esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 4 Experiments  
### Implementation Details  
#### 4.1.1 Datasets  
We validated the effectiveness of our proposed method on two public datasets: COCO [41] and RUOD [42]. The COCO dataset consists of approximately 200,000 annotated images, with about 118,000 images in the training set, 5000 in the validation set, and 50,000 in the test set. Since its release, the COCO dataset has become a standard benchmark in the field of computer vision, providing a diverse and challenging evaluation environment. By conducting experiments on this dataset, we could effectively demonstrate the performance improvements of our model in handling various object categories and scenarios. The RUOD dataset is a real-world underwater object detection dataset, covering a wide range of underwater scenarios and challenges. The training set contains around 9800 images, while the test set contains around 4200 images. The RUOD dataset includes underwater detection tasks not present in the COCO dataset, highlighting unique challenges such as light interference, color distortion, and complex object occlusion. By conducting experiments on the RUOD dataset, we can showcase the generalization ability of our model in different environments, demonstrating its robust performance under specific conditions.
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
###### Abstract  
Topospheric delay is one of the major factors affecting the accuracy of electromagnetic distance measurements. To provide wide-area real-time high precision zenith tropospheric delay (ZTD), the temporal and spatial variations of ZTD with altitude were analyzed on the bases of the latest meteorological reanalysis product (ERA-Interim) provided by the European Center for Medium-Range Weather Forecasts (ECMWF). An inverse scale height model at given locations taking latitude, longitude and day of year as inputs was then developed and used to convert real-time ZTD at GPS stations in Crustal Movement Observation Network of China (CMONOC) from station height to mean sea level (MSL). The real-time ZTD grid product (RtZTD) over China was then generated with a time interval of 5 min. Compared with ZTD estimated in post-processing mode, the bias and error RMS of ZTD at test GPS stations derived from RtZTD are 0.39 and 1.56 cm, which is significantly more accurate than commonly used empirical models. In addition, simulated real-time kinematic Precise Point Positioning (PPP) tests show that using RtZTD could accelerate the BDS-PPP convergence time by up to 32% and 65% in the horizontal and vertical components (set coordinate error thresholds to 0.4 m), respectively. For GPS-PPP, the convergence time using RtZTD can be accelerated by up to 29% in the vertical component (0.2 m).  
BDS/GPS; zenith tropospheric delay; real-time precise point positioning; ECMWF; CMONOC
## 2 Materials and Methods
### Proposed Method for Collecting and Processing Information for IRI Calculation
#### 2.3.2 Virtual Modeling  
In order to achieve 3D virtual reconstruction, the collected images must undergo computational processes that use algorithms based on photogrammetric techniques called Structure from Motion (SfM), which manage to represent digital surface models (DSMs). SfM algorithms allow you to merge recorded photos using computer vision techniques, which automatically defines pixels as tie points in photos that recognize common elements [22]. In this way, it generates a 3D representation based on 2D images. SfM-based algorithms adjust all recognized point packages in photos to minimize average quadratic error (RMSE) [38]. This optimization problem is known as Package Adjustment (Bundle Adjustment) [39].  
Usually, GCP is applied to improve the model's accuracy; however, following the findings of [40], it is possible to obtain satisfactory results without GCP for similar applications in road projects. To verify a correct and reliable digital representation of the pavement, it is possible to perform a generated point cloud analysis (PC) using graphs that explain the quality of the reconstruction in terms of resolution and uncertainty of the points' location. DSM models will be useful for surface analysis, in this case, of pavement. Once the model is created, it is possible to extract longitudinal profiles representing the different axes considered in traditional methods when assessing the roughness of the road.
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
## 4 Analysis of the Influence of Backfill Dip Angle on Stress Distribution
Near the height of 20 m, the vertical stress experienced by the filling body due to the arched effect is 0. When the inclination angle of the filling body is 45\({}^{\circ}\), its trend is similar to that of a large inclination angle. Among them, sudden large tensile stress and compressive stress occur at heights of 10 m and 25 m on the image. The main reason is that the stress arch during the stress transfer process between the upper and lower plates affects the stress along the midline of the filling body. As shown in Figure 13b, the data indicate that the horizontal stress changes gradually from the top to the bottom with the height position: the tensile stress gradually decreases. As the depth continuously descends, the tensile stress reduces to 0 at a certain position and transforms into compressive stress, which continuously increases to the peak. Subsequently, the compressive stress shows a trend of first decreasing and then increasing. Figure 12: Simulation profile of inclined backfill. This phenomenon reveals that under the effect of the arching effect, there are areas within the filling body where both the vertical stress and the horizontal stress are zero. It further indicates that the arching effect can effectively transfer and share part of the load and reduce the overall stress level of the filling body. There is a phenomenon of stress concentration in the middle and upper parts of the filling body due to horizontal stress. Therefore, filling materials with better tensile strength should be adopted in the tensile area to prevent tensile failure of the filling body. In actual engineering, stress detection should focus on the bottom of the filling body and the pressure concentration area. The research results can provide a basis for stress monitoring points in actual engineering, offer references for the strength design and optimization of filling bodies, and help reduce the failure risk of mine filling bodies and improve economic benefits.
# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 3 Proposed Method
### EEG Feature Extraction  
Figure 2b depicts the preprocessing stage, which includes feature extraction in the frequency domain. The EEG signal is composed of five sub-bands: delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-14 Hz), beta (14-32 Hz), and gamma (32-60 Hz). The frequency characteristics are extracted for each channel using a dedicated array of resonators. To cover the whole frequency range, each of the five EEG sub-bands is further divided into several specific frequencies. Therefore, for each sub-band, a different number of resonators is assigned as follows: 10, 8, 5, 7, and 7 resonators for delta, theta, alpha, beta, and gamma, respectively. We propose an array of a total of 37 SNN-based resonators for each of the 14 EEG channels to detect all EEG frequency components. The rationale behind selecting different numbers of resonators for each EEG sub-band is as follows. Since each resonator is tuned to detect frequencies in the range of \(f_{0}\pm\Delta f_{0}\), the number of required resonators not only depends on the specific EEG sub-band range but also differs for the low and high frequency ranges, as can be seen in Figure 3.  
The EEG signal undergoes encoding and conversion into spikes using delta-sigma modulation coding and is then fed into the resonator circuit as a binary string in the timeline. To detect a specific EEG frequency within different sub-bands, the neurons' parameters (LP and LF) are determined by Equation (3). The resonator's frequency response to a chirp signal is depicted in Figure 3. The response signal is normalized in terms of amplitude. To capture the energy within a specified frequency band and detect the different frequencies in each EEG sub-band, we propose using an array of SCTN-based resonators.  
Figure 3: SCTN-based resonator frequency response to a chirp signal in the EEG sub-band ranges.  
Figure 2: Overall SNN-based architecture. (**a**) EEG electrodes positioned according to 10–20 standard. (**b**) SNN-based resonators used for feature extraction through supervised STDP learning. (**c**) Feature mapping, with EEG topologic map consisting of \(11\times 11\) SCTNs. (**d**) SCTN-based classification network trained with unsupervised STDP.  
Figure 4a,c depicts the frequency outputs of the resonators using a spikegram representation method, which demonstrates the occurrence of spikes over time for each EEG sub-band. Each time bin represents the aggregated spikes from each group of resonators, reflecting the power within that bin. The strength of the signal is shown in color, where red represents the maximum spike rate. To evaluate the quality of the proposed frequency features, we compared them to the features extracted in the Fourier domain. Figure 4b,d depicts the spectrogram of the FFT transform of the same EEG signal under testing for the five sub-bands. A comparison between the SNN-based spikegram and the FFT spectrogram demonstrates high correlation between the SNN-based extracted features and the FFT features.
# COVID-19 Outbreak Prediction with Machine Learning
## 2 Materials and Methods  
Data were collected from worldometers website [46] for five countries, namely Italy, Germany, Iran, USA, and China, for total cases over 30 days. Figure 2 presents the total case number (cumulative statistic) for the considered countries. Currently, to contain the outbreak, the governments have implemented various measures to reduce transmission by inhibiting people's movements and social activities. Although information on changes in social distancing is essential for advancing the epidemiological models, for modeling with machine learning no assumption is required. As can be seen in Figure 2, the growth rate in China was greater than that for Italy, Iran, Germany, and the USA in the early weeks of the disease [46].  
The next step is to find the best model for the estimation of the time-series data. Logistic (Equation (6)), linear (Equation (7)), logarithmic (Equation (8)), quadratic (Equation (9)), cubic (Equation (10)), compound (Equation (11)), power (Equation (12)), and exponential (Equation (13)) equations were employed to develop the desired model. These models are generally not good fits for outbreak prediction beyond the available data. In this study, through parameter tuning we aim at finding the optimal performance of these models. The model with the best performance is later used for comparative analysis.  
\[\text{R}=\text{A}/(1+\text{exp}(((4^{\text{*}}\mu)^{\text{*}}(\text{L}-\text{ x})/\text{A})+2)) \tag{6}\]  
\[\text{R}=\text{A}\text{x}-\text{B} \tag{7}\]  
\[\text{R}=\text{A}+\text{B}\text{log}(\text{x}) \tag{8}\]  
\[\text{R}=\text{A}+\text{B}\text{x}+\text{C}\text{x}^{2} \tag{9}\]  
\[\text{R}=\text{A}+\text{B}\text{x}+\text{C}\text{x}^{2}+\text{D}\text{x}^{3} \tag{10}\]  
\[\text{R}=\text{A}\text{B}^{\text{x}} \tag{11}\]  
\[\text{R}=\text{A}\text{x}^{\text{B}} \tag{12}\]  
\[\text{R}=\text{A}\text{EXP}(\text{B}\text{x}) \tag{13}\]  
A, B, C, \(\mu\), and L are parameters (constants) that characterize the above-mentioned functions. These constants need to be estimated to develop an accurate estimation model. One of the goals of this study was to model time-series data based on the logistic microbial growth model. For this purpose, the modified equation of logistic regression was used to estimate and predict the prevalence (i.e., _l_/Population at a given time point) of disease as a function of time. Estimation of the parameters was performed using evolutionary algorithms such as the genetic algorithm (GA), particle swarm optimizer, and grey wolf optimizer. These algorithms are discussed in the following.  
Figure 2: Cumulative number of cases for five countries during a thirty-day period.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 4 Measurements and Analysis
### Outdoor Received Power Measurements
#### 4.1.1 Received Power Measurements at RX1  
For a time period of seven days, outdoor measurements were performed according to the measurement setup outlined in Section 3.1. The measurement data recorded by RX1 are presented in the upper half of Figure 10. The data in this part of the graph are filtered by a moving-average window with a size of 30 samples, corresponding to one hour of measurement. The received power fluctuations correspond to standard deviations of 1.29 dB at 434 MHz and 2.09 dB at 868 MHz. Furthermore, according to free space propagation theory, one would expect the average 434 MHz signal to be 6 dB stronger than the average 868 MHz signal. This behavior is not present here, due to the different placement of the antennas (the 434 MHz antenna being placed indoors and the 868 MHz antenna being placed outdoors, as discussed in Section 3.1.2). Moreover, based on the received packet numbers, the packet loss was determined. For this test, this loss adds up to 0.61% for the 434 MHz band and 6.11% for the 868 MHz band. The packet loss in the latter band is higher because of collisions with packets from other transmitters. A more detailed analysis indeed revealed a much higher occupation of the 868 MHz band, when compared to the 434 MHz band.  
To investigate the origin of the sizable drop in the received power of the 868 MHz signal on day 6, temperature and rainfall data was provided by the Armand Pien weather observatory. These data are presented in the lower half of Figure 10. On day 6, a sizable peak in rainfall was observed which coincides directly with the signal level dip. A slightly lower amount of rainfall was observed on the first day of measurement (day 0), resulting in a smaller, but still measurable drop in signal level on that day. These signal dips are however not present in the 434 MHz data, which were measured with an indoor antenna. The origin of this feature is most probably to be found in the antenna placement. With the 868 MHz antenna being placed outdoors, the humidity on this antenna is expected to have changed its  
Figure 10: 7 days of outdoor received power measurements gathered at RX1 compared to temperature (red curve) and rainfall (blue curve) data.  
behavior in such a way that a mismatch occurred, causing the drop in signal. Consequently, for outdoor operation more weatherproof antennas should be selected. The temperature on the other hand did not influence the measured data in any discernible way as temperature fluctuations were too limited to have a significant impact on the receiver noise. Finally, it has to be noted that because of the relatively low received signal values, this receiver's attenuators remained at 0 dB during this experiment.
## 2 Materials and Methods
### Evaluation Metrics  
In addition to ML models, linear regression (LR) models were employed for comparison. A variety of metrics were employed to assess the performance of these models. R\({}^{2}\) (Coefficient of Determination) and RMSE (Root Mean Squared Error) were used for ML models. R2 quantifies the proportion of the variance in the response variable that the independent features can explain. RMSE evaluates the square root of the average of the squared differences between observed and predicted values, reflecting the model's prediction accuracy. R2, RSE (Residual Standard Error), RMSE, and AIC (Akaike Information Criterion) were used for LR models. RSE assesses the standard deviation of the residuals, indicating the typical size of the errors. AIC measures the relative quality of statistical models, balancing model complexity and goodness of fit. Combining these metrics mitigates individual disadvantages and sensitivities, enhancing evaluation efficiency [15]. These metrics are presented in Equations (1)-(4) [55; 56; 57].  
\[\mathrm{R}^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-x_{i}\right)^{2}}{\sum\limits _{i=1}^{n}\left(y_{i}-\overline{y_{i}}\right)^{2}} \tag{1}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(y_{i}-\hat{y_{i}}\right)^{2}}{N}} \tag{2}\]  
\[\mathrm{N}\mathrm{E}=\sqrt{\frac{\sum_{i=1}^{N}\left(y_{i}-\hat{y_{i}}\right)^ {2}}{N-p-1}} \tag{3}\]  
\[AIC=2k-2\ln(\hat{L}) \tag{4}\]  
where  
\(y_{i}\) is the observed value;  
\(\hat{y_{i}}\) is the predicted value;  
N is the number of observations;  
p is the number of predictors (independent variables);  
k is the number of parameters in the model;  
\(\hat{L}\) is the maximum value of the likelihood function for the model.  
For ML models, key hyperparameters were optimized using grid search combined with 10-fold cross-validation. Each algorithm was tuned over a defined parameter space:  
RF: mtry \(\in\) {1, 2, 3}, ntree \(\in\) {100, 500, 1000};  
CB: committees \(\in\) {1, 10, 50, 100}, neighbors \(\in\) {0, 1, 5, 9};  
XGB: nrounds \(\in\) {100, 200}, max_depth \(\in\) {3, 5, 7}, eta \(\in\) {0.01, 0.1, 0.3}; other parameters were fixed: gamma = 0, colsample_bytree = 1, min_child_weight = 1, subsample = 1;  
GB: sigma \(\in\) {0.1, 1, 10} for the radial basis function kernel.  
The optimal parameter set for each model was selected based on the lowest cross-validated RMSE.
## 2 Results and Discussion  
### Physical and Chemical Properties Analysis  
Drip loss revealed large variations during cold storage, and the value of drip loss at 4 \({}^{\circ}\)C was higher than that of at 0 \({}^{\circ}\)C with storage time (Figure 1a). Based on previous findings [16], it was assumed that the drip loss increased due to the myofibrillar fraction denaturation and aggregation.  
Colour significantly affected the appearance, storage and acceptability of seafood. The \(L^{*}\) and \(a^{*}\) values significantly decreased with increased storage time (Figure 1b,c). The result indicated that tuna muscle stored for 6 days exhibited a mixed spectrum of metmyoglobin and oxymyoglobin [17]. The decrease in \(a^{*}\) was most probably caused by the outflow of pigment in the exudates that were produced during storage [18].  
Tuna muscle contains some histidine, which is easy to form histamine. High content of histamine is easy to cause histamine poisoning [19]. Changes in the histamine concentrations of bigeye tuna are shown in Figure 1d. During storage, the histamine concentrations increased. Moreover, histamine concentrations showed more upward trends at 4 \({}^{\circ}\)C than that at 0 \({}^{\circ}\)C, indicating the effects of temperature on histamine formation. The histamine concentrations increased from 1.5 to 4.50 and 4.77 mg/100 g at 0\({}^{\circ}\)C and 4 \({}^{\circ}\)C, respectively, which did not exceed the limit of the 5 mg/100 g (FDA, 1998) standard [20]. The histamine concentration of bigeye tuna increased during cold storage may be caused by the autolysis process catalysed by either histamine oxidase or histamine dehydrogenase [21; 22].
# Calibration of a 3-Component External Balance for MAVs Wind Tunnel Research
## 1 Introduction
The three-component external balance discussed in this paper was specifically designed and built by the Experimental Aerodynamics Area of INTA to measure aerodynamic forces on Micro Aerial Vehicles (MAVs) in a wind tunnel [17; 18]. The load-sensing elements used in this balance are high-precision strain gauge load cells, renowned for their excellent repeatability and linearity. The geometric design of the load-sensing system allows for complete separation and independent measurement of the three key components of aerodynamic loading, namely, lift force, aerodynamic drag force, and pitching moment. The complete balance calibration process is detailed in the next sections, including the mathematical formulation used to obtain the calibration matrix, an analysis of interactions between the force components, and an evaluation of the uncertainties associated with the external balance.
## 5 Implementation
### SH State-Machine, User Interface, and Control Algorithm  
Figure 8a shows the SH interface for a batch order which contains four different pump orders. The different states of the SH can be seen in Figure 8b. The SH goes to the setup-state when it receives a start event from the TH, therefore it starts discovering the available operational resources in the workcell. In the case showed in the figure, two operational resources can be discovered, one of them is Worker_1@HTC_Platform and the other is Robot_1@HTC_Platform. The SH will move to the ready-processing-state if it discovers the existence of at least one worker, at least one IR, and a product order. Otherwise, The SH stays in the idle-state till the three conditions are achieved. In the ready-processing-state, the SH checks the status of all the IRs and the workers and the product orders. The SH will move to ready-assignment-state if three conditions are fulfilled, these conditions are a free available IR, a free available worker, and an available product order. Otherwise, The SH stays in the idle-state till the three condition are achieved.  
Figure 7: **(a)** OH (Operational Holon) User Interface; **(b)** ROH (Robot Operational Holon) User Interface; **(c)** ROH State-Machine; **(d)** WOH (Worker Operational Holon) User Interface; **(e)** WOH State-Machine.  
In the ready-assignment-state, The SH will reserve the first product order in the batch to be executed, and it reserves one available IR and one available worker to assign this reserved product order simultaneously for both of them. During the implementation of this research, we have presumed that the robot takes two minutes to load one product for the worker, therefore in the execution-state, the required time from an IR to execute its task will be two minutes multiplied by the number of products in the assigned product order. Thus, as has been mentioned before, the worker only can start his task after two minutes of the task assignment process, when he has a complete product to assemble. After the worker task done event arrives, the SH will move automatically to the ready-assignment-state to execute the next product order in the batch.  
The SH will always go from any current state to the setup-state when a robot/worker registration/deregistration event or a new batch order event arrives. The SH will always go from any current state to the idle-state in the following cases: no worker or robot is available, no batch order is available, or no worker or robot is free. The SH will move from idle-state to ready-processing-state if one robot/worker status changes from free to busy or vice-versa.