive cryptographic knowledge, as most software tools and frameworks like MP-SPDZ [16], FRESCO [17], or ABY [18] require complex technical setups and the manual implementation of MPC functions usingspecific cryptographic primitives or protocols (see [11] for a detailed overview). More user-friendly solutions are limited, with notable examples being the industry-level platforms, e.g., Sharemind MPC (Cybernetica AS, Tallinn, Estonia) [19] and Carbyne (Robert Bosch GmbH, Stuttgart, Germany) [20], as well as no-code tools like EasySMPC [21]. Recently, Federated Secure Computing [6] introduced an accessible architecture that shifts the burden of complex cryptographic processes to the server and provides a simple API.
## 2 Materials and Methods
### Time-Series MODIS Data Processing
#### 2.3.3 Extraction of County Level Average EVI2  
County level average time-series EVI2 was extracted using the general cropland mask and crop-specific masks for yield estimation. MODIS data were only extracted from pixels with good quality according to the quality attributes associated with the MODIS product, that is, pixels that were clear or assumed clear of cloud contamination, and were not in cloud shadow.
## 6 Experimental Results and Analysis
### Influence of Tape Arrangement
#### 6.3.3 Thermal Performance Index Results  
The variation in the thermal performance index for the CVRT arrangements with respect to the attack angle is illustrated in Figure 15. The experimental outcomes demonstrate that the _TPI_ for the backward CVRT arrangement is 1.01-1.02, which is lower than that for the forward arrangement. The maximum _TPI_ values for the backward and forward arrangements are 1.13 and 1.11, respectively.  
Figure 12: Effect of V-rib attack angle (\(\beta\)) on the thermal performance index.  
The experimental findings are strongly in agreement with the numerical predictions. Among the configurations studied, the backward-arranged twisted tape with the lowest rib attack angle was observed to generate the most intense longitudinal vortex flows. This configuration exhibited the highest levels of turbulent kinetic energy (TKE) and temperature gradients in both the near-wall and core flow regions. These flow characteristics significantly reduced the separation zone behind the ribs and promoted more stable and effective flow reattachment. As a result, this configuration achieved the highest Nusselt number and friction factor values.  
The enhancement in convective heat transfer, as indicated by the increased Nusselt number, plays a dominant role in determining the thermal performance index (TPI). Although the associated frictional losses are also elevated, the proportional gain in heat transfer more than compensates for this increase. Consequently, the twisted tape with the lowest attack angle delivers the most favorable TPI, reflecting the optimal balance between heat transfer enhancement and pressure loss.  
Figure 13: Effect of tape arrangement on the heat transfer rate.  
Figure 14: Effect of tape arrangement on the friction factor.  
Figure 15: Cont.
###### Abstract  
This paper proposes a novel feed array antenna to achieve a uniform electric field distribution in the near-field region for feeding a large-aperture antenna. The feed antenna has a \(4\times 4\) rectangular array configuration to obtain uniform near-field distribution on a virtual target surface. Each element of the array consists of a Vivaldi radiator and parasitic rings, and these two components have different radiating modes. In particular, the near-field pattern of the parasitic rings can be varied by adjusting their radii. Thus, the required near-field distribution on the virtual target surface can be achieved by optimizing the radii of the parasitic rings. To further enhance the uniformity of the electric field, the input phase of each Vivaldi radiator is adjusted by applying different transmission line lengths to the Vivaldi radiators depending on their positions in the array. To verify the feasibility of the proposed antenna, the electric field distributions are measured in an electromagnetic anechoic chamber. The results demonstrate that the proposed feed array antenna can achieve uniform near-field distribution with an average of 1.7 dB and a deviation of 6.8 dB on the virtual target surface placed within half a wavelength from the antenna aperture.  
feed array antenna; parasitic rings; uniform near-field; Vivaldi radiators +
Footnote †: journal: Applied  
Design of a Feed Array Antenna to Obtain a Uniform Near-Field Distribution on a Virtual Surface Placed within a Specified Wavelength
###### Abstract  
A facile heat-up synthesis route is used to synthesize environmentally friendly Ag\({}_{2}\)S colloidal quantum dots (CQDs) that are applied as light absorbing material in solid state p-i-n junction solar cell devices. The as-synthesized Ag\({}_{2}\)S CQDs have an average size of around 3.5 nm and exhibit broad light absorption covering ultraviolet, visible, and near infrared wavelength regions. The solar cell devices are constructed with a device architecture of FTO/TiO\({}_{2}\)/Ag\({}_{2}\)S CQDs/hole transport material (HTM) / Au using a solution-processed approach. Different HTMs, N2,N2,N2\({}^{\prime}\),N7,N7,N7\({}^{\prime}\)/octakis(4-methoxyphenyl)-9,9\({}^{\prime}\)-spirobi(9H-fluorene)-2,2\({}^{\prime}\),7,7\({}^{\prime}\) tetramine (spiro-OMeTAD), poly(3-hexylthiophene-2,5-diyl) (P3 HT), and poly((2,3-bis(3-octyloxyphenyl)-5,8-quinoxalinediyl)-2,5-thiophenediyl) TQ1 are studied for maximizing the device photovoltaic performance. The solar cell device with P3 HT as a hole transport material gives the highest performance and the solar cell exhibit broad spectral absorption. These results indicate that Ag\({}_{2}\)S CQD have high potential for utilization as environmentally friendly light absorbing materials for solar cell application and that the hole transport material is critical to maximize the solar cell photovoltaic performance.  
quantum dot; Ag\({}_{2}\)S; solar cell; low-toxic; energy conversion +
Footnote †: journal: Applied Physics Letters
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 1 Introduction
* By using the LIME (local interpretable model, agnostic explanation) super-pixel explanation, the root causes of model classification errors were made clearer and a further understanding was obtained. After the image correction preprocessing on the misclassified cases in the WHU-RS19 [18] dataset, this correction procedure was found to improve the overall classification accuracy. We hope that readers can better understand the reasoning mechanism of AI models for remote sensing scene classification. The remainder of this paper is organized as follows: the dataset is introduced in the second section. In the third section, the steps of the developed research method are explained in detail. The data results and results from other studies are discussed in the fourth section, along with an analysis of why improved results were obtained using the proposed method. The fifth section is the conclusion.
# Single-Parameter-Tuned Attitude Control for Quadrotor with Unknown Disturbance
## 4 Training and Testing in the Simulation
### Performance in the Simulation and Details  
#### 4.2.1 Quadrotor Details  
The quadrotor UAV used in this paper is an \"X\" type F550 quadrotor, equipped with TMOTOR 2213 brushless motors, TMOTOR 9545 propellers, SKYWALKER 30A Electronic Speed Controls (ESCs) and an ACE 5300 mAh 30C 3S Li-polymer battery. The adjustable timing of the ESC is set to a medium level to achieve better performance. With these parameters, the CGPs of the three axes can be calculated. Table 1 lists the system parameters.  
We estimated other types of quadrotors such as an F250 traversing machine and a 10 kg grade agricultural plant protection quadrotor. The CGPs of the x and y axes are between 50 and 200, and the CGPs of the z axes are between 3 and 10. The range of CGPs is limited between 1 and 200 in this paper as a result.
## 4 Diversity Performance  
The diversity performance of the proposed antenna shows the applicability for the connective link of wireless network requirements. The envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), channel capacity loss (CCL), and total active reflection coefficient (TARC) are examined in this section for the evaluation of diversity performance. Each parameter is discussed in the subsequent sections.
## 3 Methodology
### PLC Communication with the Node Network  
The programming and configuration of the PLC were carried out using TIA Portal software where the programming of the entire process is carried out for each of the stations. The PLC used is the S7-1500 where the input and output modules used in the process are configured. Through Node-Red, one can interact with the information from the PLC and in this way obtain real-time data from the DT with bidirectional communication for monitoring and configuring the process. For communication between the PLC and Node-Red, the TCP/IP, MQTT, OPC UA, etc., protocols can be used. The proposed architecture is shown in Figure 5 where OPC UA is used and the PLC is configured to act as an OPC UA server, and the nodes and TAGs that will be sent to Node-Red are also defined. In Node-Red, the nodes are installed for communication with the OPC UA protocol. In our case, \"node-opcua\" and \"OPC UA Browser\" were used. Subsequently, the connection is established by configuring the IP, cycle time, specific port, slot, and rack; these parameters are established to have an ordered identification of the variables received in node-red. The PLC TAGs must be translated into the format required by the new instances, and specific URL addresses can be used for each interaction. Subsequently, each OPC UA node belongs to a class called NodeClass where the common attributes of the OPC UA nodes are defined as shown in Figure 6.  
Once one has the information in Node-Red, the data are sent to the Firebase cloud. In the cloud, a project name and location are configured, and the database and security that will also be used are configured. While in Node-Red, the configuration is carried out to send the data obtained from the process to Firebase; first, the cloud libraries used, in this case \"node-red-contrib-firebase\", must be installed and then, the configuration is carried out of the Firebase nodes where data writing and reading operations are established.  
Once the sensor data are in the Firebase cloud, the configuration is processed with Unity to create the DT applications with AR and VR. First, the Firebase SDK is installed in Unity software, which allows for communication between the platforms, and then, communication is carried out using the TAGS used in the programming to link with the virtual environments that were previously designed to make the animations based on of the process and data obtained. Finally, the sending of data from Unity to Firebase is configured to provide feedback and control the process from both physical and virtual environments in real time.
## 5 Results and Discussion
### Second Experiment  
In Figure 6, it is possible to see the F1 score results from each algorithm tested from the result of each target variable to their mean.  
The SVM algorithm presents a potentially good result, with an average of 0.625, but there is something that must be taken into consideration--the good prediction is made for  
Figure 5: Variation of the F1 Score along the different tables related to variable _time_in_company_ modelled and tested.  
Figure 6: Variation of the F1 Score in the different algorithms tested.  
the paid loans, represented by the point _SVM_1_ on the graph with 0.70. When looking at the prediction of unpaid loans (_SVM_0_), the F1 score falls to 0.55, which will behave as a random attribution of unpaid (just as flipping a coin, the loan can be labeled correctly as potentially unpaid the same number of times as wrongly). This will bring uncertainty to the way the officers would be organized, so it was decided not to implement it.  
Furthermore, the rest of the results found are weak, very similar to the first experiment, which is not enough to implement a model--it would end up resulting in a random allocation. So, for what was tested in the first and second experiments, a classification approach for a solution does not have satisfactory results. That is why a different approach was also considered as a third experiment.