esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Students' Performance Prediction  
Numerous researchers have explored student performance prediction, yet no standardized set of features exists for solving this problem. The features used often depend on the specifics of the course, the LMS platform, or additional available student data. [PERSON] et al. [15] addressed both regression and classification problems for predicting student performance using various machine learning methods, such as K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Logistic Regression (LR), and others. They employed different feature combinations, including demographic, performance, and engagement data, using the Open University Learning Analytics Dataset (OULAD) from two courses [23]. The classification problem involved predicting whether a student would pass or fail the course after each of the six mid-term assessments. As the assessments progressed, the F1-score of the models improved, starting at 78% for the first assessment and reaching 94.9% for the sixth. The researchers found that demographic data did not significantly impact prediction accuracy. Similarly, a more recent study [17] also utilizing OULAD and other datasets confirmed that demographic features offer little added value when performance or activity data is available.  
Researchers in early student performance prediction have utilized various methods to establish the Prediction Time Point (PTP), the precise moment a prediction is generated. For instance, [PERSON] et al. [18] defined the PTP at 10%, 25%, 33%, and 50% of the course duration, using the first and last login timestamps on the platform as reference points to calculate these percentages across the student cohort. Similarly, [PERSON] et al. and [PERSON] et al. [13; 16] divided the course into weekly segments, updating features each week. Conversely, [PERSON] et al., [PERSON] et al., and [PERSON] et al. [14; 15; 19] based the PTP on specific assignments, updating features as assignments became available.  
Defining the PTP by specific assignments is impractical for non-linear courses, as all assignments are continuously available and can be completed in any order or repeated. Similarly, using course duration or weekly intervals for the PTP is unsuitable. While the course has set start and end dates, students can progress at their own pace. Some may take the entire course duration to finish, while others may complete it in a few weeks.  
Although [PERSON] et al. [16] claim to perform early predictions in self-paced education by defining the PTP through weekly course divisions, this approach is actually more suitable for linear courses. The OULAD dataset used in their study features course modules released sequentially over time, implying a linear progression [23]. This inherent structure justifies dividing the course into weekly segments for analysis, as student interactions are expected to follow a linear pattern throughout the course duration.
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Agronomic Aims and Data Sources
The two papers apply CNNs, with excellent accuracy performance in spore identification. A step forward is obtained by [310]: after training with microscope images, spore counting is directly obtained from air sampling in the field for _Alternaria_ spp. The application of algorithms to data fusion from different sources is used by [PERSON] et al. in [152], working on huanglongbing on citrus. Working on phytobiomes to predict diseases, several different databases are considered and, thanks to the application of ten algorithms, a high prediction accuracy is obtained. The use of different data sources in agriculture is relevant because of the efforts needed to deliver sufficient data for algorithm application. An alternative is suggested in [48], which proposes a novel sliding segmentation method to face the issue of insufficient data volume in soybean disease diagnosis. Instead, working on kiwifruit trunk disease, ref. [311] suggests an image generative model able to create credible and varied samples.
## 2 System Model, Problem Formulation and the PS Solution  
### System Model  
In this paper, we investigate the transmit power optimization problem of SWIPT system. Consider a SWIPT system comprising of \(K\) transmitter-receiver (Tx-Rx) pairs with single-antenna on all transmitters and receivers where the \(k\)-th Tx-Rx pair is denoted by \(Tx_{k}-Rx_{k}\left(k\in\mathcal{K}=\{1,\ldots,K\}\right)\), as shown in Figure 1. It is assumed that the transmitter transmits an omnidirectional signal, and a receiver, \(Rx_{k}\), can collect the signal from transmitter \(Tx_{k}\) and all the signals from other transmitters \(Tx_{j}\left(j\in\mathcal{K},j\
eq k\right)\), which interference with the target signal. Because the received signal could not be used for ID and EH at the same time, every receiver is connected to a time switcher when utilizing the TS scheme, or to a power splitter when utilizing the PS scheme. However, in this paper, we are not too focused on the optimization problem, but only on the solutions that can help improve the complexity and computation time in comparison with the conventional optimization algorithm. Therefore, to reduce the system complexity, we just investigate the PS scheme.  
Let \(h_{kk}\) denotes the direct channel of the \(Tx_{k}-Rx_{k}\) pair. At the transmitter \(Tx_{k}\), we assume that the transmitted symbol is a Gaussian random variable with zero mean and variance \(p_{k}\) (which is regarded as the transmit power at \(Tx_{k}\)). Moreover, assume that the transmitted symbols of different transmitters are independent. The transmitted signal is \(x_{k}=\sqrt{p_{k}}s_{k}\) where \(s_{k}\) is the symbol which carries information at the transmitter \(Tx_{k}\) with \(E\left\{\left|s_{k}\right|^{2}\right\}=1\).  
We assume that receivers under the PS scheme can concurrently implement ID and EH from received signals as shown in Figure 0(b). From PS structure, receiver \(Rx_{k}\) can divide the received signal into an ID stream with power ratio \(\theta_{k}\in(0,1)\) and an EH stream with power ratio \((1-\theta_{k})\). The received signal at receiver \(Rx_{k}\) is:  
\[r_{k}=\sum\limits_{j=1}^{K}\sqrt{p_{j}}h_{jk}s_{j}+n_{k},\quad\forall k=1, \ldots,K \tag{1}\]  
where \(n_{k}\sim\mathcal{CN}\left(0,\sigma_{k}^{2}\right)\) is additive white Gaussian noise (AWGN) from the antenna at the receiver \(Rx_{k}\). The signal-to-interference-plus-noise ratio (SINR) at \(Rx_{k}\) is given as:  
\[\text{SINR}_{k}=\frac{\theta_{k}p_{k}|h_{kk}|^{2}}{\theta_{k}\sum\limits_{j \in K\backslash\{k\}}p_{j}\Big{|}h_{jk}\Big{|}^{2}+\theta_{k}\sigma_{k}^{2}+ \delta_{k}^{2}},\quad\forall k \tag{2}\]  
where \(v_{k}\sim\mathcal{CN}\left(0,\delta_{k}^{2}\right)\) is the circuit noise at receiver on ID stream. The harvested energy at receiver is given by:  
\[\text{EH}_{k}=\mu_{k}\left(1-\theta_{k}\right)\left(\sum\limits_{j=1}^{K}p_{j }\Big{|}h_{jk}\Big{|}^{2}+\sigma_{k}^{2}\right),\quad\forall k \tag{3}\]  
where \(\mu_{k}\in(0,1]\) is the energy harvesting efficiency from the EH of \(Rx_{k}\).
## 4 Results and Discussion
### SM-LST Relationship at the Seasonal Scale
Figure 8: Correlation (R) of in situ SM at the SMOS morning passes and \(T_{max}\) (**left**) and their anomalies (**right**) for each season: winter (December–January–February, DJF), spring (March–April–May, MAM), summer (June–July–August, JJA) and autumn (September–October–November, SON). Outliers are depicted with red crosses. Similar behavior is obtained with in situ SM acquired at the SMOS afternoon passes (not shown). Figure 9: Correlation (R) of SMOS SM at the morning passes and MODIS LST Aqua (**left**) and their anomalies (**right**) for each season: winter (December–January–February, DJF), spring (March–April–May, MAM), summer (June–July-August, JJA) and autumn (September–October–November, SON). Similar behavior is obtained at the afternoon passes and/or using Terra day (not shown). Normalized occurrence frequency density diagrams of in situ SM at the SMOS morning passes and \(T_{max}\) (top row), and SMOS SM at the morning passes and MODIS LST Aqua day (bottom row) are displayed in Figure 10 for each season. Using in situ data, SM exhibits a similar dynamic range in winter than in other seasons, but there is a very low dynamic range in \(T_{max}\) since there is a minimum thermal gradient of the soil in this area. This leads to the appearance of a line instead of a triangle in the SM-LST space. By contrast, satellite data have a higher dynamic range of LST Aqua day, showing a small triangle in the SM-LST space. The triangle shape is clearly visible in transition seasons (autumn and spring) both using in situ and satellite data due to the high SM and LST dynamic ranges. In summer, it is better identified using in situ than satellite data. Differences between ground-based and satellite data could be due to the different soil layer captured by in situ LST measurements (0-5 cm) and sensed by MODIS (skin), as well as to the higher thermal inertia of ground-based observations, particularly in wet soils during winter. These results are consistent with those obtained in Figures 8 and 9. Similar behavior is obtained at the afternoon passes and/or using Terra day (not shown).
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 4 Outlook
### Trends Toward Expanding New Features of RO
#### 4.2.2 Multi-Feature GNSS Receiver  
In addition to navigation and RO, another function of the GNSS signal is GNSS reflectometry (GNSS-R), which can be used for observing oceans, ice, soil, and other geophysical parameters. This feature is also important for TC and NWP as it provides a sufficient wind field measurement, particularly over oceans. So far, this spaceborne GNSS-R technique has been implemented in The Cyclone Global Navigation Satellite System (CYGNSS) mission [87; 88], Jason-2/OSTM satellite, OceanSat-2 [89], and Disaster Monitoring Constellation (DMC) [82].  
The GNOS sounder onboard FY-3E integrated the RO function and GNSS-R function in one receiver to save space and power for the satellites. Thus, the sounder measures the ionosphere, neutral atmosphere, sea wave, and wind field [24] simultaneously. In the future, multi-feature GNSS receivers will also be a trend. If more integrated receivers are developed, more RO missions will help increase the use of GNSS-R data. This will provide more atmospheric parameters for TC forecasts and NWP.
## 4 Discussion
### Performance of the Different Vegetation Indices  
Overall, NDVI was the best predictor for the estimation of NEE and GPP during the wet phase of the growing season, reinforcing the conclusion of previous studies that NDVI is a suitable index for depicting photosynthetic C assimilation [9; 64; 65]. In addition, the best results obtained with NDVI instead of EVI or GNDVI suggest that the frequently reported issue of NDVI saturation was not observed in our study. Indeed, NDVI values observed are below the threshold of 0.8 reported for grasslands above which saturation has been observed [66; 67]. On the other hand, the fact that greenness related vegetation indices were less performant when the entire growing season was considered is probably due to the influence of increased VPD on GPP and tissue water content in the karst grassland addressed by our study, during the dry phase. In fact, LSWI and MNDWI proved to be ideal for estimating fluxes during the dry period, confirming the fact that water-related vegetation indices are the best indicators of photosynthetic activity during dry periods, as they are more sensitive to water availability than the other vegetation indices [23]. NDSVI, which is also a water-related vegetation index, did not give as good results for the dry phase as LSWI and MNDWI. Other studies reported the low performance of NDSVI in depicting non-photosynthetic vegetation in a semi-arid grassland and a cropland [68; 69], which is confirmed by the low performance observed in our study.  
The two sources of vegetation indices (Landsat and Proba-V) could not really be compared, given that Landsat images were single date images whereas NDVI\({}_{\text{PV}}\) are 10 days composite data. In addition, the difference in the time step of aggregation of fluxes (10 days for NDVI\({}_{\text{PV}}\) and 5 days for Landsat vegetation indices) makes a comparison inappropriate. However, it should be noted that the highest correlation coefficient obtained with NDVI\({}_{\text{PV}}\) is generally lower than the one obtained with Landsat vegetation indices. This is probably due to the greater variability in fluxes and NDVI\({}_{\text{PV}}\) within the aggregation timeframe of the NDVI\({}_{\text{PV}}\) data (10 days).  
The similarity in the correlation observed for most of the VIs with GPP and NEE is probably the consequence of the good relationship observed between the two variables, which often occurs when the analysis is focused on the growing season [29; 30; 31].  
Despite the fire event that occurred in August 2016, we did not notice any outliers corresponding to that period in our regressions. This might be explained by the occurrence of the fire event towards the end of the dry phase, and the grasses recovered during the second part of the wet phase in September. Indeed, grasslands are known to recover quickly following a fire event [70].
## 6 Discussion and Future Works
In summary, the proposed probabilistic robustness assessment could achieve the following:  
* Reveal the range of the critical mode variations;
* Demonstrate the sensitivity of the critical mode to uncertainty in different system parameters;
* Identify the risk of system instability and the probability of occurrence of a specific condition;
* Identify major parameters contributing to system stability;
* Easily include the stochastic nature of renewables;
* Allow better utilization of existing assets compared to the deterministic approaches based on worst-case scenarios. For future works, investigating the impacts of outer control loops, such as DVC and AVC, on system stability and performance are also recommended. Additional stability and performance indicators, such as frequency of oscillations, phase and gain margins, steady-state error, disturbance rejections capability, singular values, etc., could be defined and developed to clarify system behavior better. Moreover, robustness analysis considering the mission profiles of PV modules or wind turbine systems, resonances in the grid impedance, and unbalanced network conditions would be interesting. Probabilistic control system design, which minimizes the probability of the critical mode in the unsuitable area, would be an exciting topic as well. Finally, considering more complex and larger power systems is highly recommended. When multiple power electronic converters are connected to the point of common coupling, different behaviors might occur and possible interactions between power converters and passive elements of power systems might adversely affect the entire system operation.
## 5 Characteristics of the Applied Communication Technologies for the Underground Mine Stope
### Mesh Routing Protocols  
The Hybrid Wireless Mesh Protocol (HWMP) is the default mesh routing protocol for IEEE 802.11s. HWMP offers a combination of proactive routing (where nodes maintain routing tables with precalculated paths) and reactive routing (where routes are discovered on-demand) [7]. HWMP is an adaptation of the reactive Ad Hoc On-demand Distance Vector (AODV) routing protocol [42] called Radio-Metric AODV (RM-AODV) [43]. AODV operates in the IP layer and uses the hop count as its routing metric, whilst RM-AODV operates in the MAC layer and uses the radio-aware routing metric for its path selection.  
The Better Approach To Mobile Ad Hoc Networking (B.A.T.M.A.N.) [6] protocol is a proactive mesh routing protocol. The main contribution of the B.A.T.M.A.N. mesh protocol was the decentralization of knowledge regarding the complete route of a message chain, from start to finish. A node in the network will not attempt to determine the complete path of the message it is passing, but rather it will only determine its best next hop towards the destination. The version known as B.A.T.M.A.N.-adv was used for this research. This version of B.A.T.M.A.N. operates in the MAC layer.  
An experimental performance comparison of HWMP and B.A.T.M.A.N is necessary to determine the deployment of the two in different stope environments. Unfortunately,the short length of stopes limits the number of hops deployed to a minimum of three, considering the range of the deployed Wi-Fi6 systems.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 3 Bibliometrics Analysis
### The Performance of the Representative Methods on Public Datasets
#### 3.2.1 Public Datasets and Evaluation Indicators  
The most commonly used datasets in the field include Laboratory for Image and Video Engineer (LIVE) [95], Categorical Subjective Image Quality (CSIQ) [96], Tampere Image Database 2008 (TID2008) [97], Tampere Image Database 2013 (TID2013) [98], Blurred Image Dataset (BID) [99], image dataset from the University of Helsinki CID2013 [100], etc. Among them, LIVE, CSIQ, TID2008 and TID2013 datasets are analog distortion datasets; BID and CID2013 datasets are natural distortion datasets. The specific information of these public datasets is shown in Table 4.  
The sharpness performance evaluation indicators are used to determine whether and to what extent the evaluation result of an image sharpness evaluation algorithm is accurate and consistent with the subjective human judgment, which requires certain standards. The most commonly used sharpness performance evaluation indicators are Pearson Linear
## 5 Discussion  
Based on the experimental results in Section 4.2, an interesting phenomenon can be observed in that the effective ranges of the proposed SSSE and traditional RGB-D method are highly complementary. Thus, the fusion application of SSSE and traditional RGB-D method can provide wide range human skeletonization for indoor and outdoor scenarios. In the fusion method, the traditional RGB-D method and SSSE are deployed under different scenarios. For humans inside the effective range of RGB-D cameras, the traditional RGB-D based skeletonization method can provide solid human skeleton extraction method. For humans outside the effective range of RGB-D cameras, SSSE method can redress the unreliable 3D joint positions appears in RGB-D skeletonization result. In order to evaluate the fusion application effectiveness, a comparison between the reliable joint percentage of original skeletons extracted by Kinect and redressed skeletons processed by SSSE is carried out in this section. Reliable joints are defined as joints generated by SSSE not affected by sheltering, and joints generated by Kinect with a confidence index above 0.7. On the contrary, unreliable joints are unavailable joints affected by sheltering in SSSE methods, or joints generated by Kinect with confidence index under 0.7. For better evaluation of the fusion application, Kinect is set up to skeletonize a human subject outside its effective range.  
The unreliable 3D joint positions in Kinect skeletonization result is redressed by SSSE simultaneously. In total, 20 sets of experiments have been launched to evaluate the reliable joint percentage enhancement.
# Advanced Automatic Welding System for Offshore Pipeline System with Seam Tracking Function
## 1 Introduction
If the calculation of the correction value for seam tracking is incorrect, the weld centerline of the weaving motion deviates from the weld centerline. When the welding torch is brought close to the sidewall, an undercut welding defect occurs. On the other sidewall, the welding torch position is far away from the weld groove wall, and incomplete penetration occurs. This study has developed an advanced automatic welding system that increases productivity and saves time using a pneumatic system. The guide track with a pneumatic system is an integral part of the welding carriage as it automatically connects the pipe joints. The automatic welding system communicates with each control module for automatic welding and commands the welding carriage. The arc sensor was developed to achieve high seam tracking accuracy to fully automate the welding system.
## 3 PFR - A Probabilistic Forwarding Protocol
### The Robustness of \(\mathsf{PFR}\)  
To prove the following robustness result, the authors in [9] consider particles \"very near\" to the \(ES\) line. Clearly, such particles have large \(\phi\)-angles (i.e. \(\phi>134^{o}\)). Thus, even in the case that some of these particles are not operating, the probability that none of those operating transmits (during the probabilistic phase 2) is very small. Thus, [9] proves the following.  
**Lemma 5** ([9]): \(\mathsf{PFR}\) manages to propagate the crucial data across lines parallel to \(ES\), and of constant distance, with _fixed_ nonzero probability (not depending on \(n\), \(|ES|\)).
## 2 The IoV Scenario and Use Cases  
The Internet of Vehicles is envisaged as one of the most promising business areas in the near future [13]. Consequently, the automotive industry has been driving an intense research and development effort to identify business cases and technologies capable of supporting the new use cases requirements, which cannot be fully met with existing technologies. IoV is mainly characterized by the massive collection of data mainly provided by in-vehicle and infrastructure sensors, transferred to the network or shared among vehicles, and processed to feed intelligent algorithms to enhance network management and orchestration [14]. For instance, it is estimated that each connected vehicle can generate or consume more than 40 TB of data every eight hours [15], thus stretching the capacity of the networks to the limit and requiring the processing of a vast amount of data, in some cases in real time. Although this verticality has been gaining momentum during the last decade and has attracted the interest of the research community, it still presents technological challenges to be addressed. These challenges can be summarized as: (i) the large scale and extremely dynamic scenarios resulting from a large number of vehicles with high mobility; (ii) unstable connectivity caused by fast varying channel conditions; (iii) limited and distributed computational capacity; (iv) the need to support services with tight latency requirements or high data rate needs. Clearly, the aforementioned technological challenges fall into two research areas. On the one hand, the communication between nodes, which must be able to support high data rates, high reliability, and low latency communications. These communications are usually known as _Vehicle-to-Vehicle_ (V2V), _Vehicle-to-Infrastructure_ (V2I), _Vehicle-to-Network_ (V2N), and _Vehicle-to-Pedestrian_ (V2P) communications or, in general, _Vehicle-to-Everything_ (V2X) communications [16]. On the other hand, the complexity, the scale, and the latency constraints of the problems call for real time optimization solutions which, in turn, must be adapted to the particularities of the network.  
This work is aimed at discussing novel, efficient, and real time optimization approaches: AO and DML, both addressing the requirements of IoV. In order to set up the framework of the optimization problems and solutions, this section briefly overviews the communications technologies and the computational paradigm in IoV, and finally identifies the use cases proposed by standardization organizations in collaboration with the industry.