esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 2 Background  
The Testbed in Aarhus for Precision Positioning and Autonomous Systems (TAPAS), is a dense network of GNSS reference stations (see Figure 1) that allows for precise network RTK (NRTK) positioning in the city of Aarhus in Denmark [24] (The TAPAS RTK service is freely available through the Danish Agency for Data Supply and Efficiency, SDFE. For more information, visit www.tapasweb.dk). Distances between stations are as low as 4.1 km (TA03-TA05) and up to just 22.3 km across the bay of Aarhus (TA04-TA09).  
Figure 1: The TAPAS real-time kinematic (RTK) network around the city of Aarhus, with zoomed in view on the location of stations TA03 and TA10 as well as the monitoring station © The Danish Agency for Data Supply and Efficiency (SDFE).  
The TAPAS stations all have a similar setup and use Sepentrrio PolaRx5S reference station receivers with Leica AR20 antennas. The stations deliver continuous GNSS observation data from the four major constellations (GPS, GLONASS, Galileo, and BeiDou) using the signals shown in Table 1. The GNSMART software from Geo++ is used to generate a virtual reference station (VRS) solution [25].  
Besides the reference stations, the network also includes a monitoring station. This station is a permanent static rover connected to the VRS solution from the network, and it has been established in order to assess the performance at all hours of the day. It uses a consumer-grade multi frequency, quad constellation receiver, and antenna.  
Owing to the density of the network, archived GNSS data can be used for processing several different PPK solutions between any of the stations within the network.  
Investigations into jamming on the TAPAS network have previously been reported [8], and these showed a total of 1934 jamming incidents on the 11 TAPAS stations during 2019. As with the STRIKE3 report [10], this study suggests that stations placed in areas with human activity (major roads, harbor, and so on) see the majority of the jamming, and that the incidents in general are of short duration and low power. Although we have no further knowledge on the actual interference sources, the low power and short duration could be due to interferers being small personal privacy devices (PPDs) found in vehicles passing nearby the stations.  
This investigation will focus on jamming incidents detected at stations TA03 and TA10. Information about the placement of these stations, as well as the monitoring station, is shown with a map overview of TAPAS in Figure 1.  
Both stations are placed at public schools, and, with TA03 being closest to a major road, this station also sees more jamming than TA10. Antennas are naturally placed on the roofs of the schools a few meters above ground. Thus, with an antenna gain pattern that highly attenuates signals coming from below, the impact of signals from jammers would be reduced compared with the impact on a consumer-grade GNSS receiver at street level.
# Polyhedral DC Decomposition and DCA Optimization of Piecewise Linear Functions
## 5 Exploiting the Convex/concave Decomposition for the DC Algorithm
### Proximal Rather Than Global  
By some authors the DCA algorithm has been credited with being able to reach global minimizers with a higher probability than other algorithms. There is really no justification for this optimism in the light of the following observation. Suppose the objective \(f(x)=\frac{1}{2}(\widetilde{f}(x)+\widehat{f}(x))\) has an isolated local minimizer \(x_{*}\). Then there exists an \(\varepsilon>0\) such that the level set \(\{x\in\mathbb{R}^{n}:f(x)\leq f(x_{*})+\varepsilon\}\) has a bounded connected component containing \(x_{*}\), say \(\mathcal{L}_{\varepsilon}\). Now suppose DCA is started from any point \(x_{0}\in\mathcal{L}_{\varepsilon}\). Since \(f_{0}(x)=\frac{1}{2}(\widetilde{f}(x)+\widehat{f}(x_{0})+\widehat{g}(x_{0})^{ \top}(x-x_{0}))\) is by Equation (37) a convex upper bound on \(f(x)\) its level set \(\{f_{0}(x)\leq f(x_{0})\}\) will be contained in \(\mathcal{L}_{\varepsilon}\). Hence any step from \(x_{0}\) that reduces the upper bound \(f_{0}(x)\) must stay in the same component, so there is absolutely no chance to move away from the catchment \(\mathcal{L}_{\varepsilon}\) of \(x_{0}\) towards another local minimizer of \(f\), whether global or not. In fact, by adding the convex term  
\[\tfrac{1}{2}\left(\widehat{f}(x_{0})+\widehat{g}(x_{0})^{\top}(x-x_{0})- \widehat{f}(x)\right)\ \geq\ 0\,\]  
which vanishes at \(x_{0}\), to the actual objective \(f(x)\) one performs a kind of regularization, like in the proximal point method. This means the step is actually held back compared to a larger step that might be taken by a method that only requires the reduction of \(f(x)\) itself.  
Hence we may interpret DCA as a proximal point method where the proximal term is defined as an affinely shifted negative of the concave part. Since in general the norm and the coefficient defining the proximal term may be quite hard to select, this way of defining it may make a lot of sense. However, it is certainly not global optimization. Notice that in this argument we have used neither the polyhedrality nor the inclusion property. So it applies to a general DC decomposition on Euclidean space. Another conclusion from the \"holding back\" observation is that it is probably not worthwhile to minimize the upper bound very carefully. One might rather readjust the shift \(\widehat{g}^{\top}x\) after a few or even just one iteration.
## 5 Link-Based Stability Notions
### Additive-Weight
However, the stability notion that is considered in [15] is the classical one, while we intend to consider \(\alpha\)-stability, link-additive stability and link-maximal stability. Our idea is to exploit this encoding to define a general method to find, given an SMW, the link-additive stable marriage and the \(\alpha\)-stable marriage with the maximum additive-weight. Since we have shown that, given an SMW, \(P\), the set of the link-additive (resp., \(\alpha\)-) stable marriage of \(P\) coincides with the set of the weakly-stable marriages of the stable marriage problem with ties \(Linkm(P)\) (resp., \(\alpha(P)\)), then, to compute one of the link-additive (resp., \(\alpha-\)) stable marriages, we just need to find a classical weakly stable marriage of \(Linkm(P)\) (resp., \(\alpha(P)\)). Furthermore, since we want to find the link-additive (resp., \(\alpha-\)) stable marriage with the highest additive-weight, then we can consider the SMW with ties \(wLinkm(P)\) (resp., \(w\alpha(P)\)), which is obtained by \(Linkm(P)\) (resp., \(\alpha(P)\)) by associating with each pair their link-additive strength (resp., their initial score). Then, we can apply the encoding shown in [15] to this SMW with ties. More precisely, since we want to maximize the sum of the link-additive strengths of the pairs, we must use the encoding shown in [15], which encodes an SMW as a weighted CSP, where the preferences are interpreted as costs, and the goal is to find the solution (i.e., the assignment of values to the variables) with the minimum sum of the costs. The procedure that we propose first computes the SMW with ties \(wLink(P)\). Next, it computes \(Neg(wLink(P))\), which is the SMW with ties obtained by \(wLink(P)\) by changing the score of every pair, say \(x\), with \(-x\), and by adapting all the orderings of the pairs. Finally, it encodes the problem obtained so far as a weighted CSPs, and thus, it computes an optimal solution of such a weighted CSP. The same algorithm can also be used to find the link-max (resp., \(\alpha\)) stable marriage of \(P\) with the maximum additive-weight. We have only to replace \(wLinkm(P)\) with \(wLinka(P)\) (resp., \(\alpha\)) in our procedure.
## 2 Materials and Methods
### Short Overview of Reliability Methods  
Reliability analyses using the FOSM analytical, Taylor Series, FORM and Simplified FORM methods were conducted using algorithms developed in Matlab programing language, while the Monte Carlo simulations were conducted in an application developed in the c# programming language. All of the used algorithms are validated by the computer program \"Rt\" [30]. Compared to \"Rt\", the developed algorithms are adapted to the problem in question, simplified in data input, include the calculation of the design value of permanent vertical action according to Eurocode 7, and provide additional possibilities when printing out the results. The algorithm used for conducting the Monte Carlo simulations is optimized for quicker execution and additional printing possibilities, e.g., the graphic representation of the histogram of the performance function and the dependence of the \(\mathrm{COV_{pf}}\) value to the number of simulations. Direct integration was performed using an algorithm developed in the c# programming language and _.NET Core framework_. Analyses done using the Analytical FOSM, Taylor Series and PEM methods were performed with the assumptions that the FS follows both normal and lognormal distribution. The following abbreviated notation was adopted for easier assumption recognition: normally distributed FS--method name N; lognormally distributed FS--method name LN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Expected Performance Level** & \(\beta\) & **Probability of Unsatisfactory Performance** \\ \hline High & 5.0 & \(3\times 10^{-7}\) \\ Good & 4.0 & \(3\times 10^{-5}\) \\ Above average & 3.0 & 0.01 \\ Below average & 2.5 & 0.06 \\ Poor & 2.0 & 0.023 \\ Unsatisfactory & 1.5 & 0.07 \\ Hazardous & 1.0 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Expected performance level according reliability indexes [16].
## 3 Measure of Sparseness  
In [20], the sparseness of a channel impulse response is measured by Equation (6).  
\[\chi=\frac{L}{L-\sqrt{L}}\Bigg{(}1-\frac{\left\|\hat{\mathbf{w}}\right\|_{1}}{ \sqrt{L}\left\|\hat{\mathbf{w}}\right\|_{2}}\Bigg{)}, \tag{6}\]  
where \(\left\|\hat{\mathbf{w}}\right\|_{p}\) is the p-norm of \(\hat{\mathbf{w}}\) and \(L\) is the dimension of \(\hat{\mathbf{w}}\). The range of \(\chi\) is \(0\leq\chi\leq 1\). That is dependent on the sparseness of \(\hat{\mathbf{w}}\). As \(\hat{\mathbf{w}}\) becomes sparser, the sparsity, \(\chi\), comes close to 1, and as \(\hat{\mathbf{w}}\) becomes denser, \(\chi\) comes close to 0. We often have small and none-zero value of \(\chi\), even in a dense channel. For example, Figure 1 shows the relation of the value of \(\chi\) and the percentage of none-zero components in \(\hat{\mathbf{w}}\) with \(L=215\). In Figure 1, we consider all possible cases of none-zero components in \(\hat{\mathbf{w}}\).
# Exopolysaccharides from Cyanobacteria: Strategies for Bioprocess Development
## 2 EPS from Cyanobacteria  
### Ecology  
Cyanobacteria are a very diverse taxonomic group that can be found both in unicellular or multicellular forms of coccoid and/or filamentous [7]. These prokaryotic organisms have a bacterial like cellular envelope. They are structurally similar to Gram-negative bacteria, although some features and thickness of peptidoglycan resemble Gram-positive bacteria. In nature, cyanobacterial EPS can play a variety of functions such as adhesive, structural, protection against abiotic stress, bio weathering processes, gliding motility, and nutrient repositories in phototrophic biofilms or biological soil crusts [22; 23; 24]. The excretion of EPS, and in particular CPS, can occur via a junctional pore complex (JPC). The quantity and compactness of carbohydrates can help to sort the different types of CPS. Sheaths and capsules are more compact which aim to protect the cellular environment, from abiotic or biotic stresses, while the slime excretions are less compact and allow the cyanobacteria to have gliding motility. Some CPS may also contain aromatic pigments, like scytonemin or oligosaccharidesacharide mycosporine-like amino acids. These molecules which absorb UV light can be found in cyanobacteria located in highly exposed sunlight areas [25]. Interestingly, the RPS fraction is less noted in nature, whereas only under laboratory conditions can be easily detected and quantified [26].
## 3 Experimental Section
### Preparation of Gelatin (STB-G) and Gelatin Hydrolysate (STB-GH) of Kipjack Tuna Bone  
Bones of kipjack tuna were prepared following a previously established protocol [3]. Briefly, frozen bones were unfrozen and processed into small pieces (1-2 cm). Then, bone debris were mixed with 0.1 M NaOH at a bone/solution ratio of 1:10 (\(w/v\)) for 6 h, and the solution was replaced every 2 h to remove non-collagenous proteins. The alkaline-bone pieces were washed with tap water at a ratio of 1:20 (\(w/v\)) for 3 times. After that, the bone pieces were demineralized with 0.5 M EDTA-2 Na (pH 7.4) at a ratio of 1:10 (\(w/v\)) for 12 h, and the solution was changed every 12 h. Then, the demineralized-bone pieces were washed with tap water at a ratio of 1:20 (\(w/v\)) for 10 min and the washing was performed 3 times. The demineralized-bone pieces were then soaked in 0.2 M acetic acid solution with a bone/solution ratio of 1:10 (\(w/v\)) for 24 h, and acidic solution was changed every 12 h to swell the collagenous material in the bone. Acid treated bone pieces were washed with tap water until the wash water became neutral and finally washed with distilled water (DW) to remove other residues.  
The gelatin was extracted from pretreated bone pieces of kipjack tuna using the method described by [PERSON] et al. with a slight modification [19]. The gelatin extraction was carried out in DW at 60 \({}^{\circ}\)C for 8 h with a bone/water ratio of 1:10 (\(w/v\)). Finally, the extracting solution was filtered with filter paper, and the filtrate was centrifuged at 12,000 g for 15 min. The resulting supernatant, named as STB-G, was obtained and lyophilized. The yield of gelatin was calculated on the dry matter of freeze-dried collagens in comparison with the wet weight of bones used for extraction.  
STB-G hydrolyzed under in vitro gastrointestinal digestion (pepsin-trypsin system) was performed following the method of [PERSON] et al. [35]. Gelatin dispersions (pH 1.5, 1%, \(w/v\)) were hydrolyzed using pepsin at pH 1.5, 37.0 \({}^{\circ}\)C with a total enzyme dose of 1% (\(w/w\), 1 g enzyme/100 g gelatin). In 2 h, the mixture was neutralized with NaOH solution (1.0 M) and hydrolyzed using trypsin at pH 7.0, 37.0 \({}^{\circ}\)C with a total enzyme dose of 1% (\(w/w\), 1 g enzyme/100 g gelatin) for 2 h. After that, the gelatin hydrolysis was heated at 90 \({}^{\circ}\)C for 15 min to terminate trypsin digestion and centrifuged at 8000 g for 15 min at room temperature. The resulting supernatant, referred to as STB-GH, was collected, freeze-dried, and kept at \(-20\)\({}^{\circ}\)C for further analysis. The concentrations of STB-GH and its fractions were expressed as mg protein/mL and measured by the dye binding method of Bradford with BSA as the standard protein.
## 6 Discussion  
In Section 3, we discussed the scalability of the proposed scheme. The precondition under which the proposed scheme could still identify PS-QAM would require that the relevant statistical features of the PS-QAM to be identified remain constant. If the information entropy changes, the probability distribution features of PS-QAM will also change. As such, when PS-QAM with different information entropy needs to be identified, corresponding modifications to the procedure of the proposed MFI scheme and their relevant thresholds must be made. The proposed scheme was suitable for application scenarios wherein the set of the modulation formats that need to be identified from are made known before applying the MFI scheme. The authors would like to highlight the fact that such a condition also holds for machine learning MFI algorithms, since they also need training samples to contain the modulation formats to be identified in their training process. In other words, if the modulation formats of an incoming signal are not known before the training process  
Figure 18: The complexity comparison between the proposed scheme and schemes using DNN, PCASP and M-PSO.  
is applied, the machine learning algorithm will also fail to work properly. Furthermore, the proposed scheme cannot identify higher-order modulation formats, such as 128 QAM and 256 QAM, because of its low tolerance to noise. Under the influence of noise, the corresponding amplitude features of these higher-order modulation formats will not be highlighted in the histogram, resulting in increased difficulty of identification. Future work will determine methods by which to identify the higher-order modulation formats.
## 4 Conclusions  
Simultaneous validity analysis showed that the highest ICCs for hip flexion, knee flexion, and trunk rotation were 0.938-0.999, 0.763-0.998 or higher, and 0.751-0.995, respectively. This shows a high level of reliability for this novel marker-less motion-analysis system compared with a marker-attached motion-analysis system with traditional reliability. We found that this novel marker-less motion-analysis system enables very accurate joint measurement and monitoring even when simultaneously performing combined motions with multiple planes of motion. Therefore, this system can detect damage to joint movement as an objective indicator in rehabilitation and sports, which requires precise measurements of the motion and movement of the lower extremities. Furthermore, it has accuracy and reliability for monitoring the performance of real-world interventions. Comparing and analyzing the reliability and validity of automated evaluation using a multi-view image sensor-based motion-analysis system (4 DEYE) is expected to be of great help in evaluating the most difficult field of rehabilitation. Therefore, a musculoskeletal evaluation system with high evidence-based clinical practice guideline knowledge based on future clinical research should be established, and a reference value for judgment should be secured via clinical tests on actual subjects. It is expected that bad posture and movement patterns that mainly appear in patients and workers, including the general public, can be used to analyze the triggers associated with each pattern (lack of muscle strength, lack of joint motion range, etc.), derive data on \"muscle skeletal disease knowledge\", and provide hope to establish a life-cycle musculoskeletal health examination database for all ages.  
Conceptualization, J.A. and H.C.; methodology, S.W.K.; software, H.C.; validation, J.A.; formal analysis, H.L.; investigation, J.A.; resources, H.L.; data curation, J.A.; writing--original draft preparation, J.A.; writing--review and editing, J.A. and H.C.; visualization, H.C.; supervision, J.L. and H.-D.K.; project administration, J.A.; funding acquisition, J.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The experimental protocol followed the Declaration of Helsinki and was approved by the Institutional Review Board of a Korea University (KUIRB-2022-0260-01, 31 July 2023) prior to implementation.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study, and written informed consent was obtained from the patients to publish this paper.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
# Multi platform Computer Vision System to Support Physical Fitness Assessments in Schoolchildren
## 2 Materials and Methods
### Multiplatform Implementation
#### 2.3.3 Data Preparation  
This section defines the requirements and procedures for preparing and collecting data in laboratory and field environments for the four physical fitness tests. The developed software allows its maximum level of precision considering that both the field and laboratory tests are carried out in a controlled environment, where when carrying out the tests, certain aspects are taken care of, such as visual noise, which for our context refers to objects and situations that may confuse the software in detecting bodies and evaluating evidence. The student to be assessed must not wear accessories or clothing that generates visual obstruction of joints. Ideally, the student should wear a sports uniform composed of a polo shirt with short sleeves, without extravagant prints, shorts, and sports shoes, preferably using opaque or gray colors. The uniform should not contain reflective colors nor be composed entirely of pure white or black to improve the accuracy of joint detection. Testing in the laboratory environment was conducted with the Azure Kinect DK, positioned at 1.6 m from the students. The test is shown to the students, who then proceed to do the tests. For the biceps curl test, students are given a dumbbell between 6 kg and 4 kg, depending on whether they are male or female. In thirty seconds, they perform as many bicep curls as possible with their left arm. The elbow angle must be less than 45 degrees for a curl to count. We count the repetitions with the system implemented through Azure Kinect, visualizing the data in Unity software. Finally, we capture the maximum flexion and extension angle obtained while performing the test. Figure 1 shows body tracking in Unity while running the biceps curl test. Testing in the field environment was carried out with the two Canon EOS M50 Mark II cameras that were used to capture videos with their respective tripods and a specific distribution. The student was positioned at 2.5 m in front of the camera; this distance was selected based on preliminary research and considered appropriate through our experimentation, which is an ideal distance for recognizing bodies and joints [18]. To record the biceps, flexibility, and abdominal tests, the student must position himself in front of the camera, in the center of the focal angle. In the case of biceps, the student looks at the camera, that is, in a parallel line of vision. In contrast, in the abdominal and flexibility test, the student positions himself in a line of sight perpendicular to the camera, regardless of whether he looks to the right or left. For the horizontal jump test, the student is positioned at the starting point at the extreme right or left of the focal angle, with a line of sight
## 4 Experimental Results and Discussion
### Comparison with State-of-the-Art  
To compare our proposed method with other state-of-the-art methods, we conducted an experiment on UP-3D dataset. Since our iterative optimization-based method often fails because of local minima, we used a reduced test set of 139 images selected by [PERSON] et al. [25] to limit the range for the global rotation of body shape model.  
We implemented two state-of-the-art methods for qualitative and quantitative comparisons. In the baseline method, the objective function is defined as the reprojection error of 2D joints only. These methods are described in more detail below:  
* **Reprojection error of 2D joints (RE) only** This method only minimizes the error between ground truth 2D joints and projected 2D joints to estimate the 3D pose.
* **RE + Laplacian regularization (LR)** Laplacian regularization is proposed by [PERSON] et al. [17] to prevent the vertices from moving too freely and potentially avoids mesh self-intersection in triangular mesh based 3D reconstruction. We employed this method in 3D pose estimation and the objective function is defined as the sum of reprojection error of 2D joints and the Laplacian regularization term.
* **RE + Sphere approximation (SA)** [PERSON] et al. [22] built a set of spheres as a coarse approximation to the body shape model and derived a differentiable penalty term via calculating the intersection between spheres. To implement this method, We designed a set of spheres to approximate the surface of human body, as is shown in Figure 10. The objective function is defined as the sum of reprojection error of 2D joints and the intersection between spheres.
* **RE + SPT** This is our proposed method whose objective function is defined as the sum of the reprojection error of 2D joints and the self-intersection penalty term proposed in this paper.