esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanotechnology, Nanomedicine, and the Kidney
## 4 Nanotechnologies for Renal Imaging
### Optical Imaging  
Optical imaging plays a fundamental role in biological research and clinical studies, offering an easy way to visualize events at a cellular and molecular level. Optical imaging has minimal invasiveness compared to other in vivo imaging technologies while having high sensitivity and spatiotemporal resolution.  
Several NP-based methods have recently emerged to improve optical imaging contrast, including near-infrared fluorescence, chemiluminescence, and photoacoustic.  
Ultrasmall luminescent metal NPs have been developed to enhance image contrast and kidney clearance. Compared to organic fluorescent dyes, these ultrasmall luminescent metal NPs have intrinsic luminescence that can be tuned with surface treatment. For example, AuNPs' emissions can differ with the density and conformation of surface thiol ligands on the metal NPs. [PERSON] et al. (2019) increased the number of zwitterionic glutathione ligands on ultrasmall gold NPs (GS-AuNPs), changing the emission center from a near-infrared (810 nm) to a visible wavelength (600 nm). Compared to using an organic fluorescent probe, GS-AuNPs also showed a 50-times enhanced contrast of kidney-to-background and increased the detection window from 0.6 min to 10 h ([PERSON] et al., 2019), dramatically improving the usage of molecular fluorescence imaging on assessment of the stage of kidney dysfunction. In addition, [PERSON]'s group also demonstrated the possibility of controlling AuNPs' distribution in the body through surface tuning. They used polyethylene glycol thiol ligand (PEG-AuNPs) as the surface modification on NPs, extending 10-fold the blood distribution half-life from 5.4 min to 56.1 min without making significant changes on the final 24 h renal clearance efficiencies. Consequently, PEG-AuNPs were more efficient in targeting tumors than GS-AuNPs ([PERSON] et al., 2019; [PERSON] et al., 2019).  
Photoacoustic (PA) imaging is becoming an essential tool in renal imaging because it possesses the advantages of both optic and ultrasound imaging and can provide a greater temporal and spatial image resolution. An even better resolution has been obtained by combination with nanotechnology. For example, GS-AuNPs (here, Au25(GS)18) were injected and excited by an near-infrared laser wavelength of 800 nm, eliminating the interference from endogenous hemoglobin (Hb) and HbO2 and providing real-time imaging. GS-AuNP PA allows to take a clear image every second and, therefore, to visualize NPs transportation in real-time from the aorta to the renal parenchyma, their renal filtration, and their elimination. The data collected can then be used to quantify the GFR in healthy and unilateral ureteral obstruction (UUO) kidneys, demonstrating the possibility of applying this technology to detect the early stage of kidney injury ([PERSON] et al., 2019).
## 4 Field Data Application  
The proposed method is then applied to field data from the northern Chinese oil field. The Q of the layer between travel times of 3.2 s and 4.0 s (Figure 9) is calculated. The subsurface is grided by 80 \(\times\) 80 and each grid is 80 m.  
The inversion Q results are shown in Figure 10. The Q inversion results by the proposed method (Figure 10b) shows a higher quality compared with that of the conventional method, such as the artifact at depth 4000 m and also the background noise in from depth 7000 m to 8000 m.  
Figure 8: Comparison of average Q with 15% Gaussian random noise calculated using the two methods with true Q values. Standard deviation of Q are also plotted.  
Figure 10: Q profiles calculated by using conventional method (**a**) and the proposed method (**b**).  
Figure 9: Field data of common shot gather.  
Then the data are processed by Q compensation migration with estimated Q as the input. The common reflection point gatherings from the two methods are compared in Figure 11. The results of the proposed method give better recovered amplitudes and corrected distortions of phase. The attenuated reflections below 3.5 s are recovered. Significantly higher horizontal continuity and vertical resolution between 3.44 s and 3.5 s can be found in Figure 11 compared to conventional methods. The resolution of reflections around 3.6 s are higher when compared with that of the conventional method.
## 3 Forecasting Models and Evaluation Metrics
### Forecasting Models
#### 3.1.1 Multistage Forecasting  
In this study, a multistage model is designed, consisting of two stages and constructed from a different combination of forecasting algorithms, to examine the impact this might have on the accuracy of the forecasting. Let us assume that the initial forecast is denoted by \(\hat{y}_{1}(t)\) for time \(te\{0\to TR\}\), where \(TR\) is the last data point of the training set. The residuals, denoted by \(e(t)\), are the differences between the actual values and the initial forecast.  
\[e(t)=y(t)-\hat{y}_{1}(t) \tag{1}\]  
We can then use these residuals to forecast the future residuals, which we will call \(\hat{e}(t)\). The hybrid forecasting model that incorporates these two forecasts can be represented mathematically as:  
\[\hat{y}(\tau)=\hat{y}_{1}(\tau)+\hat{e}(\tau) \tag{2}\]  
where \(\hat{y}(\tau)\) is the final forecast at time \(\tau e\{TR\to TS\}\), \(TS\) refers to the last data point of the test set.  
In other words, this model combines the information from the initial forecast with the information contained in the historical residuals to generate a more accurate final forecast. Figure 4 visualises the multistage modeling process.  
Figure 5 further expands on Figure 4 to describe the multistage forecasting process. In the first stage of the multistage model, the training data acts as input to the \"First Model\". Part of the output from the \"First Model\" is used to compute the \"Residuals Data\", and the other part becomes the \"Initial Forecast\". The \"Residuals Data\" is then passed on to the \"Second Model\" to obtain the \"Residuals Forecast\", which is then arithmetically added to the \"Initial Forecast\" to give the \"Final Forecast\" data.  
For this paper, the multistage model is implemented using two different approaches. The first involves having the same algorithm in both stages of the model; that is, the \"First Model\" and \"Second Model\" are the same (see Figure 4). In the second approach, a hybrid  
Figure 4: Architecture of the multistage model.  
Figure 5: Data flow through the multistage model.  
of models is used; that is, the \"First Model\" and \"Second Model\" are different, such as SVR-LSTM. The following are the multistage models utilised in this study:  
* Multistage models with similar algorithms 1. SVR-SVR 2. FBP-FBP
* Multistage models with hybrid algorithms 1. FBP-SVR 2. FBP-LSTM 3. SVR-LSTM
## 4 Feasibility of PAZ as an Opportunity Illuminator in a Passive Bistatic Radar System
### Knowledge of the OI Position  
The position of the OI is a key element for the definition of the bistatic geometry. In Figure 6, \(R_{T}+R_{R}\) can be estimated from the delay between the transmitted wave and the target echo, \(\tau_{bistatic}\), and the baseline length, \(L\), Equation (8). The instantaneous position of a satellite can be determined using the simplified general perturbation model (SGP4), which estimates the instantaneous two-line element (TLE) parameters of the orbit from an initial state [35]. The standard TLEs are routinely available from CelesTrak [36]. As an example, the TLE parameters of TerraSAR-X on 13 April 2015, obtained from that source, are presented in Figure 17.  
\[R_{T}+R_{R}=c\cdot\tau_{bistatic}+L \tag{8}\]  
Figure 16: AF main cuts: (**a**) zero Doppler; (**b**) zero delay.  
Given the instantaneous position of the OI, the location of the PR receiver and the radiation properties of the OI and the PR surveillance channel, the main geometry parameters (\(R_{T}\), \(R_{R}\) and \(\beta\)) can be estimated for each detected target as functions of time. A case study example is defined and analyzed in Section 5.
## 4 Testing
### Results for the Receiver Operation Mode
#### 4.3.2 C/N\({}_{0}\) Enhancement  
In order to show the performance of C/N\({}_{0}\) enhancement employing the calibrated array, the measured C/N\({}_{0}\) values after beamforming for the array antenna and for the single antenna are compared. The C/N\({}_{0}\) values are calculated as the ratio of the received satellite signal power to the noise density calculated by a noise floor estimator that correlates the received signal with a normalized fictitious PRN [19]. The minimum power distortionless response (MPDR) beamformer is employed to shape the array beam pattern [16]. In doing so for the signal of each satellite, the steering vector is estimated in the receiver operation mode and the corresponding optimal array weight vector **w** is obtained as:  
\[\textbf{w}=\hat{\textbf{R}}^{-1}\textbf{a}_{i}\left(\textbf{a}_{i}^{H}\hat{ \textbf{R}}^{-1}\textbf{a}_{i}\right)^{-1} \tag{21}\]  
where \(\hat{\textbf{R}}\) is the estimated correlation matrix which is defined as:  
\[\hat{\textbf{R}}\triangleq\frac{1}{K}\sum_{k=0}^{K-1}\textbf{y}_{k}\textbf{y} _{k}^{\textrm{H}} \tag{22}\]  
The expression in Equation (22) is the estimation of the covariance matrix using \(K\) successive measurements. The MPDR beamformer maintains the main lobe of the antenna array beam pattern in the direction of the desired signal thereby minimizing the power of noise and interference. Figure 14 shows the normalized polar beam patterns _versus_ azimuth and elevation angles for the satellite signals. It can be observed that for the signal of each PRN, the main lobe is steered toward the direction of that PRN. In order to have an actual sense of the improvement achieved by applying the beamforming process, the measured C/N\({}_{0}\) values for the satellite signals at the reference antenna and the beamformer are illustrated in Figure 15 in which, after 30 s, the receiver switches from the single antenna structure to the multi-antenna structure. Due to the non-uniform antenna gain patterns and different amplifier gains, C/N\({}_{0}\) values increase differently for signals coming from different directions.  
Figure 13: Heading angle determination results for PRN 31 when this PRN is not used in the calibration process.
# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 3 Similarity-Driven Edge Bundling (SDEB)  
Consider a graph \(G=(V,E)\) composed of a finite set of data vertices \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) and a finite set of edges \(E\), with a vertex \(v_{i}\in V\) representing a data object \(d_{i}\in\mathbb{R}^{m}\) (objects described by numerical attributes), and edges \(e_{ij}=\{v_{i},v_{j}\}\in E\) representing relationships between data objects \(d_{i},d_{j}\in D\). Also, suppose that \(\delta(d_{i},d_{j})\) is a function that measures the dissimilarity between pairs of objects/vertices. The goal of the _Similarity-Driven Edge Bundling (SDEB)_ technique is to draw \(G\) bundling its edges to create groups obeying the similarity relationship imposed by \(\delta(d_{i},d_{j})\) so that bundles represent connections among similar groups of objects.  
SDEB accomplishes this through a two-step process. First, a tree-like structure is created defining a _backbone_ to guide the bundling process containing intermediate vertices that connect the data vertices or data objects based on their similarity. In the second step, the _backbone_ vertices (data and intermediate) are mapped to the two-dimensional visual space, and edges are bent following the paths defined by the _backbone_. Figure 1 outlines this process. In the next sections, we detail these two steps and explain our design choices.
## 5 Comparison of Remote Sensing Platforms applications in Northern Peatlands  
It has been perceived as a remarkable remote sensing platform usage shift over the past six years. Table 8 details the proportion of studies and applications of different remote sensing platforms applied in northern peatlands. Satellites, the traditional core of peatland remote sensing, continue to dominate, accounting for 36.5% of studies. This prevalence is attributed to the widespread availability of satellite imagery with high spatial and temporal resolution, enabling efficient mapping and monitoring of extensive peatland areas. Satellites offer cost-effective, large-scale data acquisition, making them ideal for large-scale peatland assessments and monitoring programs. Nevertheless, their dominance has been challenged by the ascendancy of airborne sensors, which provide higher-resolution imagery, accounting for 20.4% of studies. This utilisation stems from their ability to capture finer details of peatland landscapes, enabling more detailed assessments of vegetation characteristics, land cover, and hydrological patterns. However, the higher operational costs of airborne sensors limit their widespread adoption compared to satellites. Speaking of ultra-high-resolution UAVs, despite their relatively recent development, they have experienced a remarkable rise in popularity, their usage increasing since 2017, accounting for 13.9%. In contrast, in situ sensors, while offering the finest resolution data, remain a niche technology employed in just 11% of studies. Mixed platforms offer a more comprehensive understanding of peatland features by fusing data from disparate sensors, e.g., satellite, airborne, UAV, and in situ, representing a promising approach enabling researchers to gain previously inaccessible insights. They account for 18% of studies. In situ and mixed remote sensing platforms have been used less than satellite and airborne platforms. This is primarily due to their higher cost and logistical complexity. In situ sensors require direct contact with the peatland surface, limiting their applicability to large-scale assessments (raw data extracted from the included articles for this section is in Supplementary Tables S2 and S3). Figure 4 provides valuable insights into the evolution of research trends from 2017 to 2022 and can guide future research directions. Showing up the trend analysis of the applications over the years, the most common applications of remote sensing for northern peatlands are mapping and monitoring peatland in satellite, mixed and UAV, where it is increasing since 2017, while in airborne and in situ the increasing application is hydrol
# Metadata Quality for Biobanks
## 6 Metadata Quality Characteristics and Metrics
### Metadata Reliability
#### 6.8.2 Calculating Metadata Reliability Metrics
Then \(MRel_{s_{1}}^{avgM}=(0.7+0.8+0.9)/3=0.8\). Collection-Level Metadata Reliability  
Below we list some of the possible collection-level aggregate reliability metrics. Sample-based average degree of metadata source reliability for a collection is defined as an average of the sample-level metadata reliability values for all samples in a collection:  
\[MRel_{c}^{avgS^{M}}=\frac{\sum\limits_{s\in S_{c}}MRel_{s}^{avgM}}{n(S_{c})}, \tag{23}\]  
where \(S_{c}\) is a set of all samples in \(c\). Suppose there is a collection \(c_{1}\) which contains 100 samples, where 40 samples have sample-level average metadata reliability of 0.55, and 60 samples have sample-level average metadata reliability of 0.85. Then \(MRel_{c_{1}}^{avgS^{M}}=(0.55\cdot 40+0.85\cdot 60)/100=0.73\). Metadata attribute-scoped average degree of metadata source reliability for a collection is calculated as an average of the metadata source reliability values of the metadata sources related to a specific metadata attribute describing values of all data item attributes instantiated by the samples in a collection:  
\[MRel_{a^{M}c_{c}}^{avgS^{M}}=\frac{\sum\limits_{s\in S_{c}}Rel(r_{a^{M}}(s))}{ n(S_{c})},a^{M}\in A_{c}^{M} \tag{24}\]  
Suppose there is a collection \(c_{1}\) which contains 100 samples, where the data item attributes of 15 samples are described by the data item accuracy metadata attribute with the source reliability of 0.35, and the data item attributes of 85 samples are described by the data item accuracy with the source reliability of 0.75. Then \(MRel_{(accuracy)c_{1}}^{avgS^{M}}=(0.35\cdot 15+0.75\cdot 85)/100=0.69\). _The absolute minimum for metadata source reliability for a collection_ defines the value of the narrowest \"bottleneck\" for a metadata source reliability that exists in the collection:  
\[MRel_{c}^{\min SM}=\min_{s\in S_{c}}\min_{a^{M}\in A_{C(s)}^{M}}Rel(r_{a^{M}}(s)) \tag{25}\]  
Suppose there is a collection \(c_{1}\) where the minimal metadata source reliability for one sample \(s_{1}\) is the reliability of the data item accuracy source equal to 0.4, and the minimal metadata source reliability for the rest of the samples is the reliability of the data item completeness source equal to 0.9. Then \(MRel_{c_{1}}^{\min SM}=\min(0.4,0.9)=0.4\). It is also possible to find a metadata attribute with minimal source reliability in the collection by finding \(MRel_{c}^{\min SM}\) and checking which metadata attribute it corresponds to. In the previous example, this metadata attribute is evaluated to data item accuracy.
# Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks
## 3 System Description
\[r=\sum_{j=0}^{T}BLSTM_{2}(H_{1},...,H_{T})_{j}\cdot\hat{\alpha}_{j} \tag{8}\]  
\[\hat{\alpha}_{j}=\frac{e^{tanh(W_{u}BLSTM_{2}(H_{1},...,H_{T})_{j}+b_{\hat{u}} )}}{\sum_{k=0}^{T}tanh(W_{u}BLSTM_{2}(H_{1},...,H_{T})_{k}+b_{\hat{u}})} \tag{9}\]  
\[p=\sum_{j=0}^{Q}BLSTM_{2}(G_{1},...,G_{Q})_{j}\cdot\hat{\beta}_{j} \tag{10}\]  
\[\hat{\beta}_{j}=\frac{e^{tanh(W_{u}BLSTM_{2}(G_{1},...,G_{Q})_{j}+b_{\hat{u}} )}}{\sum_{k=0}^{Q}tanh(W_{\hat{u}}BLSTM_{2}(G_{1},...,G_{Q})_{k}+b_{\hat{u}})} \tag{11}\]  
where \(W_{\hat{u}}\in\mathbb{R}^{d_{s}}\), \(W_{\hat{u}}\in\mathbb{R}^{d_{s}}\), are the weights of the attention mechanism for document and summary at sentence level. These vector representations \(r\) and \(p\), captures bidirectional relationships among the sentence representations, which are obtained from the representations of their words. Then, they can be used to distinguish correct summaries for documents which forces the attention mechanisms to focus on the most similar sentences of both. In order to do this, the vector representations of the document \(r\), the summary \(p\) and the difference between them \(|r-p|\) are concatenated to feed a fully-connected output layer with softmax activation function [23], as defined in Equation (12). Figure 3:0  
In order to train the model, for each document we built positive pairs \((D_{j},S_{j})\), provided by the ES-NEWS corpus and negative pairs \((D_{j},S_{k}):j\
eq k\) where \(S_{k}\) is chosen randomly from the summaries of the remaining documents. For the positive pairs, the ground truth was \(y_{i}=1\) whereas for the negative pairs, the ground truth was \(y_{i}=0\). In this work, we used batches of 64 document-summary pairs (32 positive pairs and 32 negative pairs). The training objective consisted in minimizing the cross-entropy between the prediction \(\hat{y}\) and the class \(y\). The model was trained for 20 epochs of 1500 batches on a GPU Titan X during 3 h.  
To carry out document summarization with SHA-NN, once the network has been trained to distinguish correct summaries for documents, the attention mechanisms at sentence level can be used to rank sentences and then, to select the most relevant of them based on this rank. That is, for the summarization process, given a document \(D\), a forward pass is performed on the left branch of the siamese network (\(HAN_{1}\) in Figure 3) to obtain the attention score \(\hat{\alpha}_{j}\) of each document sentence. From the ranking of the document sentences based on those scores, the \(k=3\) sentences with higher attention score are selected to build the summary.
## 3 Design Requirements  
Our overall objective is to build a _secure_ medical sensor network for health monitoring using _low-power_ wireless motes. To achieve this objective, the system needs to support long-term monitoring while ensuring the privacy and authenticity of medical data. Below we elaborate on these three requirements.  
First, patients with chronic diseases require **long-term monitoring**, which implies the following: (a) the sensor nodes should ideally operate for days without recharging; and (b) during the lifetime of a sensor node, it may be intentionally or accidentally turned off for various reasons, e.g. when it is out of battery power, so the design should not rely on the assumption that the sensor nodes are always on. These considerations have major influences on the design of our security mechanisms.  
Second, since personal health data is extremely sensitive, a high confidence on the system's ability to ensure **data privacy** is essential. From the healthcare providers' point of view, a data breach would have very serious legal and financial consequences. From the users' point of view, they may be hesitant to use the system if they are not convinced that their data will be kept confidential.  
Third, **data authenticity** is very important in our system since healthcare providers rely on the data to diagnose and treat their patients as well as react to emergencies. Patients' lives can be endangered if their data or commands from healthcare providers are intentionally changed or forged. Note that the verification of data authenticity has to be carried out as soon as data is received to ensure timely reaction to emergencies.  
We emphasize that our work focuses on keeping patient data secure _as it is transferred to base stations_. The problem of data security does not stop at that point; there are still issues such as ensuring that only authorized personnel can view the data, and preventing accidental disclosure of the data. These areas are outside the scope of this particular work. We note, however, that there has been work addressing these issues; e.g., several variants of role-based access control (RBAC) schemes tailored for the healthcare industry were surveyed in [21].