esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
## 5 Discussion and Perspectives--Towards Operational Sinkhole Early Warning
Specifically, the side-looking geometry of SAR sensors mean that it is possible that the visibility of subsidence features would be blocked by structures on the ground in the SAR's LOS [20]. The successful detections of precursory deformation using SAR interferometry approaches highlights the potential for the development of sinkhole early warning systems based on InSAR techniques. Most notably, in several cases, surface deformation features could be observed for days and up to years before the collapse. Furthermore, it was observed that the precursory deformation features were frequently larger than the eventual sinkhole [5; 26]. This is an important consideration since it points to small-scale sinkholes being detectable by precursory basins larger than they are. Th successful investigations further suggest that a particular strength of InSAR measurements include: (1) providing a quantitative assessment of ground movements in sinkhole prone areas, (2) assisting with the identification of stable areas, (3) analysing the impacts of specific events (i.e., water table changes, cumulative rainfall events, leaking water bearing services etc.) on deformation rates, and (4) the detection of precursory deformation for early warning of potential catastrophic events [22].
## 2 Materials and Methods
### Phase-Space Reconstruction  
When the time-series data composed of gait features were obtained, we hoped to further extract the difference of features, so as to make the learning effect of classifier more obvious. For patients with PD with a similar grade, the difference in numerical expression of gait characteristics may not be high, and the sample was small, which was likely to affect the recognition accuracy of the classifier. Human walking can be regarded as a complex nonlinear dynamic system. By reconstructing the time series of gait characteristics into a high-dimensional phase space, more abundant information can be mined to achieve the purpose of improving feature discrimination and classification accuracy. The one-dimensional time series corresponding to each gait feature was reconstructed in a phase space, and the data was mapped into a data point cloud in the abstract topological space by the time-delay-embedding method, which can be thought of as sliding a \"window\" of fixed size over a signal, with each window represented as a point in a (possibly) higher-dimensional space. More formally, given a time series of gait feature \(f\), one can extract a sequence of vectors of the form:  
\[f_{i}=[f(t_{i}),f(t_{i}+\tau),\ldots,f(t_{i}+(d-1)\tau] \tag{4}\]  
\[s=t_{i+1}-t_{i} \tag{5}\]  
where \(d\) is the embedding dimension and \(t\) is the time delay. The quantity \((d-1)t\) is known as the \"window size,\" and \(s\), known as stride, is the difference in \(t_{i+1}\) and \(t_{i}\). Then, \(TD_{d,\tau,s}\) is the cloud of points where \(f\) maps to the phase space with parameters \(d\), \(\tau\), and \(s\):  
\[TD_{d,\tau,s}(f)=[f_{1},f_{2},\ldots,f_{n}] \tag{6}\]  
In this study, \(TD_{d,\tau,s}\) is a numerical time series of multiple gait features. Therefore, too long of a delay will reduce the relevance among elements. Considering the calculation cost and better separability, we chose \(d=3\), \(\tau=1\), and \(s=1\) in this study.
## 3 Multi-Feature Extraction
### Extraction of Image Texture Information Based on Intra-Frame Information
The Gram matrix reflects the pairwise correlations between different feature intensities in the feature map, with diagonal elements representing the response of specific filters and off-diagonal elements indicating the relationships between different filters. With standardized input dimensions of (\(B\), \(C\), \(H\)\(\times\)\(W\)) for Gram matrix computation, the final output tensor dimensions are reformulated as (\(B\), \(C\)\(\times\)\(C\)). This dimensional transformation is systematically achieved via (1) spatial dimensions (\(H\), \(W\)) into \(H\)\(\times\)\(W\), and (2) Gram matrix-based computation of inter-channel covariance features to generate cross-channel correlation matrices. Furthermore, Gram matrices are computed frame-wise to preserve temporal dynamics. Compared to direct feature mapping, the Gram matrix demonstrates heightened sensitivity to subtle spatiotemporal inconsistencies in generative deepfakes by encoding cross-channel texture statistics, thereby providing an interpretable physical basis for detection methodologies. For model construction, a Gram matrix-based image texture enhancement module is introduced to calculate image texture information. This module is designed to aid the neural network in effectively capturing texture features, thereby significantly improving the network's ability to extract these features. The structure of the Gram image texture enhancement module is depicted in Figure 5.  
In the network module illustrated in Figure 5, feature extraction is first conducted by a 3 \(\times\) 3 convolutional layer, followed by a Gram matrix calculation layer to capture global texture features. A channel attention mechanism is then introduced between the subsequent two 3 \(\times\) 3 convolutional layers and the normalization and activation function blocks, enhancing the representation capability of the image and further optimizing texture feature representation. Finally, a global average pooling layer is employed to ensure dimensional consistency with the backbone network. The texture features extracted at different levels of the backbone network are fused together using appropriate feature fusion methods, resulting in comprehensive image texture feature information across various network layers. This approach facilitates a better understanding of the overall structure of textures and the relationships between texture elements.
## 4 Repetition-Based Cooperative Broadcasting Scheme
### Initial Broadcasting
The received power \(p_{R}\) is divided into \(n\) levels, \(\left(p_{R}^{1},p_{R}^{2},\cdots,p_{R}^{n}\right)\). If \(p_{R}\leq p_{R}^{1}\), the channel state during the current slot is set to \(s_{1}\), and the reception result of the packet is _failed_ (i.e., \(b_{11}=0,b_{12}=1\)); if \(P_{R}^{i}\leq p_{R}\leq p_{R}^{i+1}\),\(1\leq i\leq n-1\), the channel state during the current slot is set to \(s_{i}\), and the probability that the packet is successfully received is \(b_{11}\); if \(p_{R}\geq p_{R}^{n}\), the channel state during the current slot is set to \(s_{n}\), and the reception result of the packet is _successful_ (i.e., \(b_{n1}=1\), \(b_{n2}=0\)). For \(z^{\prime}\) any neighbor, say \(x\), node \(z\) can get an observation sequence \(O_{zx}\) during an observation period, i.e., \(T\) frames, since node \(x\) sends a packet during its slot in every frame. Then, node \(z\) uses the Baum-Welch algorithm [32; 33; 34] to construct the corresponding hidden Markov model \(\lambda_{zx}\) based on the observation sequence \(O_{zx}\). Finally, node \(z\) can calculate the probability \(\beta_{T+1}^{zx}\) that the packet from node \(x\) is successfully received during \(x^{\prime}\) slot in the \((T+1)th\) frame. Suppose that the channel state is \(s_{i}\) during the slot in the \(Tth\) frame and changes to \(s_{j}\) during the slot in the \((T+1)\)\(th\) frame. \(\beta_{T+1}^{zx}\) can be calculated as:  
\[\beta_{T+1}^{zx}=\sum_{j=1}^{n}a_{ij}b_{j}(\sigma_{T+1}) \tag{6}\]  
where \(b_{j}(\sigma_{T+1})\) denotes the probability that the observation result is \(\sigma_{T+1}\) under the channel state \(s_{j}\) during the slot in the \((T+1)\)\(th\) frame. In \(x^{\prime}\) slot in the \((T+1)\)\(th\) frame, node \(x\) transmits \(\beta_{T+1}^{zx}\) to node \(z\) along with the payload. For the source node, it knows all the probabilities that the packet will be successfully receivd by its neighbors. These probabilities are calculated by the neighbors and are sent to the source node before broadcasting. If a probability is smaller than a threshold \(p_{th}\), the source node adds the ID of the corresponding node into the set \(\mathcal{D}_{P}\) of the predicted target nodes. When its slot comes, the source node broadcasts the set \(\mathcal{D}_{P}\) along with the safety message.
## 3 Results  
### Calibration  
#### 3.1.1 Leaf Area Index (LAI) Measurement  
The LAI was measured in the study area during rice cultivation in the rainy season of 2013. LAI values from the calibrated plots were measured using the LAI-2000 Plant Canopy Analyzer. (LI-COR Inc., Lincoln, NE, USA). Figure 4 shows a good relationship between LAI and rice age (days) of a primary rice cycle, which fits with the linear function. At the average of LAI value, R\({}^{2}\) is approximately 0.917 for Group A (Machine transplant) and 0.88 for Group B (Manually transplanted rice).  
Figure 4: The relationship between the measured average LAI and rice age for sample LAI mean of rice. Graph (**a**) was presented the relationship of rice in groups A and graph (**b**) for rice group B; error bar represents standard deviation of field measurement.  
In additional, using the One-Sample Kolmogorov-Smirnov test of the distribution of LAI data from field measurement was run. As a result, it presented the normal distribution for LAI data of both rice group A (test statistic = 0.167, N = 17, \(p>\) 0.05) and B (test statistic = 0.189, N = 17, \(p>\) 0.05).  
A Pearson's correlation was run to determine the relation between average LAI values and rice age (growth stages in 17 field-observed dates). There were very strong, positive correlation between LAI and rice age (days) test both rice group A (r = 0.958, N = 17, \(p<\) 0.001) and B (r = 0.941, N = 17, \(p<\) 0.001).
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
The robot ensures precise movement support and resistance, tailoring the exercises to individual needs, while also providing real-time data for therapists to monitor and adjust the rehabilitation process. This personalized, interactive approach boosts patient motivation, ensures safety, and promotes faster, more effective recovery outcomes. The new rehabilitation system integrates the lower limb rehabilitation robot, LegUp [25], with a patient-centered interactive interface that combines AR technology with serious gaming applications, creating a comprehensive and engaging therapy experience. LegUp is a modular parallel robot developed for the rehabilitation of all the main joints of the lower limb. The robot is developed to perform rehabilitation exercises with the patient lying on the bed, which enables the treatment of a wide group of patients, including bedridden ones. The system leverages the robot's precise and repetitive movement capabilities alongside AR-based interactive exercises and game modes aiming to boost patient motivation, adherence, and overall rehabilitation outcomes. This research will assess whether this integrated approach enhances functional rehabilitation, examine its impact on patient engagement, and explores possibilities for therapeutic application in other settings such as homes or clinics. The paper is structured as follows: Section 2 presents the LegUp robot for lower limb rehabilitation, followed by Section 3, which details the proposed system and the game developed for serious gaming in a rehabilitation context. Section 4 outlines the methodology proposed for serious gaming-based treatment, followed by Section 5 which presents the integration of the proposed system in rehabilitation treatment The advantages of this system are discussed in Section 6, and finally, Section 7 concludes the study.
# Breast Cancer Classification Using an Adapted Bump-Hunting Algorithm
## 3 PRIM-Based Framework for Breast Cancer Classification and Explanation
### Presentation of the Framework  
As covered previously in the literature review section, the PRIM is well adapted for medical field prediction by detecting interesting subgroups. Therefore, we introduce a PRIM-based framework to build accurate, interpretable, and explainable classifiers for breast cancer classification. The suggested framework used the PRIM algorithm implemented on random selections of features. This allows the extraction of rules for all the class labels in the breast cancer dataset under consideration, according to predefined support, peeling, and pasting thresholds. In addition, the framework also contains a method to detect regions in the space where conflicting rules may overlap and a method to handle these rule conflicts. We also find in the framework a method to prune irrelevant rules that are usually redundant or contained in one another. Finally, the last step consists of validating the classifier of breast cancer prediction. Figure 3 displays the different steps contained in the proposed framework. Step 1 consists of the data preprocessing and the establishment of the learning objectives. Step 2 defines the rules generated by the PRIM on random features. These two first steps are mandatory. Steps 3 and 4 are optional depending on the dataset and the objectives of the use but we highly recommend them to increase the interpretability and the explainability of the model. Finally, Step 5 is where the classifier, hence the model, is validated. Hereafter, we develop the main steps of the proposed framework.  
Here is a formal statement and explanation of the Random Feature Selection PRIM-based framework:  
Input:  
* Feature space X = {x\({}_{1}\), x\({}_{2}\),..., x\({}_{n}\)} with categorical and numeric features
* Target variable y = {0,1}
* Training data D = {(x\({}_{1}\),y\({}_{1}\)), (x\({}_{2}\),y\({}_{2}\)),..., (x\({}_{n}\),y\({}_{n}\))}
* Minimum support threshold \(\beta\)  
Figure 3: The 5 steps in the R-PRIM framework for breast cancer classification.  
* Peeling parameter \(\alpha\) Procedure:
* Random Feature Selection phase with random number of features for each feature combination
* Box construction phase for each subspace having the peeling criteria \(\alpha\) and the minimum support \(\beta\)
* Metarules applied to all boxes to find associations between boxes and overlapping between boxes
* Assessing the quality of boxes using the density, the coverage, and the support
* Selection of the final boxes for the classifier  
Output:
* Final classifier for prediction
* Metarules for detecting overlapping regions
* Weak boxes for subgroup and knowledge discovery  
The metarules phase is optional in case the experts wish to detect new regions in the input space to discover insights in the data to explain some phenomena.
# Assessing the Homogeneity of Forage Mixtures Using an RGB Camera as Exemplified by Cattle Ratios
## 4 Discussion  
The identified absorption features of the green mass in the TMR will help implement the proposed method of controlling the homogeneity of feed mixes by using fluorescence.  
The shortcomings of the existing methods based on Infrared spectroscopy include the measurement time. To obtain the result, it is necessary to accumulate the spectrum indicators at different frequencies [39].  
The proposed method has a number of advantages:  
* speed of information processing;
* simplicity of constructive implementation;
* the possibility of monitoring the homogeneity of mixtures with similar characteristics in another industry.  
However, the method based on the visible range does not allow to evaluate the nutritional value of feed, which can be attributed to disadvantages.  
Color areas responsible for the feed were highlighted on the images obtained during the tests using the Color Thresholder app. The use of different color spaces showed no significant advantage. The RGB space was used as the most familiar and required no additional analysis. The resulting video was transmitted to the machine operator's smartphone in a real-time mode. In turn, the operator adjusted the operating time of the mixing unit. Such a system could be upgraded to a fully autonomous solution and installedon a robotic feeder or a feed wagon, which would select the optimum operating time of the mixer and ensure energy saving.
## 2 Development of the Profiler
### Sensor Suit  
The Global Ocean Observing System (GOOS) defines essential ocean variables, which are ancillary to ecosystem observations, to resolve the relevant stratifications as follows: temperature, salinity, dissolved oxygen, chlorophyll, backscatter, and pressure or depth of the measurements [16]. This set of parameters defines the backbone of the core biogeochemical and physical measurements. Hence, the Winchi profiler sensor suite featured the following: RBRoncerto C.T.D++ logger by RBR Ltd. (Ottawa, Canada) with a Sea-point Chlorophyll Fluorometer (SCF) and Turbidity Meter and Oxygen Optode 4831F of Aanderaa Data Instruments AS (Bergen, Norway) (Figure 8). The time constants of the temperature and pressure sensors are less than 0.01 and 1 s, respectively. For profiling, fast sensors are used; the dissolved oxygen (O\({}_{2}\)) sensor response time (63%) with fast response foil is less than 8 s. The time constants of fluorometer and turbidimeter are about 0.1 s. The conductivity, temperature, and depth (CTD) data sets are obtained at an interval of 0.5 s. Considering 0.25 m s\({}^{-1}\) for the profiler vertical movement speed, the reliable vertical resolution for the temperature profile is ~0.13 m. Dissolved oxygen, chlorophyll, and turbidity data are obtained at an interval of 1 s.  
Figure 7: Battery and MCU that are mounted on the housing end cap. The MCU is protected by a plastic shield (green colored). The rechargeable battery with a protection circuit board is covered by thermal adhesive tape.  
The control electronics provides four serial ports with independent 12-V power supply lines and four 24-bit ADCs for analog sensors with independent 5-V and 12-V power supply lines. There are also two I2C bus ports with 3.3-V power boosters for digital sensors. The current configuration of the sensors can be expanded by 2 sensors with serial ports, 3 analog sensors, and a reasonably sized chains of digital sensors at I2C buses. The power demand of these extra sensors ultimately depends on their specifications. However, taking into account the power draw of the winch motor in motion, especially in the bottom-bound direction, it would be fair to estimate the decrease of the operation time as 10-15%.  
In the future, O\({}_{2}\) sensors should be complemented with pH and pCO\({}_{2}\) sensors. However, implementation depends on the availability of sensors to operate stably in sea or land waters during long-term missions. As emphasized by Reference [17], although O\({}_{2}\) optodes are widely used in oceanography, pH and CO\({}_{2}\) optodes are still at the nascent level.
## 1 Introduction
An analysis of the flight test confirmed that the navigation solutions were reliable and that there were no issues caused by the false lock, although there were a few outliers in the solution caused by bad dilution of precision (DOP) at a low elevation and by channel power fluctuations.
## 4 Simulation Results
### IMGO for PV Source Penetration and Shunt Capacitors in Distribution Systems
Additionally, IMGO demonstrates improved consistency in its results, reflected by a smaller standard deviation (161.05) compared to that of MGO (254.66). This suggests that IMGO not only minimizes energy losses more effectively but also provides more stable and reliable outcomes than MGO. In order to extend the comparisons with the widely accepted metaheuristic techniques of GA, DE, PSO, and MVO across the different 20 separate runs, Figure 17 displays the box and whisker plot of MGO, IMGO, DE, PSO, GA, and MVO for energy loss minimization. In this figure, the related box of the GA does not appear, as it acquires very high values compared to the others. As shown, a significant reduction is clearly demonstrated by the proposed IMGO, with improvements of 2.09%, 4.18%, 5.96%, 92.61%, and 1.55% compared to MGO, DE, PSO, GA, and MVO, respectively, considering the mean acquired indicator. To assess its performance, comparisons are conducted with the reported algorithms, including the Dragonfly Algorithm (DA), Augmented Subtraction-Based average Algorithm (ASBA), SBA, Osprey Optimization Algorithm (OOA), and Coati Optimization Algorithm (COA) [84]. Table 16 presents a comparative analysis of daily energy losses (in kW/day) across multiple optimization algorithms, including the proposed IMGO, MGO, and several other reported methods (ASBA, DA, NGO, COA, OMA, OOA, and SBA). This table highlights the effectiveness of IMGO in minimizing energy losses, achieving the lowest energy losses per day at 10,495.75 kW/day. Compared to MGO, IMGO shows a 0.82% improvement in reducing energy losses. The improvement percentages indicate that IMGO also outperforms all other listed algorithms, with a 0.55% reduction over ASBA, 1.48% over DA, 1.54% over NGO, 2.05% over COA, 2.81% over OMA, 4.27% over OOA,  
Figure 16: Heatmap of the obtained cost using MGO and IMGO for energy loss/day (kWday) minimization. and a substantial 7.68% reduction over SBA. These results demonstrate IMGO's superiority over other well-established algorithms in minimizing energy losses.