esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
They are characterized by hydrophilic nano-lamellae with a large surface area and high aspect ratio, which can be easily functionalized and exfoliated in a polymer matrix. The resulting completely exfoliated nanocomposites show enhanced mechanical resistance, reduced fuel crossover, and outstanding ion conductivities above 90-100 \({}^{\circ}\)C [28, 29]. In this study, the sulfonated polyether ether ketone (sPEEK) derivative at a specific degree of sulfonation was chosen as the polymer matrix due to its low cost, high availability, low environmental impact, and outstanding thermo-mechanical strength. SPEEK is a copolymer consisting of non-sulfonated PEEK structural units that are hydrophobic, and sulfonated PEEK units that are hydrophilic. This structure allows for a phase micro-separation similar to that which occurs in Nafion, with hydrophobic domains that give mechanical strength to the membrane, and hydrophilic domains that absorb significant amounts of water and are therefore responsible for proton conduction [30]. The efficient and low-cost nanocomposite electrolyte membranes have been obtained by the dispersion of an organo-silica layered material (SLM) [27]. SLMs are 2D nanostructures of a very simple, inexpensive, one-pot synthesis with a high surface area, which can be easily functionalized with appropriate functional groups. Preliminary studies on SLM decorated with sulfonic groups demonstrated good chemical affinity and facile exfoliation and dispersion in polymer matrices [31, 32], producing nanocomposite electrolyte membranes with an improved thermal stability and proton conductivity. In the present work, SLM was synthesized starting from a mono-functional silica precursor by the condensation mechanism, in order to provide phosphonate functional groups. The final nanomaterial, called PSLM, has a layered structure and the surface contains a high number of \(PO_{3}^{-}\) groups. sPEEK-PSLM nanocomposite membranes with different filler loadings were prepared by a solution intercalation method. All the samples, pristine and nanocomposite membranes, were characterized in terms of proton transport properties, mechanical behavior (by dynamic mechanical analysis (DMA)) and dimensional stability. In particular, NMR spectroscopy was crucial to measure the water self-diffusion coefficients (pulse field gradient NMR method) and the relaxation times (T\({}_{1}\)), while proton conductivities were evaluated by electrochemical impedance spectroscopy (EIS).
## 2 Material and Methods
### Deriving Uncertainty Estimates
#### 2.6.2 Merged Sea-Ice Concentration Uncertainty  
If MODIS data are available, the first case in Equation (4) needs to be considered, and the merged SIC at the pixel with the coordinates (x,y) is given by  
\[SIC_{merged_{xy}}=SIC_{MODIS_{x,y}}+SIC_{AMSR2_{5 km}}-SIC_{MODIS_{5 km}} \tag{9}\]  
\(SIC_{MODIS_{xy}}\) and \(SIC_{MODIS_{5 km}}\) are basically two samples of the same dataset, only that \(SIC_{MODIS_{5 km}}\) is the average MODIS SIC in the 5 km by 5 km around the pixel at the coordinates (x,y). We thus consider Equation (9) to be a linear combination of the MODIS and AMSR2 SIC and calculate the uncertainty as  
\[\sigma_{SIC_{merged}}=\sqrt{Unc_{SIC_{MODIS}}+Unc_{SIC_{AMSR2}}}\times\frac{ 1}{\sqrt{2}}, \tag{10}\]  
where \(\sigma_{SIC_{merged}}\) is the uncertainty of the merged SIC and \(Unc_{SIC_{MODIS}}\) and \(Unc_{SIC_{AMSR2}}\) are the uncertainty contributions of the MODIS SIC and the AMSR2 SIC, respectively. As \(SIC_{MODIS}\) and \(SIC_{AMSR2}\) are retrieved from different wavelength regimes and by different approaches, we consider them to be independent observations of the same quantity and thus scale the uncertainty by \(\sqrt{2}\). \(Unc_{SIC_{MODIS}}\) and \(Unc_{SIC_{AMSR2}}\) are given by  
\[Unc_{SIC_{MODIS}} =\sigma_{SIC_{MODIS}}^{2} \tag{11}\] \[Unc_{SIC_{AMSR2}} =\sigma_{SIC_{AMSR2}}^{2}, \tag{12}\]  
where \(\sigma_{SIC_{MODIS}}\) is the MODIS SIC uncertainty as given by Equation (5) and \(\sigma_{SIC_{AMSR2}}\) is the uncertainty of the AMSR2 SIC. We adopt the approach of the authors of [17], who give \(\sigma_{SIC_{AMSR2}}\) as a function of the AMSR2 SIC as shown in Figure 2.  
If MODIS data are not available for the merging, the merged SIC uncertainty is equal to the AMSR2 SIC uncertainty.
## 11 Conclusions and Future Work  
We proposed several multi-hop node authentication and key establishment protocols for WSNs. Our key establishment solutions can be split into two categories. First, we propose two solutions that use the sink as a trusted third party to establish a new key between two nodes of the network. Then, we proposed four protocols that use other nodes in the network as trusted third parties in order to establish a new key. We proved the security of all of our solutions using the automatic tool Scyther. Our protocols have very few cryptographic operations but are free of flaws as proven by Scyther. Moreover, we implemented and tested all our protocols on TelosB nodes in order to evaluate their execution time. The results show that according to the load of the network and according to the topology, one category might be more efficient than the others.  
Based on our measurements, we were able to allow nodes to detect attack attempts from other nodes in the network by simply measuring the delay of response reception. Each node can expect how much time it should take to receive a response according to the protocol being used and the hops that separate it from the sink. In the case of excessive delay, the node presumes that an attack is taking place and terminates the protocol. This can be considered as a simple intrusion detection system.  
In our future works, we plan on testing our protocols in more realistic platforms such as the IoT-LAB [39] platform. It would then elucidate how our different protocols would perform in a large scale network and how long it would take to establish the required secured links.  
The decision to choose one or the other of the protocols could be dependent on the topology information that each node has. For example, if the sink has constrained resource, it would be preferable for nodes to choose another trusted third party node to establish new keys. The choice would be based on the number of hops that might separate nodes that want to establish a secure link between each other and the trusted third party. This kind of information could be built using a neighbour discovery protocol that allows nodes to share neighbourhood discovery in order to establish \(n\)-hop neighbourhood information (where \(n\) is the number of hops that nodes will be able to discover). A trade-off should be made between the cost of neighbourhood discovery and the gain of balancing key establishment overhead on a bigger number of nodes. Of course, more extensive neighbourhood information enables higher possibility to find trusted third parties. This study will be part of our future works.  
This research was conducted with the support of the \"Digital Trust\" Chair from the University of Auvergne Foundation.  
In this paper, [PERSON], [PERSON] and [PERSON] have worked together on the design and verification of the protocols, while the implementation and evaluation work has been essentially done by [PERSON].  
The authors declare no conflicts of interest.
# PaperNet: A Dataset and Benchmark for Fine-Grained Paper Classification
## 2 Background and Related Works  
### Related Datasets  
Reuters dataset [9] contains lots of short news and corresponding topics. There are 10,789 samples and 90 classes in the dataset. Cifar-10 is an image classification dataset for identifying universal objects. The dataset contains 10 categories of RGB color images: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. The sizes of images are 32 \(\times\) 32, and there are 50,000 training images and 10,000 test images in the dataset. CUB-200 is a multi-modal fine-grained classification dataset and contains 11,788 images of birds, including 200 classes of birds, among which 5994 images are in the training dataset and 5794 images are in the test dataset. Each sample provides information about image tags, bird attributes, and image description.  
In terms of paper classification dataset, AAPD (Arxiv Academic Paper Dataset) [10] is a large dataset in the field of computer science for multi-label text classification. There are 55,840 papers, including abstracts and corresponding topics, with a total of 54 classes. The DocFigure dataset [11] consists of 33K annotated figures of 28 different categories present in scientific articles published in the CVPR, ECCV, ICCV, etc., conferences in the last several years. The details of related datasets including the average word count in each sample are shown in Table 1.  
Due to a large number of categories of scientific papers, researchers more frequently need to locate papers in subdivisions of their research fields. Compared with coarse-grained classification, the sample similarity in the sub-fields is higher, making classification more difficult. Another aspect of scientific papers is that they contain multi-modal information. Figures are usually indispensable besides texts. It is insufficient to classify paper based only on texts. Therefore, multi-modal document classification in the paper field is a potential way to increase accuracy in fine-grained paper classification.  
However, because scientific expertise is usually needed, the cost of sample labeling can be quite high. As a result, fine-grained paper classification datasets are lacking. Here, we introduce PaperNet-Dataset, a multi-modal paper classification dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Modality**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Detail**} & \multirow{2}{*}{**Word**} \\  & & **Class** & & **Sample** \\ \hline \multirow{3}{*}{Text} & Reuters & 90 & 10,789 & 144.3 \\  & AAPD & 54 & 55,840 & 167.3 \\  & IMDB & 10 & 135,669 & 393.8 \\ \hline \multirow{3}{*}{Figure} & Cifar-10 & 10 & 60,000 & - \\  & DocFigure & 28 & 33,000 & - \\  & Deepchart & 5 & 5000 & - \\ \hline \multirow{3}{*}{Multi-modal} & CUB & 200 & 11788 & - \\  & Food-101 & 101 & 90,704 & - \\ \cline{1-1}  & PaperNet v1.0 & 20 & 38,608 & 150.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of related datasets.
## 5 Bit- and Power-Loaded DMT for Highest Bit Rates
### Comparison of Maximizing Capacity  
Second, we compared the maximum capacity achievable with bit- and power-loaded ASE spectrum-sliced, two types of self-seeded FCL RSOAs and externally seeded RSOAs.  
Figure 11a,b show BtB and 25 km DMT transmission results, respectively. For low subcarrier frequencies, the ASE spectrum-sliced source supported up to 3 bit/symbol (8 QAM), the RSOA-FCL up to 5 bit/symbol (32 QAM), and the externally seeded RSOA up to 6 bit/symbol (64 QAM). The power-loading pattern shown in Figure 11a,b led to relatively similar SNRs for subcarriers with the same modulation format. As a result, the BER for different subcarriers was relatively constant over the modulation frequency. The solid lines show the overall BER. For all schemes, the overall BERs were kept below \(7.5\times 10^{-3}\) (10% overhead FEC) [54].  
Table 3 summarizes the achieved maximum line rates for the three transmitter schemes and the corresponding overall BER. In a BtB experiment the ASE spectrum-sliced, the RSOA-FCL and the externally seeded sources were delivering capacities up to 6.25, 20.1 and 30.7 Gbit/s, respectively. For a link with 25 km the sources could still deliver maximum capacities of 4.2, 10.1 and 24.5 Gbit/s.  
Figure 11: Measured SNR, bit- and power-loading pattern and per-subcarrier BER for the three transmitter schemes: RSOA ASE spectrum-sliced (SS) (blue), self-seeded RSOA-FCL (red and brown) and externally seeded (ES) RSOA (green). (**a**) and (**b**) show results for back to back case and 25 km transmission. The ASE spectrum sliced scheme can support modulation formats with up to 3 bits/symbol while RSOA-FCL and externally seeded RSOA can encode up to 6 bits/symbol for low subcarrier frequencies. The solid line in the bottom plot indicates the overall BER that can be achieved with either of the three transmission schemes. All bit-error ratios are within the FEC (forward error correctio) limit of \(7.5\times 10^{-3}\).  
It is interesting to note that the chirp-dispersion induced usable bandwidth reduction results in considerable capacity reduction, especially in the case of the ASE spectrum-sliced and RSOA-FCL transmitters. This can be explained by the relatively wider linewidth of the two sources, thus incurring higher dispersion penalty. However, O-band RSOAs [53], can be used to lower dispersion penalty and avoid the need for NZDSF. O-band has already been used for upstream transmission in legacy PONs to relax the requirement on inexpensive poor sources [1; 55].
## 4 Multi-Antenna Frequency-Selective Channels
### Naive Extension  
We start by briefly presenting the direct extension of the approaches studied in the previous section to any \(S>1\). Unlike the previous section, we adopt the general matrix notation introduced in Section 2. First, with \(S=1\), conventional learning obtains the predictor by solving problem (13), which is generalized to any \(S>1\) as the minimization  
\[V^{*}(\mathcal{Z}_{f}^{\text{tr}}|\bar{V})=\operatorname*{arg\,min}_{V_{f} \in\mathcal{C}^{S\text{N}\times S}}\left\{||X_{f}^{\text{tr}}V_{f}-Y_{f}^{ \text{tr}}||_{F}^{2}+\lambda||V_{f}-\bar{V}||_{F}^{2}\right\} \tag{20}\]  
over the linear prediction matrix \(V_{f}\) in (2). Similarly, transfer learning computes the bias matrix \(\bar{V}^{\text{trans}}\) by solving the following generalization of problem (15),  
\[\bar{V}^{\text{trans}}=\operatorname*{arg\,min}_{V\in\mathcal{C}^{S\text{N} \times S}}\left\{\sum_{f=1}^{F}\left\|X_{f}V-Y_{f}\right\|_{F}^{2}\right\}, \tag{21}\]  
followed by the evaluation of the predictor \(V^{*}(\mathcal{Z}_{f}^{\text{tr}}|\bar{V}^{\text{trans}})\) using (20); whereas meta-learning addresses the following generalization of minimization (17),  
\[\bar{V}^{\text{meta}}=\operatorname*{arg\,min}_{\bar{V}\in\mathcal{C}^{S \text{N}\times S}}\left\{\sum_{f=1}^{F}\sum_{i=1}^{\text{te}}\left|V^{*}( \mathcal{Z}_{f}^{\text{tr}}|\bar{V})^{\top}x_{i,f}^{\text{te}}-y_{i,f}^{\text {te}}\right|^{2}\right\}, \tag{22}\]  
over the bias matrix \(\bar{V}\in\mathcal{C}^{S\text{N}\times S}\), which is used to compute the predictor \(V^{*}(\mathcal{Z}_{f}^{\text{tr}}|\bar{V}^{\text{meta}})\) in (20).  
The issue with the naive extensions (21) and (22) is that the dimension of the predictor \(V\) and of the hyperparameter matrix \(\bar{V}\) can become extremely large when \(S\) grows. This, in turn, may lead to overfitting in the hyperparameter space [53] when the number of frames, \(F\), is limited. This form of overfitting may prevent transfer learning and meta-learning from effectively reducing the sample complexity for problem (20), because the optimized hyperparameter matrix \(\bar{V}\) would be excessively dependent on the data received in the \(F\) previous frames. To solve this problem, we propose next to utilize the structure of the channel model (4) in order to reduce the dimension of the channel parametrization.
## 7 The Search and Retrieval Application (SARA)  
The SARA interface is the consolidated means for accessing the archived metadata from the ARCOMEM KB. In this application, the user may select a campaign and receive an overview page containing metadata and statistics, including the owner of the campaign, duration, number of documents, a word cloud of the most frequent entities, web and social media document distribution and cultural statistics. In the next step, the user may search the campaign archive using text queries. The full text search of SARA returns lists of results at the web resource level, dynamic facets and similarity-based topic suggestions (see Figure 2).  
The dynamic facets of SARA interface presented in Figure 2 are generated from the metadata in the result set and are mainly comprised of named entities and events. Events are returned as a top-level facet item for any search for which the results contain web resources that have events associated with them at the document level. The user can employ these facets to filter the results. In addition, the topics in the topic overview are triggered as suggestions by the interface based on the interaction with the main entities that describe each topic. The list of results includes the web resource title, an indication for the volume of positive and negative opinions expressed in the text of each web resource, and the actual source for each web resource, such as blog, Twitter [33], YouTube [34], _etc._Topics, events and entities can also be explored from the individual web resource pages, as shown in Figure 3. In the web resource view, the user may examine the following:  
* List of entities, grouped by named entity type _i.e._, person, organization, location and sorted alphabetically. For each entity, the following information can be accessed:
* Entity clusters;
* Lists of events;
* Topics.  
The detailed view of the available metadata, such as ETOEs, enrichments and clusters in the SARA interface, is shown in Figure 4. The SARA interface adopted the continuous forward going search model from the early stages of the design [35]. This means that the search and refinement may always continue by clicking the metadata items.  
Figure 2: Search results page in the search and retrieval application (SARA): A list of results at the web resource level sorted by positive opinions expressed in the text, dynamic facets (on the left side) and access to the topic overview (through a button at the top right).  
Figure 4: The detailed view on the topics, events, clusters and enrichments in SARA. (**a**) Topics; (**b**) Events; (**c**) Cluster; (**d**) Enrichment.  
Figure 3: An individual web resource page in SARA.
# Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking
## 5 Conclusions  
1. By examining the digital implementation of the Doppler-aided PLL in scalar tracking-based GPS/INS ultra-tight integration, a theoretical expression of the ratio of phase error amplitudes of an aided loop to an unaided loop is derived, which indicates how the aiding effects are impacted by the product of the frequency of signal dynamics and the INS aiding rate.
2. A new sample-wise aiding method in GPS/INS ultra-tight integration for high-dynamic, high-precision tracking has been presented. Its effectiveness and advantage over traditional aiding have not only been analytically expressed and numerically simulated, but also physically tested using a digital IF signal simulator and a software receiver.  
Figure 14: Comparison of tracking errors using traditional and sample-wise aiding.  
Figure 13: Tracking errors with 500 g dynamics and 80 dB-Hz C/N0. (**a**) Carrier phase error; (**b**) Code phase error.  
The simulation with respect to the effects of two interpolation techniques on the sample-wise aiding performance helps to find the boundary conditions of the aiding methods in terms of the INS aiding data update rate.  
Although the research in this paper is based on the scalar tracking architecture in GPS/INS integration, the sample-wise aiding method can be introduced into the vector tracking-based deep coupling to satisfy high-dynamic, high-precision requirements. In the vector tracking case, the navigation filter output can be interpolated to the sampling rate before controlling the local signal generation of each satellite channel. Similarly, the method can also be extended to the fields of map aiding and integration of GNSS with other wireless positioning. Related future work also includes the analysis of the performance limitation due to the INS data error and the misalignment between GPS and INS.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61271197). [PERSON] conceived the sample-wise aiding idea. [PERSON] and [PERSON] elaborated the problem formulation, designed the simulations and the experiments. [PERSON] performed the tests. [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Scanning Protocols  
The 3D-printed phantom was scanned on a 3 rd generation dual-source 192-slice CT scanner (SOMATOM Force, Siemens Healthineers, Forchheim, Germany). A standard chest CT protocol was used to acquire high-resolution data as the reference images: 100 kVp, 352 mAs, with dose length product 390 mGy.cm [34]. Low-dose and ultra-low-dose CT scans were additionally performed:  
* Low-dose CT: 100 kVp with 180, 150, 120, 100, 80, 50, and 30 mAs
* Ultra-low-dose CT: 80 kVp with 180, 150, 120, 100, 80, 50, and 30 mAs  
Scans were acquired with 1.0 mm slice thickness and 0.5 mm reconstruction interval and reconstructed with advanced modelled iterative reconstruction (ADMIRE) algorithm using kernel 3 strength.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Material** & **Infill** & **Printer Type** \\ \hline Lung nodules, lung, left atrial appendage & PLA & 100\% & FDM \\ \hline Pulmonary venous system & PLA & 95\% & FDM \\ \hline Skin, deep back muscles, inferior vena cava, & & & \\ aorta, brachiocephalic veins, common carotid & ABS & 100\% & FDM \\ arteries, subclavian arteries. & & & \\ \hline Oesophagus, stomach, liver, spleen, thyroid & PLA & 87\% & FDM \\ \hline Muscle, pancreas & PLA & 88\% & FDM \\ \hline Trachea & PLA & 15\% & FDM \\ \hline Heart, diaphragm & ABS & 95\% & FDM \\ \hline Brachiocephalic trunk, subcutaneous fat & PLA & 73\% & FDM \\ \hline Pulmonary vessels & Polyurethane resin (Fabboxible & 100\% & LCD resin printer \\  & Rubber Like UV photosensitive resin) & 100\% & FDM \\ \hline Bone & PLA-Glow & 100\% & FDM \\ \hline \hline \end{tabular} Abbreviations PLA: polylactic acid, FDM: fused deposition modelling, ABS: acrylonitrile butadiene styrene, UV: ultraviolet, LCD: light curing resin.  
\end{table}
Table 2: Materials used for printing phantom tissues.  
Figure 2: Radiodensities of ABS and PLA materials at different infill percentages.  
The phantom was also scanned using the same protocols as the original patient (120 kVp, 125 mAs) to measure HU and assess radiation equivalence.