esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Jamming Observed on Sounding Rocket Flights from Northern Scandinavia +
It was only recently reported that there is now interference practically every day in northern Scandinavia [6]. The analysis of ADS-B data from aircraft flying over northern Scandinavia is less insightful than of data from the Baltic region further south due to the small number of flight routes and incomplete coverage by ADS-B receivers in this area. In the following, it is therefore assumed that the observed interference on the sounding rockets is caused by jamming even though there is no final evidence. To better understand the nature of the interference, the Novatel OEM719's ability to calculate the power spectrum of the R/F input was used for the first time on the MAIUS-2 mission in December 2023. The GNSS receiver can calculate the power spectrum from the R/F signals which have been converted down to the baseband, bandpass-filtered and sampled by the receiver's frontend. The power spectrum directly reveals the presence of additional signal power due to interference or jamming in the frequency bands relevant for GNSS navigation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mission** & **Lift-Off Time (UTC)** & **Launch Site** & **Jamming** & **PVT Interrupted?** \\ \hline MAPHEUS-14 & 27 February2024, 07:27:00 & Esrange & yes & yes \\ REXUS-32 & 12 March 2024, 05:15:00 & Esrange & yes & yes \\ REXUS-31 & 14 March 2024, 12:00:00 & Esrange & yes & yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._In Section 2, the C/N\({}_{0}\) ratios are exemplarily illustrated for the SOAR and MAIUS-2 payloads. Additionally, the power spectrum recorded on the MAIUS-2 mission is discussed. In Section 3, the location of the jammer is roughly estimated from the GPS observations on board the sounding rockets. In Section 4, some conclusions are given.
## 5 Results
### Application Domains and Tasks
\begin{table}
\begin{tabular}{l l l l} \hline
**Domain** & **Application/Task** & **Study Count** & **References** \\ \hline Domain agnostic & Supervised tasks & 23 & [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68] \\  & Image processing & 20 & [25; 69; 68; 69; 69; 68; 67] \\  & Decision support & 13 & [7; 7; 12; 23; 88; 90; 91; 92; 93; 94; 95; 96; 97] \\  & Recommender system & 4 & [98; 99; 100; 101] \\  & Anomaly detection & 1 & [102] \\  & Evaluation process & 1 & [103] \\  & Natural language processing & 1 & [104] \\  & Predictive maintenance & 1 & [105] \\  & Time series tweaking & 1 & [106] \\ \hline Healthcare & Decision support & 20 & [107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126] \\  & Risk prediction & 4 & [127; 128; 129; 130] \\  & Image processing & 3 & [131; 132; 133] \\  & Recommender system & 2 & [134; 135] \\  & Anomaly detection & 1 & [136] \\ \hline Industry & Predictive maintenance & 5 & [137; 138; 139; 140; 141] \\  & Business management & 3 & [142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159] \\  & Anomaly detection & 1 & [145] \\  & Modelling & 1 & [146] \\ \hline Transportation & Image processing & 4 & [147; 148; 149; 150] \\  & Assistance system & 2 & [151; 152] \\ \hline Academia & Evaluation & 3 & [153; 154; 155] \\  & Recommender system & 1 & [156] \\ \hline Entertainment & Recommender system & 3 & [157; 158; 159] \\ \hline Finance & Anomaly detection & 1 & [160] \\  & Business management & 1 & [161] \\  & Recommender system & 1 & [162] \\ \hline Judicial system & Decision support & 3 & [163; 164; 165] \\ \hline Genetics & Prediction & 2 & [166; 167] \\  & Modelling gene relationship & 1 & [168] \\ \hline Aviation & Automated manoeuvring & 1 & [169] \\  & Predictive maintenance & 1 & [170] \\ \hline Architecture & Recommender system & 1 & [171] \\ \hline Construction & Recommender system & 1 & [172] \\ \hline Culture & Recommender system & 1 & [173] \\ \hline Defence & Simulation & 1 & [174] \\ \hline Geology & Recommender system & 1 & [175] \\ \hline Network & Supervised tasks & 1 & [176] \\ \hline Security & Facial recognition & 1 & [177] \\ \hline Telecommunication & Goal-driven simulation & 1 & [178] \\ \hline \end{tabular}
\end{table} 
Table 4: List of references to selected articles published on the methods of XAI from different application domains for the corresponding tasks.
## 3 Results and Discussion
### Application to Real Animal Urine Samples  
Using the entire analytical USVA-DLLME-GC-IT-MS protocol previously developed (briefly resuming: 9 mL of simulated urine sample solution at pH 3 containing 5 \(\upmu\)L of I.S., 60 ng mL\({}^{-1}\), addition of 1 mL of acetone and of 250 \(\upmu\)L dichloromethane as extraction solvent, three times of 1 min vortex and 2 min ultrasounds, centrifugation for 10 min at 4000 rpm, 1 \(\upmu\)L injection into GC-IT-MS), some animal urine samples have been analyzed, particularly three animal urine samples, i.e., two from goats and one from a sheep. All the subjects were healthy. No residues (i.e., levels below the LODs) were found in all the samples. The analysis allows us to investigate the presence of such compounds at trace levels in these matrices, but it does not furnish evidence as to whether there was a previous assumption of such molecules. As an example are shown in Figure 1, the gas chromatograms in SIM mode of a simulated sample of urine (a) and one of goat urine sample (b) both additions with 30 ng mL\({-1}\) of each NSAID. The peaks are well-solved and the determinations are precise and accurate.  
Figure 1: Gas chromatography/ion trap-mass spectrometry (GC-IT-MS) chromatograms in SIM mode of (**a**) simulated urine and (**b**) goat urine samples, both spiked with 30 ng mL\({}^{-1}\) of each NSAID. For experimental conditions, see text. Peak list: 1. acetylsalicylic acid; 2. ibuprofen; internal standard (IS); 3. naproxen; 4. ketoprofen.
## 4 Design and Control of SCIM-FESS
### Control Structure of SCIM-FESS
#### 4.1.2 Grid Side Converter Control  
A direct vector control scheme is used to control \(i_{d}\) and \(i_{q}\) in order to regulate the DC-link voltage during the charge, discharge and standby modes of the flywheel (Figure 4). Input variables to the controller are three phase currents (\(i_{abc}\)), three-phase voltages (\(v_{abc}\)), grid frequency (\(\omega\)) at the point of grid connection and DC-link voltage (\(V_{dc}\)). The reference input variables to the controller are reference DC-link voltage (\(V_{dc}^{*}\)) and d-axis reference current (\(i_{ds}^{*}\)), which is produced by a DC voltage regulator. The compensator term (\(\omega\lambda_{dqs}=\omega\lambda_{E}i_{dqs}\)) eliminates coupling between d-q axis components and also reduces the error in the reference voltage (\(v_{ds}^{*}\)) produced by P-I regulator in order to accurately produce command voltage for modulation and generation of gating signals for switching devices. Using reference voltages, (\(v_{ds}^{*}\) and \(v_{ds}^{*}\)) the controller produces the set point values of voltages in the stationary reference frame (\(v_{qg}^{*}\)) at the terminals of the IGBT voltage source converter to produce PWM switching pulses at the desired frequency. For simplification of control system d-axis component of two-phase system is oriented to the direction of grid voltage vector, therefore the grid side voltage (\(v_{s}\)) is equal to d-axis voltage (\(v_{d}\)) of two phase coordinate frame (\(v_{s}=v_{d}\)) and q-axis component is gained as \(v_{q}=0\)[76]. The angle for transforming three-phase currents (\(i_{abc}\)) into d-q reference frame is provided by phase locked loop (PLL) at grid frequency. The control strategy of the GSC is shown in Figure 4 and is governed by Equations (36) [77].  
\[\left.\begin{array}{c}L_{g}\frac{di_{ds}}{dt}=-R_{g}i_{ds}+\omega\lambda_{qs }+v_{s}-v_{ds}^{*}\\ L_{g}\frac{di_{qs}}{dt}=-R_{d}i_{qs}-\omega\lambda_{ds}-v_{qs}^{*}\end{array} \right\} \tag{36}\]  
where \(L_{g}\) and \(R_{g}\) are grid side inductance and resistance, \(v_{s}\) is grid side voltage. In this control strategy, a unity power factor is assumed, therefore, \(i_{q}=0\) and \(i_{d,ref}\) are generated by a PI controller by comparing the reference and measured dc voltage (\(V_{dc}\)).
# A New, Fast Pseudo-Random Pattern Generator for Advanced Logic Built-In Self-Test Structures
###### Abstract  
Digital cores that are currently incorporated into advanced Systems on Chip (SoC) frequently include Logic Built-In Self-Test (LBIST) modules with the Self-Test Using MISR/Parallel Shift Register Sequence Generator (STUMPS) architecture. Such a solution always comprises a Pseudo-Random Pattern Generator (PRPG), usually designed as a Linear Feedback Shift Register (LFSR) with a phase shifter attached to the register and arranged as a network of XOR gates. This study discloses an original and innovative structure of such a PRPG unit referred to as the DT-LFSR-TPG module that needs to phase shifter. The module is designed as a set of identical linear registers of the DT-LFSR type with the same primitive polynomial. Each register has a form of a ring made up exclusively of D and T flip-flops. This study is focused on the investigation of those parameters of DT-LFSR registers that are essential to use these registers as components of PRPG modules. The investigated parameters include phase shifts and the correlation between sequences of bits appearing at outputs of T flip-flops, implementation cost, and the maximum frequency of the register operation. It is demonstrated that PRPG modules of the DT-LFSR-TPG type enable much higher phase shifts and substantially higher operation frequencies as compared to competitive solutions. Such modules can also drive significantly more scan paths than other PRPGs described in reference studies and based on phase shifters. However, the cost of the foregoing advantages of DT-LFSR-TPG modules is the larger hardware overhead associated with the implementation of the solution proposed.  
logic built-in self-test; pseudo-random pattern generator; linear feedback shift register; ring register; STUMPS; T-type flip-flop
## 7 Conclusions  
The measurements for temperature distribution have been performed by a thermocouple. The temperature distribution has been reconstructed by the SMART algorithm and the results have been compared with those by the thermocouple giving the following summary.  
1. It can be seen that the CSLOS method has better performance than the SLOS method for initial value calculation. Therefore, a CSLOS method has been adopted to get initial values. It has been experimentally shown that this approach is appropriate for choosing the initial values for performing iterative calculations with the experimental absorption spectra of the CT-TDLAS.
2. In order to evaluate the performance of the adopted CT-TDLAS algorithm, three parameters, FWHM, SSD, and ZNCC, have been adopted, by which the adopted CT-TDLAS algorithm is capable of reconstructing the temperature distribution and the concentration distribution with relatively high accuracy.
3. To enhance of the performance of the temperature measurement using the CT-TDLAS, the SMART algorithm has been adopted, and the relative error was 10.12%. This implies that a SMART algorithm is a reliable approach for reconstructing the multiple signals of the CT-TDLAS.
4. The discrepancies between the temperature measured by the thermocouple and the temperature calculated by the CT-TDLAS were quite small at outside locations from the flame burner center. However, these discrepancies were relatively larger at the center region (high-temperature area) of the burner. It seems that the major part of the temperature discrepancies was due to the influence of the radiation flux.  
Writing--original draft preparation, M.-G.J.; writing--review and editing, D.-H.D., Y.D.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea Government (No.2017R1A2B2010603). Further, this has been also supported by the program of Business for Cooperative R&D (S2652733), Business Cooperated R&D Program (R0006261, RFP No. 17-102-006) of Korean Government, and the Ministry of Trade, Industry and Energy(MOTIE) and Korea Institute for Advancement of Technology(KIAT) through the National Innovation Cluster R&D program(P0006900).  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Price Impact  
According to [35], the price of batteries declines at a rate of around 11% per year. Therefore, this section will discuss the relationship between power source price and the economic cost coefficient.  
Figure 9 illustrates the variation in the total costs with an increase in years and the weighting factor. It can be observed that the total costs gradually decrease as the years increase. In the first three years, the minimum cost is reached when the weighting factor is 0.5. However, in the fourth year, after the battery price drops for the third time, the minimum cost occurs at a weighting factor of 0.6. This indicates that system durability is no longer the primary limiting factor. In the future, it will be crucial to consider the actual operating conditions and make specific price adjustments to optimize the economic cost coefficient, thereby minimizing the total system costs.
## 3 RRP Description
### Routing Establishment  
This phase constructs the initial routing paths for each sensor node in the network. The shortest hop paths are used, considering the minimization of delay and energy consumption. After the first phase, the BS is able to extract all the shortest hop paths from GlobalGraph (\(n\times n\)). With a panorama of the network, it then constructs and updates the routing table of each node.  
There are three steps in this phase: (1) uplink graph construction, (2) routing table construction, and (3) routing table update. The uplink graph, denoted by UplinkGraph (\(n\times n\)), contains all of the shortest hop paths from sensor nodes to the BS. When the uplink graph is obtained in the first step, the BS computes the routing table (RT) for each node using this graph. There are two fields included in a GS's RT: the next hop set and cost. The next hop set records the IDs of the next hop candidates for the uplink communication of the current node, and the cost is the hop count from the current node to the BS. After extracting the routing table of each sensor node in the uplink graph, the BS unicasts the computed RT to each node with the knowledge of the node's location in the third step for data transmission. Upon receipt, each sensor node stores the RT for communication.  
Figure 5: **(a)** Pseudo-code of virtual topology construction; **(b)** an example of network virtual topology, which results from the FSOSN in Figure 2 after the 1 st phase of RRP.  
**Algorithm 1** (**Algorithm 1**): The routing table of each GS in Figure 2 after running the 2 nd phase of RRP.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node** & **Next Hop Set** & **Cost** \\ \hline \(S_{2}\) & \(\{S_{I}\}\) & 2 \\ \(S_{4}\) & \(\{S_{2}\), \(S_{7}\}\) & 3 \\ \(S_{5}\) & \(\{S_{4}\), \(S_{6}\}\) & 4 \\ \(S_{6}\) & \(\{S_{7}\}\) & 3 \\ \(S_{7}\) & \(\{S_{3}\}\) & 2 \\ \(S_{8}\) & \(\{\mathrm{S_{9}}\}\) & 3 \\ \(\mathrm{S_{9}}\) & \(\{S_{I0}\}\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The routing table of each GS in Figure 2 after running the 2 nd phase of RRP.  
Figure 6: **(a)** Pseudo-code of routing establishment; **(b)** an example of uplink graph, which results from the FSOSN in Figure 2 after the 1 st phase of RRP.  
The pseudo-code of this phase is shown in Figure 6(a). The uplink graph construction is similar to the level first search (LFS) algorithm. Two queues, _P_Queue_ and _H_Queue_, are used in pairs. _P_Queue_ is for queuing the sensor nodes from low level to high level, and _H_Queue_ is the hop count between the corresponding sensor node in _P_Queue_ and the BS. Note that CHs are in the lowest level, because it takes them only one hop to get to the BS. The BS maintains a _Hop_ value, which is initially set to 0, for each node. This _Hop_ value records the smallest number of hops from the sensor node to the BS. Figure 6(b) gives an example of the uplink graph, which results from the FSOSN presented in Figure 2 after running this routing establishment phase. Table 1 outlines the results of the RT of each GS.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 2 Materials and Methods
### Experimental Demonstrations  
Experimental activities were conducted to explore with the help of a cold atom gravimeter and a ground electrostatic accelerometer prototype the concept of space acceleration measurements with a hybrid instrument. The main objectives were here more particularly to perform some preliminary experimental demonstrations of hybridization and to address experimentally the satellite rotation issue, which would induce a complete loss of contrast of the atomic signal.  
Hybrid Lab Prototype  
To perform the experimental tests of hybridization, a cold atom gravimeter, described in detail in [68], was used. For the electrostatic technology, a dedicated ground prototype was assembled by reusing parts of previously developed space accelerometers. The EA was mounted just below the atomic gravimeter, replacing the Raman mirror in a standard configuration (see Figure 7). Both instruments were mounted on a passive vibration isolation system (see Figure 8), allowing tests to be performed in high- or low-vibration conditions (10\({}^{-6}\) m\(\cdot\)s\({}^{-2}\) < \(\sigma_{vib}\) < 10\({}^{-2}\) m\(\cdot\)s\({}^{-2}\), where \(\sigma_{vib}\) is the standard deviation related to ground vibrations).  
In all the experiments having demonstrated the coupling of an atomic sensor with a classic one, the Raman mirror, acting as the reference for the atomic measurement, was fixed rigidly to the classic accelerometer housing, reducing the separation between the two measuring points. Here, the association of the atom accelerometer with the electrostatic sensor allows using the proof-mass of the latter as the Raman reference mirror. To our knowledge, no experiment has reported hybridization by directly linking the atoms and the proof-mass through the Raman laser.  
As can be seen in Figure 8, the whole setup was put on an excitation platform actuated by three piezoelectric actuators, allowing vertical translation and rotation around the two horizontal axes.  
Figure 7: Scheme of the hybrid lab prototype comprising a cold atom gravimeter and an electrostatic accelerometer. In this configuration, the proof-mass of the EA acts as the retro-reflecting Raman mirror, the reference mirror for the CAI measurement.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Holographic Metasurfaces
#### 3.3.5 Feed Mechanism  
Efficient feed mechanisms are at the heart of the working of holographic metasurfaces. RHS (refs. [74; 75]) replace conventional phase-shifting methods with the amplitude control of radiation elements to enable precise and dynamic beamforming. Extensive simulations and experimental prototypes have validated a novel amplitude-controlled beamforming algorithm, showing real-time, high-definition video transmission (ref. [74]). Similarly, RRSs (ref. [79]) can provide an energy-efficient feed mechanism due to reduced reliance on power-hungry phase shifters. These mechanisms indicate that novel feed design plays a key role in achieving high performance and efficiency for 6G networks.
###### Abstract  
Water pollution and scarcity are serious concerns for the growing world population. To meet the ever-pressing demand of fresh water, a variety of desalting techniques of seawater have been developed. Due to its environmental friendliness, high efficiency, easy regeneration of the electrodes, ambient operating pressure, and low operating potential suitable for the use in remote areas, the capacitive deionization (CDI) method is one of the most sustainable among them. This work focuses on the preparation of high-entropy oxides (HEOs) and carbon/HEO composites and the evaluation of their specific capacitance in view of their possible use as CDI electrode materials. CrMnFeCoNi-HEO, having spinel structure (sHEO), is obtained in the form of nanoparticles (NPs) and nanofibers (NFs) by the sol-gel method and electrospinning, respectively. Composite NFs with embedded sHEO NPs or MgCoNiCuZn-HEO NPs with rock-salt structure (rHEO) are also produced. In the 5-100 mV s\({}^{-1}\) scan rate range, the specific capacitance improves in the order C/rHEO NFs (8-32 F g\({}^{-1}\)) \(\cong\) sHEO NPs (9-32 F g\({}^{-1}\)) \(<\) sHEO NFs (8-43 F g\({}^{-1}\)) \(<\) C/sHEO NFs (12-66 F g\({}^{-1}\)). The highest capacitance is obtained when the beneficial contributions of the carbon matrix and smaller-sized HEO NPs are synergistically coupled.  
high-entropy oxides; carbon/high-entropy oxide composites; capacitive deionization +
Footnote †: journal: Applied Sciences
## 3 Case Study  
### Project Overview  
This section focuses on a specific arch dam, which was a concrete double-curvature arch dam consisting of 31 segments. The dam had a maximum height of 289 m, a crest arc length of 709 m, a crest thickness of 14 m, a maximum arch-end thickness of 83.91 m, and a maximum thickness of 95 m, including the expanded foundation. The total concrete volume was approximately 8 million m\({}^{3}\). The construction of the dam did not include longitudinal joints; instead, it employed a monolithic casting method with large concrete volumes and high construction continuity, which considerably increased the difficulty of temperature control during construction. Additionally, the dam is located in the subtropical monsoon region, where the average annual temperature is 21.95 \({}^{\circ}\)C. The average temperature in January, the coldest month, is 12.4 \({}^{\circ}\)C, while in July, the hottest month, the temperature reaches 26.5 \({}^{\circ}\)C, with extreme temperatures ranging from a maximum of 42.7 \({}^{\circ}\)C to a minimum of \(-\)0.3 \({}^{\circ}\)C. The climatic conditions are characterized by distinct wet and dry seasons, strong sunlight, dry winters, hot and rainy summers, and sharp temperature fluctuations, all of which further complicate concrete temperature management. Therefore, both the dam's specifications and the environmental conditions presented significant challenges for controlling the concrete temperature.  
For temperature monitoring, the dam employed distributed fiber optic temperature sensing (DTS) to collect real-time internal temperature data from typical dam segments and special locations, including segments 17 and 19 on the riverbed, segments 7 and 27 on the abutments, and segment 11 as a transition section.  
In this study, the fiber-optic temperature sensor was calibrated using constant-temperature water baths. The temperature resolution of the fiber-optic temperature measurement system is 0.05 \({}^{\circ}\)C, with a measurement accuracy of 0.1 \({}^{\circ}\)C and a spatial resolution of 1.02 m, ensuring reliable monitoring with high resolution across the entire measurement range [40].  
Figure 7 shows the layout of the fiber optics within the dam segments and the distribution of the DTS temperature monitoring units.  
Figure 6: Online modeling process. Y stands for Yes and N stands for No.  
Figure 8a illustrates the arrangement of the fiber optics on the surface of a block, and Figure 8b the cross-sectional view along line I-I.