esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles
## 3 Methodology
### Data-Driven Application Scenarios
Multiple lateral acceleration masks  
\[\mathbf{b}_{a,i}=(\mathbf{a}_{y,ref}\geq r_{l,i}a_{y,smax})\ \wedge\ (\mathbf{a}_{y, ref}\leq r_{u,i}a_{y,smax}) \tag{5}\]  
are derived by checking whether the acceleration signal \(\mathbf{a}_{y,ref}\) lies within the stationary interval ranges \([r_{l,i},r_{u,i}]\). In the fifth step, we apply the connected components algorithm to pull-up short gaps between separated islands in the acceleration masks \(\mathbf{b}_{a,i}\) to get the updated masks \(\mathbf{b}_{a,i}\). According to the proposed amendment [17], the gaps may be up to 2 s if the additional condition  
\[\mathbf{b}_{add}=(\mathbf{a}_{y}\leq 1.4\cdot a_{y,smax})\ \wedge\ (\mathbf{a}_{y}<3.3\,\mathrm{m/s^{2}}) \tag{6}\]  
is fulfilled. Combining all masks with a logical AND-operator yields the resulting masks  
\[\mathbf{b}_{i}=\mathbf{b}_{v}\wedge\mathbf{\tilde{b}}_{a,i}\wedge\mathbf{b}_{ add}\ \ \forall\ \ i\in\{1,\dots,9\}\,. \tag{7}\]  
In the seventh step, we extract all events from the entire data set whose duration is larger than the threshold \(\Delta t_{min}\). Each event \(j\) is characterized by its start time step \(t_{s,ij}\) and its end time step \(t_{e,ij}\). We represent the scalar velocity parameter \(v_{x}\in\mathbb{R}\) as the mean value during the start and end time steps and the acceleration parameter \(a_{y,ref}\in\mathbb{R}\) as the center of the selected bin:  
\[v_{x,ij} =\frac{1}{t_{e,ij}-t_{s,ij}}\int_{t_{s,ij}}^{t_{e,ij}}v_{x}(t)dt\,, \tag{8}\] \[a_{y,ref,ij} =(r_{l,ij}+r_{u,ij})/2\cdot\mathrm{m/s^{2}}\,. \tag{9}\]  
After presenting the rule-based algorithm, we select a data set for the proof of concept. We take the same road network--with selected curve sections of the German roads A7 and B19--as prepared anyway according to the coverage-based algorithm of the following subsection. However, we let the virtual vehicle drive the route several times at arbitrary speeds instead of predefining them according to a special pattern. Figure 4 shows the resulting application scenarios. They are positioned in the center of the acceleration bins and their velocities are distributed contiguously. The distribution of points is random due to the velocities and will be discussed in the results section in more detail.
## 3 V2X System Architecture When Integrating Blockchain Technology and MEC
### Application of Blockchain Technology between Road Users and RSUs
#### 3.2.3 Initializing a Network Member  
When a device enters the range of the next RSU, the device automatically transmits its data (device ID). The participants, in turn, check the information and receive rating data; if the device is not found in the registry, the information is transmitted to the RSU for further decision, with the device being considered an intruder until the reasons are clarified. However, if the device is marked and initialized correctly, it is considered a full member of the network.
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 2 Methods
### HEO and QZO Satellite Observing Probability
\right) \tag{17}\]  
where \(R_{3}(-\Omega_{i})\) and \(R_{1}(-i_{orb})\) are rotation matrices as follows:  
\[R_{3}(-\Omega_{i})=\left( \begin{array}{ccc}\cos\Omega_{i}&-\sin\Omega_{i}&0 \\ \sin\Omega_{i}&\cos\Omega_{i}&0\\ 0&0&1\end{array} \right), \tag{18}\]  
\[R_{1}(-i_{orb})=\left( \begin{array}{ccc}1&0&0\\ 0&\cos i_{orb}&-\sin i_{orb}\\ 0&\sin i_{orb}&\cos i_{orb}\end{array} \right), \tag{19}\]  
where \(\Omega_{i}\) is the right ascension of ascending node which can be written as:  
\[\Omega_{i}=(\Omega_{0}-\Delta t\cdot\omega_{arth})\frac{\pi}{180^{\circ}}, \tag{20}\]  
where \(\Omega_{0}\) is the right ascending node at the initial epoch; \(\omega_{arth}\) is the angular velocity of the earth rotation. \(\Delta t\) can be obtained by Equation (15). Thus, all ECEF cartesian coordinates in one earth rotation period can be calculated and the set of these coordinates is defined as \(\Gamma\). As for point (3), the magnitude of satellite velocity in the ECEF system can be calculated using the Equations (8) and (9). Finally, the observing probability of a satellite at one point of \(\Gamma\) can be written as:  
\[P_{(X,Y,Z)}^{HEO/QZO}=\left\{ \begin{array}{l}\frac{k^{HEO/QZO}}{V_{(X,Y,Z)}^ {HEO/QZO}},(X,Y,Z)\in\Gamma\\ 0,\textit{otherwise positions}\end{array} \right., \tag{21}\]  
where the superscript \(HEO/QZO\) stands for HEO or QZO satellites; \(k^{HEO/QZO}\) is the corresponding probability constant for HEO or QZO satellites of large eccentricity orbits with the same ground track, which can be written as:  
\[k^{HEO/QZO}=\frac{n^{HEO/QZO}}{\sum\limits_{(X,Y,Z)\in\Gamma}\frac{1}{V_{(X,Y,Z)}^{HEO/QZO}}}, \tag{22}\]where \(n^{HEO/QZO}\) is the total number of HEO or QZO satellites with the same ground track; \(V^{HEO/QZO}_{(X,Y,Z)}\) is the velocity of HEO or QZO at one point of \(\Gamma\) in the ECEF system. Based on the mentioned above, observing the probability distribution of 3 QZO satellites can be seen from Figure 2. The eccentricity of QZO is set as 0.075, but the inclination and the center of the longitude of the ground track are the same as IGSO of BDS-3. The distribution shows that the probability is larger at the northern end of the track, which is very different from IGSO satellites, as a consequence of the velocity near apogee being lower.
## 6 Results and Discussion
### Latency Results  
Two tests have been performed to measure the latency effects in the GGA performance: The latency rate in a time interval is observed in the first experiment, and the relation between the latency and the number of processes used to run the algorithm is analyzed in the second test. A balanced processes assignation is applied in these tests: 50% of the processes is assigned in the UAEM cluster, and the remaining are assigned to the UPEMOR cluster.  
The test to analyze the latency rate in a time interval has been performed with several pairs of nodes selected in the two clusters sending packages of 64 bits. Figure 9 shows the average latency obtained throughout five days, from 9:00 to 16:00. A latency reduction is recorded as the day progresses: Higher latencies are reported at 10:00 (between 57 and 58 msec), and a latency value of less than 52 msec is observed at 15:00. This indicates that in the afternoon, the data transfer is more efficient. Since latency affects the GGA efficiency, it is recommended running the algorithm with it in low (in the afternoon).  
Figure 10 shows the latency behavior using a different number of processors assigned in the MiniGrid. It is observed that the latency decreases with increasing MiniGrid processes overload. This behavior is due to the fact that the bandwidth existing in the cluster communication is near to 30 Mbs, meaning that when there are a higher number of processes executed by GGA, more data are sent among clusters. A maximum of 15 Mb of data per second can be sent from UAEM cluster to UPEMOR cluster, and vice-versa, without saturating the microwave communication channel. It is crucial to conduct one proper process balancing to ensure the maximum efficiency of GGA. An overload tends to produce one inefficient running behavior on each cluster.
# Direct Selective Oxidation of Hydrogen Sulfide: Laboratory, Pilot and Industrial Tests
## 16 Zeolite Catalysts for Direct Oxidation of Hydrogen Sulfide  
Along with activated carbon, zeolites also can be used as adsorbents with catalytic properties for the oxidation reaction of hydrogen sulfide with molecular oxygen [102; 103]. For low concentrations of hydrogen sulfide, the activity of zeolites NaX, NaY, NaA decreases with time; however, after a certain time (stabilization time), the fall in activity is terminated. The stabilization time decreases with a decrease in the hydrogen sulfide concentration in the gas and does not depend on the temperature of the process. At temperatures below 300 \({}^{\circ}\)C, the degree of transformation of H\({}_{2}\)S does not depend on its initial concentration.  
The oxidation of hydrogen sulfide on various zeolites was detailed studied in detail by [PERSON] and co-authors. In [103], the reaction of the direct oxidation of hydrogen sulfide on the sodium form of faujasite was examined. As shown, when the oxygen pulse is supplied to the activated zeolite NaX, on which hydrogen sulfide was preadsorbed, the sample became intensely paramagnetic, and the electronic paramagnetic resonance method allowed the registration of a sulfur biradical -*S-(S)-S*. The measurement of catalytic activity showed that sodium faujasite is an effective catalytic system for direct catalytic oxidation of hydrogen sulfide to elementary sulfur, partially paramagnetic sulfur. The degree of hydrogen sulfide removal from the initial gas flow gradually drops and reaches a constant value during the reaction. In this case, the level of stationary activity is directly proportional to the temperature of the process to temperatures of about 150 \({}^{\circ}\)C; the further temperature rise leads to the intensive formation of an unwanted by-product-sulfur dioxide.  
[PERSON] and co-authors [104], studied oxides of transition metals supported on NaX zeolite as catalysts for the oxidation of hydrogen sulfide in gaseous products of coal gasification. Coal gasification gases can contain H\({}_{2}\), CO, H\({}_{2}\)S, CO\({}_{2}\), O\({}_{2}\), and H\({}_{2}\)O. The authors identified the influence of the nature of transition metal oxides on the catalyst activity and selectivity to sulfur formation.  
It was shown that the catalyst based on vanadium oxide showed the maximum activity (X\({}_{\rm H2S}\) = 70%) and selectivity (S = 80%). To characterize the composition of coal gasification gases [104; 105; 106] introduced the term \"Reducing Power\" [Equation (I)]:  
\[Reducing\ Power=\frac{[H_{2}S]+[CO]+[H_{2}]}{[O_{2}]+[H_{2}O]+[CO_{2}]} \tag{23}\]  
The strong dependence of the activity and selectivity of catalysts on the \"Reducing Power\" of coal gasification gases was established. In their work, the authors concluded that the vanadium oxide catalyst could be effectively used to remove H\({}_{2}\)S from gases of coal gasification.
## 2 Agri-Food Side Streams: Residues, By-Products, and Wastes
Additionally, the use of microorganisms that produce valuable chemical compounds can be a sustainable alternative to the energy-consuming production of biofuel and bioenergy. The circular bioeconomy and the \"zero-waste\" policies set by the EU propose the bioconversion of agri-food residues to useful, higher-value products [58; 59]. That is usually a multi-step process that includes the pretreatment of raw materials, hydrolysis of the polymers to readily metabolizable sugars, cultivation of microorganisms that will produce the wanted chemical compounds, and lastly, separation and isolation of the compounds [60]. Since fungi are well-known for their ability to degrade lignocellulosic material in nature [15; 61], they can be selected as bioconversion agents to avoid the hydrolysis step of the process. Fungal mycel are able to utilize the complex organic compounds of agro-industrial residues and produce useful bio-molecules that exhibit an amazing variety of functions, ranging from antibacterial antibiotics and anti-cancer agents to nutraceuticals, cosmetics, and industrial enzymes [62; 63]. Particularly, macrofungi are not only beneficial for human health but also very nutritious and tasty, and this is the reason for being examined to such a great extent [14; 17; 61].
# Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period
## 2 Pseudo-Periodic Extraction of Satellite Telemetry Data
### Symbolization Method of Satellite Telemetry Data
Currently, there are many different clustering methods, all of which have their suitable application background, and the methods differ from one another in computational efficiency and complexity. Based on the requirements for the rapid telemetry data anomaly detection and identification, clustering by the fast search and finding the density peaks (CFDP) method was adopted in this paper [25]. This method could quickly find the density peak point of any shape data set and take it as the clustering center to achieve an efficient distribution of the logarithm data points and eliminate outliers. Thus, the CFDP method is suitable for the clustering analysis of large-scale data. The method only needs to set the truncation distance, and an empirical method for parameter setting is given in [25]. The data symbolization was performed as follows: (1) the intervals of the tilt angles were searched, and (2) tilt angles into characters corresponding to the intervals were converted. Since the tilt angle could be divided into the basic form of upward, downward, and horizontal, the interval had a basic direction. Upon a directional change in the intervals, the sign should remain the same. Thus, data  
Figure 3: Comparison of the SP (salient points) and KP (key points) extracted data feature points.
## 4 Case Study: Application of APIRM Model at Istanbul Airport
### Risk Assessment of Hazards
In our study, the likelihood, frequency and possible consequence values used in the risk score calculation were obtained by a survey method from a group of six experts who supervised the construction, maintenance and repair of the pavement at Istanbul Airport. These values were assigned for the hazards (distresses) in Table 4, which are included in the APMS and pose distress to the pavements of Istanbul Airport. For each hazard, the most repeated value (maximum mode value) by experts was accepted. These values were determined in two categories: flexible runways and flexible taxiway pavements. Then the obtained risk scores were compared and are evaluated in Table 7.
# New Insights on Mechanical Stimulation of Mesenchymal Stem Cells for Cartilage Regeneration
## 4 Practical Applications
### Computational Approach  
The spreading use of bioreactors has offered the possibility to observe, in a feasible and controlled way, the process of differentiation of stem cells under mechanical stresses, leading to expanded understanding of the mechanisms underpinning the relationship between physical stimulation and biological responses. The outcome of these kinds of stimulation is strongly dependent on the design of the machine, the type of mechanical forces, their intensity and frequency, and the times of stimulation, not to mention the variables related to the scaffolds and the cells. Therefore, the results coming from different studies lead to high heterogeneity and difficulties in providing a uniform consensus about the best protocol to induce processes of differentiation. For this reason, computational models can help, since they have been suggested and used to further investigate tissue engineering strategies. These approaches could be employed to characterize the mechanical stresses imposed by the bioreactors, refine scaffold geometry, and analyze physiobiological dynamics and cell behavior [46]. In silico experiments complement in vitro and in vivo analysis, addressing some complex questions which can be difficult to answer through more traditional approaches. Theoretical assumptions, simplifications in the conceptual framework, and the need to operate consistent validations could limit their use. [PERSON] et al. [47] investigated the mechanisms of cartilage regeneration in osteochondral defects by using 3D medical imaging of the knee joint and analyzing the mechano-regulation processes underlying MSC differentiation. Two computational finite element (FE) models were employed to investigate the effects of physical stresses on cell regenerative mechanisms, as well as physiological processes like mitosis and cell death. The aim of the models was to provide predictions of the influence of different loading conditions on the whole system. The results indicated that simulation of a stance-phase gait cycle performed according to the ISO14,243-1 standard [48] induces more consistent cartilage regeneration than simulation of a vertical loading. This is because when the vertical loading was simulated, it was predicted that endochondral ossification would sustain bone development and that hydrostatic pressure would induce the formation of fibrocartilage.
# Solving the Time-Varying Inverse Kinematics Problem for the Da Vinci Surgical Robot
###### Abstract  
A dialog-elimination and Newton-iteration based quasi-analytic inverse kinematics approach is proposed for the 6 degree of freedom (DOF) active slave manipulator in the Da Vinci surgical robot and other similar systems. First, the transformation matrix-based inverse kinematics model is derived; then, its high-dimensional nonlinear equations are transformed to a high-order nonlinear equation with only one unknown variable by using the dialog elimination with a unitary matrix. Finally, the quasi-analytic solution is eventually obtained by the Newton iteration method. Simulations are conducted, and the result show that the proposed quasi-analytic approach has advantages in terms of accuracy (error \(<\) 0.00004 degree (or mm)), solution speed (\(<\)20 ms) and is barely affected by the singularity during intermediate calculations, which proves that the approach meets the real-time and high-accuracy requirements of master-slave mapping control for the Da Vinci surgical robots and other similar systems. In addition, the proposed approach can also serve as a design reference for other types of robotic arms that do not satisfy the Pieper principle.  
minimally invasive surgery robot; inverse kinematics; dialog elimination; Newton iteration +
Footnote †: journal: Applied Sciences
# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 3 Experimental Process and Result Analysis
### Data Processing and Model Setting  
First, it is vital to preprocess the data by normalization or standardization methods for data correlation analysis and network training because the magnitudes of the original data  
Figure 3: The East China Sea with the tracks of 12 typhoons from 1986 to 2016. The yellow line represents the track of Typhoon No. 1410 in 2014 and the white lines represent the tracks of the rest 11 typhoons. The time interval between contiguous dots is 6 hours.  
are usually different. Here the standardization method is utilized, and the formulas are as follows:  
\[x_{i}^{\prime}=\frac{(x_{i}-\overline{x})}{S_{d}}, \tag{10}\]  
\[S_{d}=\sqrt{\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})}^{2}}{n-1}} \tag{11}\]  
where \(x_{i}^{\prime}\) is standardized data, \(x_{i}\) is original data, \(\overline{x}\) is the mean of original data, and \(S_{d}\) is the standard deviation of original data.  
After standardization, the processed data which avoids the influence of abnormal and extreme values is good for ML training. The restore of the standardized data can be implemented according to the inverse function of the formulas above. The dataset was made in the way that the meteorological data and the water level data during the storm surge from the previous time period were selected as input, and the water level data of the next time step were selected as output. More detailly, the input data was divided into data slices by a sliding window of 24 h and then the 24-h data slice was used to predict the next 1 h, 3 h, 7 h, and 15 h of water level data respectively. The dataset was divided into a training set by 80% and a testing set by 20% and the number of the training cycle is 100. The testing set is used to train the LSTM model and the testing set is used for prediction.
## 3 Threat Detection System for the Security of Critical Infrastructures
### Case Study  
A demonstration of the INSTEAD framework in the DEXTER system, named Big City Trial (BCT), was hosted at the Rome metropolitan station Anagnina for four weeks in May 2022. This site represents the public and crowded spaces near critical infrastructures, such as transportation systems. As a part of the ATAC metro network of Roma, Anagnina station, it receives commuters every day, arriving from the southeast Lazio area (about 28,000 per day) to attend their work downtown. A portion of the corridor at the entrance of the station was devoted to the deployment of sensor and video devices in three regions: Init (start location observed by MIC and 2D Video), ReID (location for 2D Video reidentification), and Exit (location observed by EXTRAS and/or denoting the exit of the monitored area), as shown in Figure 1. Distances of the installed devices were established based on the performances of the individual detectors after lab tests together with the results from training and pre-integration testing of the INSTEAD key components operating at Anagnina in the previous months. This geometry can be easily replicated at different stations of ATAC or similar public locations.  
The devices were controlled by the software components deployed on server machines located in a dedicated room.  
The trial was performed through different scheduled scenarios, with groups of volunteers equipped with various types of objects under their jackets and/or invisible explosive traces over them. Furthermore, they walked through the corridor according to various patterns and speed. Other volunteers, playing the role of security guards, could recognize threat persons based on the image received on their smart glasses. The volunteers had given their informed consent to both sensor inspection and use of their images for the experimentation. To further enhance privacy, only face anonymized snippets were produced to minimize the processing of personal data. More details of the trials and the results of INSTEAD subsystem of DEXTER are provided in [7].
# ABORt: Acknowledgement-Based Opportunistic Routing Protocol for High Data Rate Multichannel WSNs
## 3 Acknowledgement-Based Opportunistic Routing Protocol  
We consider a large-scale multichannel WSNs for data collection applications, where all collected data are destined to one common node usually called the sink in the literature. Many-to-one data collection is very common in WSNs and usually known as convergecast in the literature. In high traffic load situations, all data will converge from leaf nodes towards the sink which causes high congestion in nodes close to the sink. The used routing policy will have an effect on the delivery ratio and end-to-end delay. The routing protocol must include a load balancing approach in order to avoid overloading some nodes while others are under-loaded.  
The aim of ABORt is to avoid congestion by balancing traffic load among under-loaded next hop nodes in the network. On the MAC layer, ABORt uses a multichannel protocol with semi-dynamic channel allocation like the one presented in [15]. On the network layer, the routing metric relies on the average packet queueing delay. Moreover, ABORt uses a multi-interface sink with three radio interfaces that can receive data at the same time on different channels. The sink node may have more or less than three radio interfaces depending on the traffic load of the network. In this paper, we decided to use three radio interfaces for the sink. All other nodes in the network operate with one radio interface. The following sub-sections describe in detail how ABORt operates.