esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Technical Features and Socio-Legal Characteristics of Africa's Satellite Constellation
### Reconstruction of African Territories from Space
At the implementation stage of the Court's judgment it was discovered by the parties that there were fundamental discrepancies in the calculations by the Court's cartographers particularly in relation to the maritime sector and that these were apparently part of the Court's evaluation. When Nigeria superimposed the Court's basepoints used in calculating the coastline against the backdrop of actual satellite imagery map, this resulted in those basepoints falling deep into water much against the technicalities of the establishment of national baselines (\"Baseline\" refers to the line from which the seaward limits of a State's territorial sea and certain other maritime zones of jurisdiction are measured.) The Nigerian team during the negotiations concluded that it was essential in accordance with the principles of maritime delimitation that the median line as prescribed by ICJ, must lie on the low-low waterline (on land), For this assertions Nigeria specifically relied on United Nations Division for Ocean Affairs and the Law of the Sea, _UN Handbook on the Delimitation of Maritime Boundaries_[54], Note may be taken that both Article 3 of the territorial Sea Convention and Article 5 of the LOSC provide in identical words that 'the normal baseline for measuring the breadth of the territorial sea is the normal baseline for measuring the breadth of the territorial sea is the low-water line along the coast as marked on large-scale, charts officially recognised by the coastal State as marked on large-scale charts officially recognised by the coastal State' [PERSON] and [PERSON] explain that \"[t]he effect of choosing the low-water line, rather than the high-tide line is to push the outer limit of the territorial sea and other zones farther seawards, particularly on coasts where there is an extensive tidal range\" [55]. Issues were also raised regarding the effect of substantial erosion on the Nigerian coastline and the effects that should have on the demarcation. Only consistent satellite imagery maintained over the decades could resolve the effects of erosion issue but those were obviously not available and could not be acquired in retrospect. Consequently, the delegation of Nigeria proposed that an independent consultant be appointed to repeat the calculations based on the application of the rule of equidistance, in order to determine the accurate coordinates of the basepoints as well as those of point X and to conduct a ground trothing. Ground-truthing is common parlance in cartography with significance in meteorology, analysis of aerial photographs, satellite imagery and a range of other remote sensing techniques in which data gathered at a distance is verified through further processes. For instance, a pixel on a satellite image is compared to what is there in reality (at the present time) in order to verify the contents of the pixel on the image. Ground-truthing is basically a validation process to confirm correspondence between places and their abstractions, whether digitally coded or paper based.
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty
## 3 Overview of Disturbance Compensators  
Here, six different disturbance compensators, including the N-steps delay estimation (NSDE) [6], the one-step delay estimation (OSDE) [7; 8], the two-step delay estimation (TSDE) [9; 10; 11], the SMC disturbance compensator (SDC) [12], the decoupled disturbance compensator (DDC) [13; 14], and the discrete-time disturbance observer (DTDO) [15], are applied to the discrete-time system, Equation (1), with parameter uncertainty and are outlined and compared as follows.
## 4 Discussion
### Clinical Implications  
The study findings hint that the interference of redundant information to clinical practice is a real but under-recognized problem for healthcare professionals. This problem may become even worse in real-world settings where interruptions and multitasking are common [30], leading to medical errors and patient safety issues. The widespread use of EMRs has brought about several advantages, such as flexibility in storage and retrieval of data, easy access across different locations, and simultaneous use by multiple users. On the other hand, the use of EMRs is inevitably associated with some drawbacks. For example, it takes longer to read text on a computer screen than to read printed text [31]. Readers may need extra cognitive processes to gain the corresponding knowledge from a computer screen than from paper [32]. Furthermore, because of the increase in time spent on completing notes [7; 13; 14], routine use of copy-and-paste has become highly prevalent among physicians [10; 11], thus generating a lot of redundant information in EMRs.  
Moreover, the layout and structure of EMRs strongly impact the retrieval of information and sometimes influence clinical decision-making in fundamental ways. Poorly designed interactions with information technology can mislead decision-making and create medical errors, ending in patient harm [33]. An analysis found that 73% of EMR-associated patient safety issues were related to human-computer interaction [34]. Being overwhelmed by less important redundant information and failing to identify relevant new information may further interfere with decision-making [22]. Since it is unrealistic to get rid of all redundant information from EMRs, some measures should be taken to facilitate capturing relevant information more easily. In this regard, highlighting new information can effectively reduce the interference from redundant information and help healthcare professionals grasp key information from EMRs and ameliorate their perceived workload.
# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 3 Literature Studies
### GRU Used Studies  
A group of researchers (2020) developed the session recommendation system using a multi-head attention mechanism and sequential behavior model (SBM) based on the GRU algorithm. The fundamental goal of SR is to forecast a user's next click based on anonymized session data from the past. This model has consisted of three layers: the embedding layer, the sequential layer, and the prediction layer. In the embedding layer, each item's features, and which items are clicked with each session have been embedded in the embedding layer as a high dimension. Then, in SBM based on GRU, there are two gates (the rest gate and update gate). In the prediction layer the output of item prediction that is clicked by the user in the next session is obtained. According to the results from the implementation of this methodology based on the YOOCHOOSE1 and DIGINETICA2 datasets by using the measurements, R@K (which represents the ratio of cases where the desired item is among the first k items in all test cases) and MRR@K (which is the average of  
Figure 6: NLP-based architecture.  
the reciprocal ranks of the desired items) concluded that the proposed model outperformed all baselines [55].  
The majority of current referral systems only utilize one form of user activity data. The Neural Multitasking Recommendation (NMTR) model is proposed to process the existing numerous behavioral recommendation models' flaws (lack of behavior semantics, irrational embedding learning, inability to model complex interactions). This model had a choice of three NCF units as interaction functions, the respective methods were named NMTR-GMF, NMTR-MLP, and NMTR-NeuMF. The architecture of the NMTR model is created from four parts (the embedding layer, hidden layer, encoding hidden layer, and update layer). ID user and ID item have shared features in the embedding layer. In the hidden layer, interactions between latent features for user-item using three neural network units of Neural Collaborative Filtering (NCF) were gathered. Then, the predictions as encoding the different sequential behaviors were cascaded. Finally, in the update layer, the joint prediction for multiple types of behaviors were obtained.
## 4 Methods and Models
### Gwr  
The GWR model takes the influences of the variables in different regions into consideration, which reflects the spatial non-stationarity of the parameters. Moreover, the GWR model allows the relationship between the variables to change with the geographical location and utilizes the spatial visualization technique in the spatial comparative analysis of different geographical locations to provide more realistic results [35]. The definition formula of GWR is shown as (3).  
\[y_{i}=\beta_{i0}(u_{i},v_{i})+\sum_{k=1}^{m}\beta_{ik}(u_{i},v_{i})x_{ik}+ \zeta_{i}\hskip 28.452756 pti\in[1,n] \tag{3}\]  
In the formula, \(y_{i}\) is the value of the \(i\)th sample point of inferiority, \((u_{i},v_{i})\) is the spatial coordinate of the sample point \(i\) of inferiority, \(\beta_{i0}(u_{i},v_{i})\) is the spatial intercept of the sample point \(i\), \(x_{ik}\) is the evaluation variable \(k\) of the sample point \(i\) of inferiority, \(\zeta_{i}\) is the residual of the sample point \(i\) of inferiority, and \(\beta_{ik}(u_{i},v_{i})\) is the regression coefficient of the evaluation variable \(k\) of the sample point \(i\) of inferiority.  
\[\beta(u_{i},v_{i})=\left(X^{T}W(u_{i},v_{i})X\right)^{-1}X^{T}W(u_{i},v_{i})y \tag{4}\]  
where \(X\) represents the independent variable matrix and \(W(u_{i},v_{i})\) is a diagonal matrix with diagonal elements of \(w_{ij}\).  
Notably, the weighting function \(w_{ij}\) was introduced when the GWR was used to estimate the regression coefficient. The weight represents the influence of the observed value at different spatial locations on the coefficient estimate at the location \(i\). In the weight function, the closest observed value at location \(i\) provides the optimum estimate value of the coefficient at location \(i\). The mathematical expression of the weighting function is shown as follows:  
\[w_{ij}=exp[-\frac{1}{2}(\frac{d_{ij}}{b})^{2}] \tag{5}\]  
where \(b\) describes the non-negative decreasing parameter of the relationship between weight \(w_{ij}\) and the distance \(d_{ij}\), which is usually referred to as the bandwidth. The higher \(b\) is, the slower the rate of \(w_{ij}\) is with the decrease in \(d_{ij}\), and vice versa. When the bandwidthis zero, only the weight of the estimate point is one. When the bandwidth is infinity, then the weights of all of the sample points tend toward one as the global regression.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 4 Proposed Method
### Anomaly Detection Metrics  
After Algorithm 1 computes the score vectors for each node using an **adaptive decay factor**, we apply similarity metrics from the work of [PERSON] et al. [20] to detect anomalous behavior at each time step in the graph. The base parameter for the _Adaptive Decay Factor_, \(\delta_{\mathbf{u}}\), has an empirical range of \([0.25,0.55]\). For stability, the tuning occurs dynamically using _Bayesian updating_, where a higher decay factor reduces the influence of past edge interactions. The anomaly detection metric was first introduced and theoretically analyzed in [20] for tracking node score variations over time. Given a score vector, \(\mathbf{v_{u}}\), the metric is defined as  
\[v_{u}^{\prime\prime}=\frac{(v_{u}(t+\Delta t)-v_{u}(t))-(v_{u}(t)-v_{u}(t- \Delta t))}{\Delta t^{2}} \tag{11}\]  
In Equation (11), the metric \(\mathbf{v_{u}^{\prime\prime}}\) represents the _discretization of the second-order derivative_ of the score vector, \(\mathbf{v_{u}}\). The second derivative captures _acceleration or deceleration_ in score changes over time, making it effective for identifying sudden shifts in node behavior that may indicate anomalies. The discretization process approximates a **continuous function** in a discrete form, ensuring computational efficiency and robustness in time-evolving graphs.  
The **theoretical justification** is provided in [20], where the authors established a basis for anomaly detection in dynamic graphs by modeling normal graph behavior using _Lipschitz continuity_, ensuring smoothness in time-series updates. The study demonstrates that normal graph streams exhibit bounded _first_- and _second_-order derivatives: \(\|\mathbf{v}_{u}^{\prime}\|_{1}\leq K_{1},\quad\|\mathbf{v}_{u}^{\prime\prime}\|_{1} \leq K_{2}\), where \(K_{1}\) and \(K_{2}\) are positive real constants. Under this assumption, normal graphs exhibit gradual changes, leading to small values of \(\|\mathbf{v}_{u}^{\prime}\|_{1}\) and \(\|\mathbf{v}_{u}^{\prime\prime}\|_{1}\). However, anomaly types, such as sudden structural changes, could cause these bounds to increase significantly, making them strong indicators of anomalous behavior (_see Section 4.3_ in [20] for more details and proofs ).  
To maintain consistency across time and balance positive and negative fluctuations, we apply **centering and variance scaling**, ensuring that anomaly scores remain comparable across different time steps.
# Influence of Angiopoietin Treatment with Hypoxia and
## 4 Discussion
### Study Weaknesses and Limitations  
One of the main limitations of this study was the relatively small sample size for the NP cells \(\text{Tie2}^{+}\) population. Sample expansion of the NP progenitor cells after a sorting process was a challenge, due to the relatively low percentage who represent this population in the NP tissue, and future studies should recruit IVD material from bigger cohorts and multiple populations and etiologies. Moreover, previous studies demonstrated the difficulties to expand, in vitro, this population [16,43]. Additionally, the \"controls\" used in the study were not truly healthy IVDs, as they were traumatic discs. In this respect, a comparison of fresh cadaveric IVD material of undegenerated nature would be essential for any future investigations. Moreover, the 2D culture was shown to not be the most accurate system for culture and expansion of NP progenitor cells [16]. Previous studies, such as that of [PERSON] et al., dealt with only \(\text{Tie2}^{+}\) cells from degenerated discs; [PERSON] et al. investigated the effect of Ang-2 in degenerated discs. It remains unclear whether the \(\text{Tie2}^{+}\) cells from the degenerated or the trauma discs react in a comparable way upon Ang stimulation. Thus, it remains unclear whether we can compare our results (cells isolated from trauma patients) to the results from publications based on degenerated discs. We suspect that the results from degenerated discs are potentially biased, as discs subjected to degeneration present a higher Ang-2/Ang-1 ratio than healthy discs [23], and the Ang-1 release should be increased as the degenerated discs present blood vessels ingrowth.  
Another strong limitation of the study is that we only used the \(\text{Tie2}^{-}\) population as a control population. Previous studies [1,37] investigated \(\text{Tie2}\) blocking antibodies as a negative control. For our study, a second negative control with a \(\text{Tie2}\) blocking antibody would have made sense, as we observed, especially in the qPCR analysis, the effects in normoxia for Ang-1/2 at 10 ng/mL and Ang-1 at 100 ng/mL. Alternatively, other small inhibitory molecules could have been tested, which are known to interfere and block the Ang-1 and/or Ang-2 receptors. These inhibitors could have been tested by adding these in equimolar concentrations or by titration approaches. These are important negative controls that were not considered here.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 2 Datasets
### Auxiliary ERA5-Land Data  
ERA5-Land data are released by European Centre for Medium range Weather Forecasts (ECMWF) and provide climate reanalysis data from 1950 to the present [39]. They are the latest generation reanalysis data of ECMWF, with their spatial and temporal resolution, model parameterization and data assimilation method being vastly improved relative to other reanalysis products [40], which can be obtained from [[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)), accessed on 30 July 2022. The hourly ERA5-Land data are in the grid of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\), with the spatial and temporal coverage being the same as those of the research area. Meteorological factors that can affect the growing and gathering process of cyanobacteria are selected, including wind speed and direction (WS and WD, calculated from dataset '10m u/v component of wind'), temperature (T, '2m temperature'), total precipitation of one day (ToP, 'total precipitation'), pressure (P, 'Surface pressure') and solar radiation downwards (SRD,'surface solar radiation downwards') during the period of 10 days before the observation date is read and averaged from the ERA5-Land dataset. The averaged ERA5-Land data in this time period will pair up with the selected CYGNSS data points on the observation date. CYGNSS data points and ERA5-Land data rasters are spatially matched based on latitude and longitude coordinates.
# Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System
## 4 System Testing  
The server receives soil temperature and humidity, air temperature and humidity every minute. Soil temperature and humidity, air temperature and humidity sensors transmit data to the network through the RS232 serial port. The interface of remote server receiving data is shown in Figure 7.  
The data collected by citrus soil temperature, humidity and air temperature, humidity sensor will be transmitted to the background. They need to be compared with the standard sample database and modified using data tolerance and data correction model to get the desired data format. The citrus precision fertilization and irrigation support model and citrus soil moisture, temperature and nutrient monitoring model give real-time expert decision-making advice according to the citrus growth knowledge database. The soil nutrients will not change significantly in a certain period of time and there is no suitable wireless sensor. So the system uses artificial data collection. The real time monitoring part mainly monitors the temperature and humidity of soil and air and gives irrigation  
Figure 8: Irrigation decision support interface.  
Figure 7: Remote server monitoring interface.  
decisions in real time based on air temperature and humidity. Figure 8 is the interface of the irrigation decision support system. The corresponding season is spring, when the average soil moisture is more than 20%. The system suggests that the orchard does not need to be irrigated, and the experts reached the same conclusion as the system after their field visit. After a year of use we found that the soluble solid content of fruit was increased by 1%-2%, orchard per mu yield increased 500 kg or more and save water and fertilizer resources 20%. It proves that the system can guide the management of the citrus orchard in the Three Gorges reservoir area scientifically.
# Surface Texture Measurement on Complex Geometry Using Dual-Scan Positioning Strategy
## 1 Introduction  
An important tool for product quality assessment, surface texture measurements are traditionally performed based on laboratory systems, due to the high degree of measurement resolution required [1; 2].  
With the rapid development of advanced manufacturing technologies, such as five-axis machining and additive manufacturing (AM), new surface measurement solutions are needed to address the challenges of complex geometries and irregular surface patterns [3; 4]. Stylus profilometry is widely used for conventionally machined samples [5]. Equipped with a coordinate measuring machine (CMM), the measurement space of stylus profilometry can be enlarged to accommodate samples of large volume and complicated geometry [6]. However, stylus profilometry is not an ideal solution for measuring surfaces with irregular patterns due to its contact measurement mechanism and flanking errors [7; 8]. Another limitation of stylus profilometry is its low efficiency for areal surface texture measurement. Therefore, based on ISO 4287 [9], stylus profilometry is mainly used in line scan mode for conventionally machined surfaces with known directional surface patterns. 3D microscopy [10], such as confocal microscopy [11; 12; 13], coherence scanning interferometry [14; 15] and focus variation microscopy [16; 17], are suitable for area scans. 3D microscopic systems are able to determine surface roughness and surface isotropy based on ISO 25178-2 [18; 19]. Using objectives with high numerical aperture (NA), 3D microscopic systems are able to achieve much higher lateral resolution compared with stylus profilometry. However, the vertical measurement range relies on mechanical scanning, which significantly limits measurement efficiency. In order to avoid mechanical movement, the concept of chromatic confocal microscopy (CCM) [20; 21] has been developed in the past decade. However, for high-accuracy measurement, CCM's sensing range is within a few hundreds of microns, which limits its application to free-form surface measurement. Furthermore, when measuring high-roughness surfaces, long scans are required to differentiate surface roughness from surface waviness and surface form [22]. Therefore, a large vertical measurement range is needed to cover surface fluctuations over a long scan range.  
Based on the above literature review and analysis, the motivation of this presented work is to develop an optical surface texture measurement system able to perform both line and area scans for challenging surfaces with complex geometry and irregular surface patterns. Unlike a laboratory solution, the proposed system has the potential to be utilized for in situ measurement [23; 24].
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 4 A Credit-Based Congestion-Aware Incentive Scheme
### Message Acceptance Selection Mechanism
When \(\mathit{Add}\) is small, the threshold will increase slowly and the node's buffer will be full quickly, so it cannot accept any message that can obtain more credits. On the other hand, when \(\mathit{Add}\) is large, the threshold will increase to a high value quickly, and the node may not accept enough messages due to the high threshold, so a proper increment _Add_ is important for the design of a message acceptance selection mechanism. Same as _Add_, a node should also choose a proper multiplicative factor _Mul_ for the message acceptance selection mechanism. Experimentally, we found that a smaller _Add_ means a low increment; when coupled with a smaller _Mul_, which means a quick decrement, it can result in good performance. So we use _Add_ = 100 s, _Mul_ = 0.3, and initial _Threshold_ = 500 s in this paper.