esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Response to Temperature Variations  
In Figure 5 as well as Table 2, the response of the individual Bragg reflection peaks to applied temperature is illustrated for a constant RI (air). Ten measurements were performed for every temperature step and the obtained mean values for the measured Bragg reflection peaks per temperature step are depicted in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & _P01_ & _P02_ & _P03_ & _P04_ & _P05_ & _P06_ & _P07_ & _P08_ & _P09_ & _P10_ & _P11_ \\ \hline pm/\({}^{o}\)C & 9.6 & 9.7 & 11.6 & 14.3 & 9.8 & 10.9 & 11.8 & 11.6 & 13.4 & 10.5 & 12.6 \\ \(R^{2}\) & 0.88 & 0.95 & 0.99 & 0.98 & 0.99 & 0.97 & 0.98 & 0.99 & 0.99 & 0.98 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitivities of the different Bragg reflection peaks to applied temperature.  
Figure 4: Bragg wavelength shift for the highest (_P01_) and lowest (_P11_) PMG for different SRI solutions.  
Figure 5: Reflection spectrum of etched GI-MMF with BG for the measurement of different surrounding temperatures (**a**) and the response of the individual Bragg reflection peaks to different applied temperature values (**b**) _P01–P11_ indicate the Bragg reflection peaks in the reflection spectrum from the left to the right, i.e., the Bragg reflection peak of the highest PMG is marked with _P01_, whereas the Bragg reflection peak of the lowest PMG is marked with _P11_.  
According to the results obtained, all individual Bragg reflection peaks show linear behavior (\(R^{2}\) value \(\geq\) 0.88) as well as an almost identical sensitivity to applied temperature (on average 11.4 pm/\({}^{o}\)C). This result is consistent with the literature (11.5 pm/\({}^{o}\)C) for GI-MMF [6]. Since all modes have almost the same thermo-optic coefficient all Bragg reflection peaks show almost identical temperature sensitivity [6]. A standard deviation of 0.014 nm has been obtained for the temperature measurements using the Bragg reflection peak _P11_ (since _P11_ exhibit negligible or no sensitivity to applied RI, it is more suitable for the determination of applied temperature), for instance.
## 2 Problem Analysis and Modeling
### Attitude Transition Angles and Time  
In the agile satellites, the transition time is calculated based on attitude angles. In the actual scene, the agile satellite attitude transition is a multi-stage process from slowly accelerating to a constant speed, and lastly decelerating. The required stabilization time should be counted after the attitude control action is completed and the transition time is usually calculated based on the method of attitude angle synthesis. This paper utilizes a piece-wise linear function to approximate the time-dependent attitude transition time. When the total attitude angle changes with few degrees, the attitude maneuver time holds a fixed value. When the total attitude angle change reaches a certain threshold, the linear function is used to calculate the attitude maneuver time.  
\[trans_{i,j}^{k}=\left\{\begin{array}{l}\lambda_{0},\Delta\theta_{i,j}\leq \theta_{1}\\ \frac{\Delta\theta_{i,j}}{\Delta\theta_{i,j}}+\lambda_{1},\theta_{1}<\Delta \theta_{i,j}<\theta_{2}\\ \frac{\Delta\theta_{i,j}}{\alpha_{2}}+\lambda_{2},\theta_{2}<\Delta\theta_{i, j}<\theta_{3}\\ \frac{\Delta\theta_{i,j}}{\alpha_{3}}+\lambda_{3},\theta_{3}<\Delta\theta_{i, j}<\theta_{4}\\ \frac{\Delta\theta_{i,j}}{\alpha_{4}}+\lambda_{4},\theta_{4}<\Delta\theta_{i,j} \end{array}\right. \tag{9}\]  
\[tw_{i}^{s},tw_{i}^{e},p_{i},l_{i},t_{i}^{s},t_{i}^{e},E,e_{i},trans_{i,j}\geq 0 \tag{10}\]  
\[\Delta\theta_{i,j}=|\theta_{i}^{roll,s}-\theta_{i}^{pitch,s}|+|\theta_{i}^{roll,e}-\theta_{i}^{pitch,e}| \tag{11}\]  
\(\lambda_{0},\lambda_{1},\lambda_{2},\lambda_{3},\lambda_{4}\): the required stabilization time for satellite attitude transition at the different stages.  
\(\Delta\theta_{i,j}\): the total attitude angle changes from \(task_{i}\) to \(task_{j}\) and the threshold value to calculate the attitude transition time.  
\(\theta_{1},\theta_{2},\theta_{3},\theta_{4}\): the thresholds of attitude transition angles at the different stages.  
\(v_{1},v_{2},v_{3},\
u_{4}\): the attitude transition speeds at the different stages.  
\(\theta_{i}^{roll,s}\): the start roll angle.  
\(\theta_{i}^{roll,e}\): the end roll angle.  
\(\theta_{i}^{pitch,e}\): the end pitch angle.  
According to the research about GRILS ([PERSON] et al., 2018), Table 1 displays these constants for attitude transition and task parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Attitude Transition Speeds & \(v_{1}=1.5\), \(v_{2}=2\), \(v_{3}=2.5\), \(v_{4}=3\) \\ Attitude Stabilization Time & \(\lambda_{0}=11.66\), \(\lambda_{1}=5\), \(\lambda_{2}=10\), \(\lambda_{3}=16\), \(\lambda_{4}=22\) \\ Thresholds of Attitude Transition Angles & \(\theta_{1}=10\), \(\theta_{2}=30\), \(\theta_{3}=60\), \(\theta_{4}=90\) \\ Duration Time Required for Imaging & \(l\sim\)N (15, 30) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attitude transition and task parameters.
# Coreference Resolution for Improving Performance Measures of Classification Tasks
## 3 Methodology
### Coreference Resolution Algorithm--Neuralconf  
Before we introduce the characteristics of the neuralcoref algorithm, we should consider one of the three different CR approaches [19]:  
* Deterministic--coreference identification based on natural language rules [20; 22];
* Statistical--coreference identification based on machine learning [19];
* Neural--coreference identification based on the neural network [20; 22].  
We decided to use the open-source library neuralcoref in our work, which is an implementation of the mention-ranking coreference model from [23] and provides very good results in terms of coreference resolution. The current version of the neuralcoref library (as of November 2022) is 4.0. It was necessary to have Spacy version 2.1.0 installed.  
The product of the model is the rating (score) for the pair s(c,m), where c is designated as a candidate for antecedent and m is a mention. The given score represents the compatibility of the coreference with the feedforward neural network [24].  
At the same time, the model extracts different words, e.g., the base form of a mention with a group of words. Each word is represented by a vector \(\mathrm{w_{i}}\in\mathbb{R}^{\mathrm{d_{w}}}\), and each group of words is represented by the average of the vectors of all the words in the group. It also  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset Name** & **All Logs** & **All Words** & **Unique Words** & **Average Number of Words in a Log** & **Shortest Log** & **Longest Log** \\ \hline KaiDMML (real) & 197 & 124,390 & 6920 & 631.42 & 31 & 5459 \\ \hline KaiDMML (fake) & 208 & 87,909 & 5728 & 422.64 & 5 & 4155 \\ \hline KaiDMML fake + real & 405 & 212,299 & 9224 & 524.20 & 5 & 5459 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of records in the KAIDMML dataset.  
Figure 4: KaiDMML dataset sample [21].  
considers the distance and match of strings. Heuristic loss functions were also used when training the model [25].  
In the context of gender association, the trained word embeddings refer to vectors that capture the relationship between words and their gender connotations. These vectors are generated using algorithms that analyze large datasets, such as the OntoNotes corpus. By utilizing the OntoNotes corpus, the algorithm is able to train the word embeddings to accurately determine the gender of given entities. This includes the ability to assign a coreference even for unknown entities (such as nicknames, names of celebrities, etc.). Overall, the trained word embeddings enable the algorithm to effectively analyze and determine gender associations, providing valuable insights into the feminine and masculine gender connotations of the given entities [26].
# On the Lagged Diffusivity Method for the Solution of Nonlinear Finite Difference Systems
## 4 Solution Procedure
### Starting Vectors and Stopping Criteria  
Starting vectors and tolerances of the iterative procedures are chosen as in the diagram in Figure 2, where \(\hat{\eta}\in(0,1)\) and \(\epsilon_{v}\) is initialized by \(e_{1}=\delta_{0}\|\mathbf{F}(\mathbf{u}^{(0)})\|\), with \(\hat{\epsilon}\) positive constant smaller than 1.  
The choice of the starting vectors is made so to start as close as possible to the solution of the system. Moreover, it also allows to write conveniently the linear system arising at each Newton iteration (e.g., see [16]). The stopping criteria, on the other hand, descend directly by the description of the LDM and by the choice of an inexact Newton iteration to solve the inner systems.  
The starting vectors determine also the initialization of the tolerances: for example, at the first Newton iteration of the \((\
u+1)\)-th lagged iteration, the tolerance of the linear solver is \(\hat{\epsilon}_{1}=\hat{\eta}\|F_{\
u}(\mathbf{u}^{(\
u+1,0)})\|=\hat{\eta}\|F_{ \
u}(\mathbf{u}^{(\
u)})\|\). We can also easily notice that all the tolerances are initialized at \(\hat{\eta}\|F_{0}(\mathbf{u}^{(0)})\|=\hat{\eta}\|F(\mathbf{u}^{(0)})\|=\epsilon_{1}\) at the first lagged iteration.  
However, if we do not apply any correction, the initial tolerance of the linear solver, \(\hat{\epsilon}_{1}\), may become smaller than the minimum possible tolerance of the simplified Newton method, \(\hat{\eta}\epsilon_{\
u+1}\). Thus, \(\hat{\epsilon}_{1}\) would be smaller than what could be required by an inexact Newton method, leading to an increase in computational cost with no foreseeable improvement in accuracy.  
To avoid this situation, we simply impose \(\hat{\eta}\epsilon_{\
u+1}\) as the minimum acceptable tolerance of the linear solver:  
\[\hat{\epsilon}_{1}=\max\left(\hat{\eta}\|F_{\
u}(\mathbf{u}^{(\
u)})\|,\hat{\eta} \epsilon_{\
u+1}\right). \tag{16}\]  
This does not lead to a worsening of the accuracy of the lagged procedure (indeed, \(\hat{\eta}\epsilon_{\
u+1}\) is the tolerance of the simplified Newton method), while significantly reducing the computational cost.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 3 Proposed Protocol
### Neighbor Discovery  
After the initialization phase, a neighbor discovery phase begins where it is considered that each node broadcast a four bytes request message (see Figure 3) in defined transmission range to discover the neighboring nodes. The packet encapsulates sender ID and a timestamp label containing the time when the packet is transmitted.  
In response, neighboring nodes forwards an \"INFO\" message of 6 bytes, containing sender/neighbor ID, timestamp, hop count and distance to the sink with a  
Figure 3: Request Packet Format.  
Figure 2: Working methodology of SOSRP.  
Upon receiving the packet, the node generates the neighbor table storing neighbor ID, hop count and distance from sink. The Time of Arrival technique is considered for calculating the distance between two nodes and it represents the accumulated hop-to-hop distance from that node to the destination. To conserve the energy, the neighbor discovery phase is only initiated when a change in topology is detected, such as nodes addition or losses.
## 3 Phase-Bias Results Assessment  
### Assessment of the CNES Phase Buses  
In order to highlight the differences between the RT and the POST products, this section displays the time series for several products for the four main constellations over five days, ranging from Day of Year (DOY) 2022-079 to DOY 2022-083. Table 4 gives the observable biases that can be found in the files available on the PPP-WIZARD project webpage for the proposed days. These observables are separated by constellations and frequency bands. They are given as per their rinex codes with constellation letter, frequency number, and tracking mode. Figures 6-8 display the code and phase biases computed by the PPP-WIZARD demonstrator and made available for the scientific community. Note that no phase biases are produced for Glonass. The Russian constellation uses Frequency Division Multiple Access (FDMA) to distinguish the signals emitted by the satellites. This prevents us from solving for the integer ambiguities of Glonass carrier-phase observables. The graphs shown are limited to some frequencies for brevity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **MW WL** & **IF** & **WL IF** & **WL IF** & **WL IF** \\ \hline GPS & F1/F2 & F1 / F2 & F1/F2/F5 & – & – \\ GAL & E1/E5a & E1/E5a & E1/E5a/E6 & E1/E5a/E5b & – \\ BEI & B1I/B3I & B1I/B3I & B1I/B3I/B2I & B1I/B3I/B1c & B1I/B3I/B2a \\ \hline \end{tabular}
\end{table}
Table 3: Choices of combinations for ambiguity resolution. MW WL stands for Melbourne–Wübbena-widelane, IF stands for ionosphere-free phase, and WL IF stands for ionosphere-free phase widelanes combinations.  
Figure 6: (a) Code- and phase-biases comparison for GPS C5Q and L5I signals (each color represents a satellite). (b) Code- and phase-biases comparison for Galileo L5 band (each color represents a satellite).  
Figure 8: Code-biases comparison for Glonass C1C frequency (each color represents a satellite).  
Figure 7: Code- and phase-biases comparison for Beidou C2I and L2I signals (each color represents a satellite).  
The first row of these figures depicts code or phase biases computed in real-time mode, whereas the second row contains the biases produced with the previously described post-processed mode. The post-processed code and phase biases for all the presented constellations and signals have discontinuities at the break of each day. This is explained by the fact that the products used every day for the POST bias calculations are discontinuous themselves. Such daily jumps in the time series are not to be seen in the RT products. However, some outliers are visible in the RT phase biases for all the constellations. The RT code biases are slowly drifting over the five days duration, whereas the POST code biases are constant over a whole day. Missing values can be seen in the RT products for Galileo and Beidou. This phenomenon may be due to some latency in obtaining the GNSS observables from all the stations.
# Approaches and Challenges in Internet of Robotic Things
## 2 IoRT: An Overview
### IoRT Architectures
#### 2.6.2 Four-Tier Architecture  
According to [47], IoRT has a four-tier architecture, divided into four layers for reliable data communication: (i) hardware layer, (ii) support layer, (iii) network layer, and (iv) application layer. The roles of three of the layers were discussed above; the fourth support layer is described below.  
**Support layer:** The support layer provides security in the architecture of IoRT. In a three-tier architecture, data are directly communicated to the network layer, which is susceptible to attacks. The support layer consists of antiviruses and secure computing, overcoming the flaws of the three-layer architecture. Information obtained from the perception layer is sent to the support layer, which provides authenticity to the user. Then, the support layer sends information to the network layer.
# StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks
## 3 The Proposed Method
### General Formula of Similarity Measure Based on Meta Structure
#### 3.3.3 Semantic Meaning Explanation with an Example  
In this section, we use an example to describe the semantic meaning of subgraph expansion probability with bias \(h\Big{(}g_{b}(i|S,G),g_{f}(i|S,G)\Big{)}\). Figure 1 is an HIN example. The given source node is Toby Kebbell and the meta structure is \(S\). Figure 2 denotes the process of subgraph expansion based on the meta structure. It is obvious that there exist two nodes connecting with Toby Kebbell through relation \(\frac{dcellIn}{\cdots}\), namely, War Horse (film) and Wrath of the Titans. Hence, the number of subgraphs in the first layer is \(g_{b}(1|S,G)=2\). For subgraph 2(a), the forward subgraph of next-hop node W. Horse is 1(a); the number of the forward subgraph is \(|g_{f}(1|S,G)|=1\). By a weighted combination of \(|g_{f}(1|S,G)|\) and \(|g_{b}(1|S,G)|\), we can obtain the expansion probability with bias \(h\Big{(}g_{b}(i|S,G),g_{f}(i|S,G)\Big{)}\). In this paper, we set out-neighbors and in-neighbors to have the same important weight in the expansion probability with bias, which means \(\omega=0.5\). Consequently, we can obtain the expansion probability with bias from 1(a) to 2(a), that is \(h\Big{(}g_{b}(1|S,G),g_{f}(1|S,G)\Big{)}=\frac{1}{2}\Big{(}\frac{1}{|g_{b}(1| \mathcal{S},G)|}+\frac{1}{|g_{f}(1|\mathcal{S},G)|}\Big{)}=\frac{1}{2}\Big{(} \frac{1}{2}+1\Big{)}=\frac{3}{4}\). The different values for \(\omega\) mean that the proportion of the weight of out-neighbor and in-neighbor information is different in the expansion probability with bias. We can adjust the range of \(\omega\) to determine the optimal value for a specific task.  
In the same way, we can compute the subgraph expansion probability with bias of the remaining layers of the meta structure. Based on the expansion probability with bias, given an instance s of the meta structure, the product of probabilities with bias of all the layers of \(s\) is the similarity between the source node \(x\) and the target node \(y\) based on \(s\), denoted by \(f(x,y|s)\). In general, given the source node \(o_{s}\), HIN G = (V, E) and the meta structure \(S\), we can obtain all instances of \(S\) through expanding the subgraph along the layer of the meta structure in graph G. For instance, in Figure 1, through subgraph expansion starting from source node [PERSON] along the meta structure \(S\), we can obtain the instance of the meta structure shown by a dashed line. For each instance, we compute the \(f(x,y|s)\) and then accumulate the similarity scores based on all instances of the meta structure to obtain the final similarity \(StructSim(x,y|S)\). It is obvious that StructSim models the probability of subgraph expansion with bias from the source node \(x\) to the target node \(y\) along a given meta structure \(S\). Two objects are more similar if they are connected by more instances of the meta structure.
# Flow Duration Curves from Surface Reflectance in the Near Infrared Band
## 4 Discussion
Although errors are still relevant when applying the proposed methodology to reconstruct the discharge series, those biases appear somehow attenuated removing the temporal component, thus focusing more on the frequency of occurrence of the discharge over a period of reference than on its dynamic in time. This study represents a first attempt to analyze the FDC with a long and easily accessible satellite dataset. However, for the nature of the sensor, it is not possible to totally overtake the intrinsic limit of the passive sensor that is linked to the cloud coverage. In this context, the use of satellite altimetry can be a valuable solution, due to the high level of accuracy it demonstrated in previous works [28; 29], especially if multi-mission approaches are used [24; 30; 31; 32]. The merging procedures available in literature [33; 34; 35] demonstrated the importance and the potential of the combination of more sensors to catch the different aspects of a same phenomenon, which could be helpful for the description of the flow regime. In particular, the approach proposed here can be profitable under different points of view. The ground observations play an important role in the evaluation of the flow regime of a river. However, due to the not-uniform distribution of the gauged stations in the world, their use should be combined with other source of data with a larger coverage in space and time, like the satellite observations. Because the river discharge is an undirect measurement, so far, no satellite observation is able to derive it directly. However, satellite sensors can provide complementary information. This study showed an example of the use of NIR sensors for providing a reliable frequency of the flow. For instance, the proposed approach can be used in the case of the decommissioning of a gauging station, ensuring a continuous monitoring of the flows and the estimation of the hydrological regime. The implementation of this approach at completely ungauged river sites is so far prevented by the calibration phase, which requires concurrent availability of C/M and ground-surveyed discharge series. However, future investigations will be dedicated to trying to overtake such constrains, perhaps considering the possibility to link the existing relationship among Q and C/M to other factors (i.e., topographic, hydrological, etc.) easily available on remote areas. This would definitely improve our capability to estimate the hydrological regime along ungauged rivers. In addition, the small number of satellite data required by the presented approach (a temporal series of image product) can be considered highly advantageous with respect to hydrological or land surface models that require numerous observations (precipitations, soil moisture, terrestrial water storage) and ancillary data.
## 2 Materials and Methods
### Data Collection and Processing  
#### 2.2.1 Image Data  
The data utilized in this study consist of Sentinel-2 satellite imagery and DEM, all obtained from the GEE platform database. Specifically, nine spectral bands from Sentinel-2 imagery were chosen, with details provided in Table 1. Using a cloud removal algorithm, images from 2018 were selected, ensuring cloud coverage did not exceed 10% in the chosen areas of Czech. These images underwent radiometric, geometric, and atmospheric corrections as part of preprocessing, which involved resampling, clipping, and mosaicking.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sentinel-2 Band** & **Center Wavelength (\(\mu\)m)** & **Resolution (m)** \\ \hline Band 2—Blu-ray & 0.490 & 10 \\ Band 3—Green Light & 0.560 & 10 \\ Band 4—Red Light & 0.665 & 10 \\ Band 5—Red Edge 1 & 0.705 & 20 \\ Band 6—Red Edge 2 & 0.740 & 20 \\ Band 7—Red Edge 3 & 0.783 & 20 \\ Band 8—Near Infrared & 0.842 & 10 \\ Band 8A—Narrow Wave NIR & 0.865 & 20 \\ Band 11—Shortwave Red 1 & 0.945 & 60 \\ Band 12—Shortwave Red 2 & 1.375 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-2 spectral band information.  
Figure 1: Research area overview.