esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## Appendix A  
In GRACE-like missions, the inter-satellite distance \(L(t)\) can be modelled with the following dominant components  
\[L(t)=L_{0}+L_{1}\sin(2\pi f_{\text{orb}}t)+L_{d}\cdot t \tag{1}\]  
such that the propagation time can be described as  
\[\Delta t(t)\approx L(t)/c_{0}. \tag{2}\]  
We assume the following frequency model for the LRI laser with constant, oscillatory and linear drift components:  
\[\
u_{\text{R}}(t)=\
u_{0}+\
u_{1}\sin(2\pi f_{\
u 1}t)+\
u_{d}\cdot t. \tag{3}\]  
Finally, the phase of the laser can be written as  
\[\Phi_{\text{R}}(t)=\int_{0}^{t}\
u(t^{\prime})\;\text{d}t^{\prime}=\
u_{0}t- \frac{\
u_{1}\cos(2\pi f_{\
u 1}t)}{2\pi f_{\
u 1}}+\frac{\
u_{d}\cdot t^{2}}{2} \tag{4}\]  
or as  
\[\varphi_{\text{TWR}}(t)=\Phi_{\text{R}}(t)-\Phi_{\text{R}}(t-\Delta t) \tag{5}\]  
in terms of the ranging phase \(\varphi_{\text{TWR}}\). These analytical models of the range and phase, with the numerical values given in Table 1, can be used to validate the expressions derived in Section 3.
## 2 Materials and Methods
### Physical Characterization  
#### 2.3.1 Color Measurement  
A colorimeter model CSM-4 (PCE Instruments, Tobarra, C-L.M., Spain) was used to measure the CIELAB color coordinates. The CIELAB is a three-dimensional color space that represents color information in terms of lightness (\(L^{*}\)), red-green axis (\(a^{*}\)), and blue-yellow axis (\(b^{*}\)).  
Color deviation from the standard (\(\Delta E\)) was calculated using Equation (1):  
\[\Delta E_{t,d}=\sqrt{{\Delta{L_{t,d}}^{2}+{\Delta{a_{t,d}}^{2}+{\Delta{b_{t,d}} }^{2}}}}\,\text{or}\Delta E_{t,d}=\sqrt{{({L_{t,d}}-{L_{0}})}^{2}+{\left({{a_{t,d}}-{a_{0}}}\right)}^{2}+{({b_{t,d}}-{b_{0}})}^{2}} \tag{1}\]  
where \(L_{0}\), \(a_{0}\) and \(b_{0}\) are the averages of the readings for the coordinates (\(L^{*}\), \(a^{*}\), \(b^{*}\)) made at time zero for the control, i.e., before the seaweeds undergo any treatment or storage; \(L_{t,d}\), \(a_{t,d}\) and \(b_{t,d}\) are the readings taken after processing (\(t\) = CTRL, MAP or VAC) and storing the samples (\(d\) = 3, 6, 9, 12, or 15 days) [41].  
The parameters for the readings were aperture 20 mm, illuminant D\({}_{65}\), and geometry 45\({}^{\circ}\)/0\({}^{\circ}\) (illumination/viewing angle). The equipment was calibrated for white before collecting the data and the readings were taken immediately after the samples were removed from the packages. For each sample, 12 replicates were measured against a white canvas, and the averages for each coordinate were calculated.
## 4 Feedback Control System and Field-Null Implementation
### Feedback Control System
#### 4.1.1 Low-Pass Filtered PID Principle  
Assuming that the time constant of an analog low-pass filter is \(\tau_{1}=R_{1}C_{1}\), its impulse transfer function \(G_{1}(s)\) is given by the following:  
\[G_{1}(s)=\frac{1}{1+\tau_{1}s} \tag{12}\]  
The mapping from the \(s\)-plane to the \(z\)-plane is given by \(s=\frac{2}{\Delta t}\frac{z-1}{z+1}\), where \(\Delta t\) is the sampling interval. The discrete system function \(H_{1}(z)\) can be derived as follows:  
\[H_{1}(z)=\frac{Y(z)}{U(z)}=\frac{L_{1}\big{(}1+z^{-1}\big{)}}{1+L_{2}z^{-1}} \tag{13}\]  
Taking the inverse \(z\)-transform of Equation (13), the discrete-time difference equation can be derived as follows:  
\[Y(t)=L_{1}U(t)+L_{1}U(t-\Delta t)-L_{2}Y(t-\Delta t) \tag{14}\]  
where \(L_{1}=\frac{\tau_{1}^{*}}{\tau_{1}^{*}+1}\), \(L_{2}=\frac{\tau_{1}^{*}-1}{\tau_{1}^{*}+1}\), \(\tau_{1}^{*}=\frac{\Delta t}{\Delta\tau_{1}}\), \(U(t)\) and \(Y(t)\) represent the input and output signals at time \(t\). Note that Equation (14) can be applied to both the low-pass filters of vectors \(\mathbf{e}\) and \(\mathbf{v}\) in Figure 7.  
Then let us consider the high-pass filter. By applying the same method as that used for deriving Equation (14), one can obtain the discrete-time difference equation of a high-pass filter as  
\[Y(t)=D_{1}U(t)-D_{1}U(t-\Delta t)-D_{2}Y(t-\Delta t) \tag{15}\]  
Figure 7: Block diagram of the filtered PID feedback-control algorithm, where \(\mathbf{I_{in}}\) is the target current, \(\mathbf{e}\equiv\mathbf{I_{in}}-\mathbf{I_{out}}\) is the error vector, \(\mathbf{v}\) is the low-pass filtered error vector and \(\mathbf{u}\) is the output of the PID controller. The inputs of the power supply block include \(\mathbf{u}\) and the feedforward voltage (not shown in this figure), and \(\mathbf{I_{out}}\) is the simulated coil current using the model in Section 2.1.  
where \(\tau_{2}=R_{2}C_{2}\) is the time constant of an analog high-pass filter, \(D_{1}=\frac{1}{\tau_{2}^{*}+1}\), \(D_{2}=\frac{\tau_{2}^{*}-1}{\tau_{2}^{*}+1}\), \(\tau_{2}^{*}=\frac{\Delta I}{\tau_{2}}\), and \(U\), \(Y\) has the same meaning as in Equation (14).  
Using Equation (14) and (15), the I/O relationship of the low-pass filtered PID control algorithm can be written as follows:  
\[\mathbf{v}(t)=L_{1}\mathbf{e}(t)+L_{1}\mathbf{e}(t-\Delta t)-L_{2}\mathbf{v}(t -\Delta t)\]  
\[\mathbf{d}(t)=D_{1}\mathbf{v}(t)-D_{1}\mathbf{v}(t-\Delta t)-D_{2}\mathbf{d}(t -\Delta t)\]  
\[\mathbf{i}(t)=I_{1}\mathbf{v}(t)+I_{1}\mathbf{v}(t-\Delta t)-I_{2}\;\mathbf{i} (t-\Delta t)\]  
\[u=K_{P}*\mathbf{v}(t)+K_{D}*\mathbf{d}(t)+K_{I}*\mathbf{i}(t) \tag{16}\]  
Here, the expressions for \(L_{1}\), \(L_{2}\), \(I_{1}\) and \(I_{2}\) follow the form of the low-pass filter coefficients in Equation (14), \(D_{1}\), \(D_{2}\) follows the ones in Equation (15) and \(K_{P}\), \(K_{D}\) and \(K_{I}\) are the gains of P, D and I, respectively.
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## 1 Introduction
The remaining parts of this report are organized as follows: Section 2 presents the system model, HPA characteristic, and the proposed algorithm; Section 3 includes the results and discussion; and Section 4 presents the conclusions.
## 2 Data
### RO Data from COSMIC, KOMPSAT 5, and MetOp-A,-B, -C GRAS, and TernSAR-X  
Using the GPS RO technique, the COSMIC measurements are traceable to the international standard of time, i.e., the SI second [1, 22, 23]. Ref. [1] has summarized the usefulness of high precision and highly accurate COSMIC data for climate monitoring. Here we used neutral atmospheric variables derived from multiple RO missions, including COSMIC, KOMPSAT-5, and MetOp-A GRAS, and TSX, also processed by CDAAC, to validate the stability of COSMIC-2. The inversion approaches to process COSMIC version 2013.3520, KOMPSAT-5, and MetOp-A,-B GRAS, and TSX data are very similar to the inversion algorithm to process COSMIC-2 data. The descriptions of CDAAC inversion procedures for all these RO missions are also described in [21, 22, 23].  
The data quality of these RO missions (except for COSMIC-2), including precision, stability, and structural uncertainty owing to inversion implementations, has been intensively validated in previous studies (i.e., [30, 31, 32]). All the RO data, including COSMIC-2 data, are downloaded from UCAR CDAAC ([[http://cosmic.cosmic.ucar.edu/cdaac/index.html](http://cosmic.cosmic.ucar.edu/cdaac/index.html)]([http://cosmic.cosmic.ucar.edu/cdaac/index.html](http://cosmic.cosmic.ucar.edu/cdaac/index.html))). We used data from multiple RO missions from October 2019 to January 2020 to quantify the stability of COSMIC-2 observations.
## 3 Results and Discussion
Figure 4.2 The 2D free energy surface contour plot for system E involving DP 10 PIL and DP 10 cellulose in water. CV1 and CV2 represent the numbers of hydrogen bonds formed between them. Figure 11 shows the 2-D free energy contour plot for the FES reconstructed from the system G (DP 20 PSSA and DP 20 PIL chains in water). The conformational analysis at the lowest energy region at CV1 = 0.3-0.6 and CV2 = 0-1 showed a partially dehydrated interaction between the ends of the PSSA and PIL chains. The lowest interaction free energy was about \(-11\) kcal/mol. The FESs for PSSA-cellulose, PIL-cellulose, and PSSA-PIL interactions in aqueous and [EMIM]CI solutions were mapped out using combined MD-MTD simulations. The conformational interactions were found to be largely affected by the solvent. In aqueous solution, the PSSA and cellulose interaction was relatively weak, leading to a slow hydrolysis reaction. In [EMIM]CI, the PSSA and cellulose interaction was much stronger and extensive, leading to a more efficient breakdown of the cellulose chains in excellent agreement with experimental observations. Moreover, the PIL chain had a significantly stronger solvation in both water and [EMIM]CI, compared to the PSSA and cellulose chains, which led to a more effective catalytic conversion of the cellulose in a solid PSSA/PIL catalyst-solid substrate reaction process. Moreover, the solvation free energy of the PIL chain in [EMIM]CI was more than 10 times larger than that in water. This indicates the effectiveness of the [EMIM]CI for cellulose hydrolysis and that the critical role that PIL plays for the catalytic conversion of cellulose, by enhancing the solubility of both the cellulose substrate and the PSSA/PIL catalyst.
## 2 State of the Art  
[PERSON] et al. in their work [26] propose a foundational approach for sentiment analysis utilizing sentiment lexicons. These lexicons consist of words that convey either a positive or negative sentiment. The method involves tallying the sentiment-carrying words and employing them as features in machine-learning techniques to ascertain the sentiment of a given sentence.  
Moreover, in [27], the authors use the tagging method for lemmas with the help of lexicons. The sentiment score was counted as \(Pos(x,pos_{word})-Neg(x,neg_{word})\), where \(x\) is a phrase, \(pos_{word}\) is a positive word from the lexicon, and \(neg_{word}\) is a negative word from the lexicon. This presents the most intuitive way of tagging tweets with lexicons.  
[PERSON] et al. in their publication [28] developed the _Semantic Orientation CALCULATOR (SO-CAL)_ with the aim of investigating approaches that delve into a profound level of analysis, incorporating the semantic orientation of individual words along with contextual valence shifters. Their methodology involves extracting sentiment-bearing words, encompassing various word types such as adjectives, adverbs, nouns, and verbs. These words are utilized to compute the semantic orientation while considering the influence of valence shifters such as downtoners, intensifiers, irrealis markers, and negation.  
The calculation of sentiment relies on two fundamental assumptions, as discussed in [PERSON] et al. [29]: the existence of prior polarity in individual words, meaning they possess a semantic orientation independent of context, and the possibility of quantifying this semantic orientation using numerical values. These assumptions have been widely embraced by various lexicon-based approaches in sentiment analysis ([PERSON] and [PERSON] [30]; [PERSON] and [PERSON] [31]; [PERSON] and [PERSON] [32]).  
Lexicons have played a crucial role in sentiment analysis, providing valuable resources for understanding and analyzing the sentiment of textual data. Among the early pioneers in this field is the General Inquirer lexicon ([PERSON] et al. [33]), which is one of the earliest and most well-known human-annotated lexicons for sentiment analysis. This lexicon comprises a comprehensive collection of 11788 English words, with 2291 words labeled as negative, 1915 as positive, and the remaining words labeled as objective.
## 3 Model and Scheme
### Scheme Implementation
#### 3.2.2 The CCES-Based Scheme
Here, \(e\) represents the end spreading degree that plays a crucial role in the robustness of the CCES scheme. The higher end spreading degree makes the CCES scheme gain more resistant against attack or hostile network environment, but has lower transmission efficiency, which will be proved theoretically and experimentally validated. Assuming \(e\) is 3, the corresponding SrcPort-DstIP-DstPort combinations of \((m_{prs})_{k}\) are shown: \((m_{prs})_{1}\): 282,192.168.1.92,94; 221,192.168.1.97,33; 283,192.168.1.91,95. \((m_{prs})_{5}\): 139,192.168.1.94,46;174,192.168.1.95,81; 140,192.168.1.93,47. \(M_{prs}\) is eventually converted into \(e\cdot t\) combinations where \(t\) is the amount of \(m^{\prime}_{k}\) in \(M^{\prime}\). After embedding them into packet headers, end spreading sequence generation is accomplished. End Spreading Sequence Demodulation  
After synchronization, \(P_{r}\) is prepared to receive the end spreading sequences through the monitoring event. Firstly, \(P_{r}\) effectively filters the irrelevant packets, which benefits from the collaboration relationship between DstIP-DstPort pairs. Next, \(P_{r}\) derives the \(DstPort_{k}\) sequences through SrcPort-DstPort pairs. Given the known LFSR order \(n\), \(PRSkey\), and the length of \(m^{\prime}_{k}\), \(P_{r}\) could generate the correct m-sequence \(PRS\) and modulate it by all possible \(m^{\prime}_{k}\) with the number of \(2^{b}\). After comparing the \(DstPort_{k}\) sequences with all possible modulation outcomes, \(P_{r}\) could determine the \(m^{\prime}_{k}\) and then derive \(M^{\prime}\). Finally, \(M^{\prime}\) is decoded to \(M\), which is contrary to the message preprocessing.
## 2 Related Work
### Quantitative Models for Cyber Resilience  
Quantitative models provide data-driven approaches to measure cyber resilience, often focusing on resistance and recovery capabilities. Recent advancements include mathematical models validated through testbeds to quantify resilience under adversarial conditions [28]. Other studies propose models accounting for multiple defense objectives and resource criticality [29; 30], while reviews highlight limited integration of adaptive mechanisms in existing frameworks [31]. The Cyber Resilience Quantification Framework (CRQF) targets dynamic IT environments [32], and various resilience metrics and scoring practices have been documented [33; 34; 35]. Additional models include resilience matrices [36], stochastic approaches [37], and game-theoretic models for adversarial interactions [38]. Metrics for IT and OT environments [39] and key resilience indicators [40] have also been proposed. Indispensable sources like the ENISA Threat Landscape report [41] are crucial for calibrating the threat entropy term (\(\sigma\)) in our model. Furthermore, the work of [PERSON] et al. [42] provides a concrete application context for our framework within critical infrastructure protection, serving as a target implementation example.  
Despite these advances, many quantitative models prioritize traditional risk metrics over dynamic adaptability and endogenous security principles. Our approach addresses these gaps by offering a structured, dynamic, and practical solution through the RC model and P-R-A-L framework. The advantages of our model include the following:* Structured Design: Based on four clearly defined dimensions--Prevention (P), Destruction Resistance (R), Adaptive Recovery (A), and Evolutionary Learning (L)--providing a comprehensive view of resilience.
* Practical Implementation: Each dimension is supported by a clear, measurable set of indicators designed for real-world applicability using mature technologies.
* Dynamic Nature: The RC model is time-varying, capturing evolving threat landscapes, while the indicators within the P-R-A-L framework support continuous updates to reflect real-time conditions.  
By integrating endogenous security concepts, dynamic quantification, and actionable metrics, our framework offers a significant advancement over existing models, ensuring both theoretical robustness and operational feasibility.
###### Abstract  
Numerous applications of human-machine interfaces, e.g., dedicated to persons with disabilities, require contactless handling of devices or systems. The purpose of this research is to develop a hands-free head-gesture-controlled interface that can support persons with disabilities to communicate with other people and devices, e.g., the paralyzed to signal messages or the visually impaired to handle travel aids. The hardware of the interface consists of a small stereovision rig with a built-in inertial measurement unit (IMU). The device is to be positioned on a user's forehead. Two approaches to recognize head movements were considered. In the first approach, for various time window sizes of the signals recorded from a three-axis accelerometer and a three-axis gyroscope, statistical parameters were calculated such as: average, minimum and maximum amplitude, standard deviation, kurtosis, correlation coefficient, and signal energy. For the second approach, the focus was put onto direct analysis of signal samples recorded from the IMU. In both approaches, the accuracies of 16 different data classifiers for distinguishing the head movements: pitch, roll, yaw, and immobility were evaluated. The recordings of head gestures were collected from 65 individuals. The best results for the testing data were obtained for the non-parametric approach, i.e., direct classification of unprocessed samples of IMU signals for Support Vector Machine (SVM) classifier (95% correct recognitions). Slightly worse results, in this approach, were obtained for the random forests classifier (93%). The achieved high recognition rates of the head gestures suggest that a person with physical or sensory disability can efficiently communicate with other people or manage applications using simple head gesture sequences.  
electronic human-machine interface; blindness; gesture recognition; inertial sensors; IMU 202010 10 4213 17 June 2020 19 June 2020
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 8 Conclusions  
In this paper, we have shown that the noise-limited censored PL data can be used in the training and positioning phases of PL model-based positioning. The censored data, i.e., when PL is larger than the noise threshold, can be taken into account using Tobit MLE when fitting the model to the training data and also in the positioning phase.  
Three different PL distribution examples are used as the true PL. Simulations compared PL model fitting and positioning results both without and with the noise-limited PL data. The results show that if the censored PL is omitted in the training phase, then the fitted PL model overestimates the long link distances. It is also shown that when the censored PL is taken into account, the fitted model matches the true distribution well. Positioning simulations were conducted with a simple log-distance law PL model. The results show improved positioning accuracy when the censored PL is properly taken in to account with MLE. A selection criterion based on PL model properties was presented that limits the number of BSs in the MLE positioning. The positioning error median and 90 th percentile were reduced by 23% to 57% when MLE is used in both phases as compared to when it is omitted in both. Positioning error reductions were demonstrated in a wide range of radio channel properties. These included different path loss exponents, omnidirectional and directional BS antennas, and different BS grid densities. Additionally, separate distributions for LOS and NLOS, various noise threshold values, and LOS detection probabilities were considered. The results also indicate that if the censored PL is taken into account in the positioning phase, then the accuracy of the PL model fitting to training data becomes far less important. With the improved accuracy and robustness against PL model fitting errors, the PL-based positioning show good promise, especially if combined with other high-precision positioning and tracking methods.  
Conceptualization, A.K.; methodology, A.K.; software, A.K.; validation, A.K.; formal analysis, A.K.; investigation, A.K.; resources, A.K., M.V., and J.T.; data curation, A.K.; writing--original draft preparation, A.K., M.V., and J.T.; writing--review and editing, A.K., M.V., and J.T.; visualization, A.K.; supervision, M.V. and J.T.; project administration, M.V. and J.T.; funding acquisition, M.V. and J.T. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results received funding from Academy of Finland project 323244 (LOCALCOM) and 328214 (ULTRA).  
Not applicable.  
The authors declare no conflict of interest.
# Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa
## Materials  
### Fire Radiative Power (METEOSAT FRP-PIXEL Product)  
Meteosat's SEVIRI acquires multispectral imagery every 15-min across the full Earth disk, extending to Europe, North and South Africa and part of South America. The ground sampling distance (GSD) is 3 km at the sub-satellite point (SSP), and grows larger as the viewing zenith angle increases away from this location [43]. The FRP-PIXEL product provided by the LSA SAF is generated via an operational version of the Fire Thermal Anomaly (FTA) geostationary active fire detection algorithm of reference [44] and the middle-wave infrared (MWIR) radiance FRP retrieval approach of references [18; 20]. Comparisons to 1 km MODIS FRP data collected concurrently with the SEVIRI data indicates that the SEVIRI product suffers from major omission errors for fires with an FRP less than \(\approx\)40 MW compared to MODIS due to SEVIRI's much coarser pixel area. However, for higher FRP fires detected by SEVIRI near simultaneously with MODIS, 76% had a SEVIRI retrieved FRP within 30% of the matching MODIS measure [42]. Given the rapidly changing nature of fire activity, the up to 6 min time difference between the two sensors views, and the fact that even absolutely simultaneous MODIS views of the same fire indicate a \(\pm\)24% uncertainty exists in the MODIS FRP measures [45], this comparison exemplifies the strong performance of the SEVIRI FRP-PIXEL product. Thus, whilst SEVIRI underestimates regional fire activity through the omission of small or low intensity fires, it is capable of reliably measuring FRP from fires that are sufficiently large and/or intense enough to be above the 40 MW detection threshold, and indeed, the inter-comparison to other SEVIRI-based active fire products [42] over southern Africa indicates the FRP-PIXEL product has the best active fire detection performance of any such product.
# Chronotype, Risk and Time Preferences, and Financial Behaviour
## Literature Review
### Conceptual Model and Hypotheses Development
#### 2.2.5 The Mediating Role of Financial Risk Preference (H6)  
To provide a richer explanation of why morningness influences investments in stock, we use theories of financial risk preferences. On the one hand, previous research reported that morning larks are less likely to engage in financial risk behaviour and are negatively associated with risk attitudes [25]. These researchers argued that self-control is the underling mechanism, as self-control is able to inhabit impulsivity, which contributes to risky behaviour. Morningness-types tend to be less impulsive. [PERSON] and [PERSON] [78] supported this argument as well. Accordingly, it is reasonable to expect that morning larks have a lower level of financial risk preference as compared with evening owls. On the other hand, another stream of literature advocated the opposite, such that happiness is positively associated with risk attitude. [PERSON] and [PERSON] [79] documented that optimism increases individuals' risk preferences, as optimistic people' cognitive appraisals engender high certainty and a higher probability of positive outcomes. Depressed-mood people are more risk averse when compared with neutral and positive-mood individuals [80]. Although the prior literature has provided contradictory arguments regarding the relationship between positive moods and risk preference, given the evidence in the literature about the association between the morning chronotype and risk attitude, and that risk preference as a predictor of participation in the stock market [26], we propose the following hypothesis:  
**Hypothesis 6**.: _Financial risk preference mediates the relationship between morning-types and stock market participation._