esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
###### Abstract  
Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios.  
autonomous navigation; dynamic environment; waypoint generation; real-world navigation Article
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
###### Abstract  
With the exponential growth of Cyber-Physical Systems (CPSs) technologies, the Internet of Things (IoT) infrastructure has evolved from built-in static infrastructure to a flexible structure applicable to various mobile environments. In this Internet of Mobile Things (IoMT) environment, each IoT device could operate simultaneously as a provider and consumer of information, and could provide new services through the exchange of such information. Named Data Networking (NDN), which could request data by content name rather than location (IP address), is suitable for such mobile IoT environments. However, in the current Named Data Networking (NDN) specification, producer mobility is one of the major problems in need of remedy. Previously proposed schemes for producer mobility use an anchor to hide the producer's movement from consumers. As a result, they require a special anchor node and a signaling procedure to track the current locations of contents. A few anchorless schemes have also been proposed, but they still require mobility signaling and all NDN routers on the signaling path must understand the meaning of the signaling. We therefore propose an anchorless producer mobility scheme for the NDN. This scheme uses a dual-connectivity strategy that can be expressed as a soft handover. Whenever a producer changes its NDN Access Router (NAR), the new mobility link service located on the mobile producer's old NDN face repairs the old link so that the connectivity with the pNAR can be maintained for a while. The old NDN face is removed after the new location information on the contents of the producer is disseminated over the NDN network by the Named-data Link State Routing Protocol (NLSR) routing protocol at the nNAR. The new mobility link service decouples connection and transaction to hide the collapse of the link. Therefore, the NDN's mobility procedure could be simplified as the handover is defined as transaction completion as opposed to a breakdown of links. The proposed scheme prevents the routing information from being abruptly outdated due to producer mobility. Our simulation results show seamless handover when the producer changes its default access router.  
Named Data Networking (NDN); Information-Centric Networking (ICN); mobility; producer mobility; Cyber-Physical System (CPS); Internet of Thing (IoT) 2020 2020 26 August 2020 27 August 2020
# Assessment of Energy Use and Photovoltaic Energy Potential in Saudi Arabian Governmental Schools
## 4 Results and Discussions
### PV Systems Analysis  
PV systems were studied considering the tariff generated from the systems, $0.0133 (\(\approx\)SAR 0.05) per kWh, and the tariff of importing from the grid, $0.0931 (\(\approx\)SAR 0.36) per kWh. The cost of the PV system components in 2023 was determined by obtaining quotes from local market sources. The average values of these quotes were used for economic calculations, and the result was $824.8 (\(\approx\)SAR 3101.4) per kW\({}_{\rm P}\). It is also assumed that the generated energy is consumed when needed, and the excess is exported to the grid. Several scenarios were studied: nearly zero-energy balance, zero bills at the end of the project life after 25 years, and increasing the price of exported electricity to the grid.
# Microbial Risk Assessment of Industrial Ice Cream Marketed in Italy
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Ice Cream Samples**} & \multirow{3}{*}{**Parameter**} & \multicolumn{4}{c}{**Number and Percentage of Samples**} \\ \cline{3-6}  & & **Satisfactory** & **Acceptable** & **Unsatisfactory** & **Potentially** \\  & & & & & **Harmful** \\ \hline \multirow{6}{*}{With inclusions} & Aerobic colonies & 114 (95.80\%) & 3 (2.52\%) & 2 (1.68\%) & – \\  & _Enterobacteriaceae_ & 89 (74.79\%) & 21 (17.65\%) & 9 (7.56\%) & – \\  & Coagulase-positive & & & & \\  & staphylococci & 113 (94.96\%) & 6 (5.04\%) & 0 & 0 \\  & _Listeria monocytogenes_ & 103 (85.55\%) & 13 (10.92\%) & – & 3 (2.52\%) \\  & _Salmonella_ spp. & 119 (100\%) & – & – & 0 \\ \hline \multirow{6}{*}{Without inclusions} & Aerobic colonies & 81 (100\%) & 0 & 0 & – \\  & _Enterobacteriaceae_ & 66 (81.48\%) & 15 (18.52\%) & 0 & – \\ \cline{1-1}  & Coagulase-positive & 79 (97.53\%) & 2 (2.47\%) & 0 & 0 \\ \cline{1-1}  & _Listeria monocytogenes_ & 81 (100\%) & – & – & 0 \\ \cline{1-1}  & _Salmonella_ spp. & 81 (100\%) & – & – & 0 \\ \hline \multirow{6}{*}{Total} & Aerobic colonies & 195 (97.50\%) & 3 (1.50\%) & 2 (1.00\%) & – \\  & _Enterobacteriaceae_ & 155 (77.5\%) & 36 (18.00\%) & 9 (4.50\%) & – \\ \cline{1-1}  & Coagulase-positive & 192 (96.00\%) & 8 (4.00\%) & 0 & 0 \\ \cline{1-1}  & _Listeria monocytogenes_ & 184 (92.00\%) & 13 (6.50\%) & – & 3 (1.50\%) \\ \cline{1-1}  & _Salmonella_ spp. & 200 (100\%) & – & – & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of the results of different microbiological analyses obtained for 200 samples of milk-based industrial ice cream based on different ranges of acceptability set for each microbiological parameter according to the guidelines provided by the Interdepartmental Center for Research and Documentation on Food Safety (Ce.I.R.S.A). Figure 1: Distributions of the results of different microbiological parameters obtained for 200 samples of industrial ice cream collected at different mass-market retailers. The results were arranged considering (**a**) all samples, (**b**) the samples with inclusions (toppings or decorations) and (**c**) samples without inclusions. _Enterobacteriaceae_ (see, in Tables S1 and S2, the samples with ID: 52, 56, 108, 182, 197) and in one sample with unsatisfactory values of aerobic colonies (see, in Tables S1 and S2, the sample with ID: 139). However, no substantial correlations were found between the microbiological parameters, as shown in Figure 2.  
MALDI-TOFF MS analysis associated with SARAMIS confirmed the biochemical identification of all isolated strains of \(L\). _monocytogenes_. Significantly higher aerobic colony loads were observed for the ice cream samples with inclusions than for those without (\(p<0.0001\), U = 3065), while no significant differences were observed between the two groups regarding _Enterobacteriaceae_ (\(p<0.2213\), U = 4460) and coagulase-positive staphylococci (\(p<0.3462\), U = 4694).
## 2 Cyber-Attack Situations in WDSs
### Attack Scenarios and Specifications  
For the BATADAL dataset, 6 attack scenarios were considered. The attack scenarios were limited to hydraulic problems, such as disruption of pump operations, tank overflow, or depletion. The attack scenarios are distributed across the whole of the simulation period, therefore a total of 383 time steps were set as \"under-attack\" conditions, whereas the other 12,555 data were denoted as \"non-attack\" conditions. The test dataset had a length of 2089 data with 407 time steps labeled as \"under-attack\" conditions.  
To apply the attack scenarios from the normal condition, the tank's water level was considered to be 0.25 \(m\) (T1 and T2), 0.75 \(m\) (T5), 1 \(m\) (T3, T4, and T7), 2 \(m\) (T6) as an initial condition; this individual simulation was used for showing the characteristic of the effect for cyber-physical attacks [28]. The specifications of each attack scenario are shown in Table 1, and Scenario 1, 2, 3, and 5 are set to perform repeated generation during the simulation period because the malfunction of the tank or pump can be often generated as they are connected to several sensors or actuators.  
* _Scenario 1:_ This scenario focuses on a direct attack on the components of WDSs, such as pumps, valves, and tanks. For example, a tank overflow may be caused by a direct attack on a pumping station through the activation of unscheduled pumps.
* _Scenario 2:_ This scenario is caused by a disturbance in the reading or transportation of data between the sensors and PLC. For instance, manipulated water level readings can lead to the depletion or low levels of water in the tanks. This problem may be due to the physical manipulation of the water level sensor or the miscommunication of information between the sensor and PLC.
* _Scenario 3:_ This scenario relates to the connection among PLCs. If Tank 1 is connected to PLC 1, and the information of Tank 1 (e.g., water level) is intercepted by the attack and is transmitted instead to PLC 2, the other tank connected to PLC 2 will either overflow or be depleted.
* _Scenario 4:_ This scenario is an attack to conceal the PLC connection problem (Scenario 3) and disrupt the information between SCADA and PLC. An example would be a situation wherein there is a problem in the identification of attacks due to the malfunction of communication links between the PLC and SCADA.  
Figure 1: Description of C-town WDSs (water distribution systems) (Note: T: tank, V: valve, PU: pump, PLC: programming logical controller).  
* _Scenario 5:_ This scenario focuses on the SCADA data transportation problem. This attack scenario entails the alteration of the packages being sent by SCADA to change the operations of a PLC it supervises. In particular, the communication link between SCADA and PLC is attacked, resulting in pump activation and tank overflow.
* _Scenario 6:_ This scenario contains random multiple combinations of attacks on PLCs.
# Nanomaterials in Bone Regeneration
## 3 Nanostructured Materials
### Titanium
described osseointegration of UFG titanium with a nano-patterned surface in the maxilla and mandible of miniature pigs. Histomorphometric and biomechanical torque out analysis revealed no significant differences between tested samples. High bone to implant contact values independent of the bone mineral density and type of bone were obtained [79]. In the study of [PERSON] et al., the degree of osseointegration among UFG titanium, coarse-grained titanium, and titanium alloy with sandblasted/acid-etched surfaces was evaluated. The implants of each group were embedded in the femur of New Zealand rabbits, and histological analysis and torque tests were conducted. Results showed suitable osseointegration between implant and bone for all three groups. The value of removal torque was slightly higher for UFG titanium [80]. There are also a large number of titanium treatments that do not change the structure of the bulk material but create a nanostructure at the interface between the material and the biological environment. Although these methods do not change the material properties, the benefits of nanostructures in contact with bone are retained [81; 82]. Micro arc oxidation is one of the treatment methods producing well-characterized titanium dioxide morphology on the surface of titanium implants. Surface morphology determines the behavior of bone cells to promote early osseointegration. According to [PERSON] et al., increasing the voltage of the electrodes in the range of 100-400 V has a beneficial effect on the adhesion, proliferation, and expression of ALP of fibroblast-like mouse bone cells [83]. Electrochemical anodization with the production of nanotubes with different diameters and thicknesses of the wall [84; 85; 86] and laser texturing with a femtosecond laser to create grooves with hundreds of nanometers resolution [87; 88; 89] are the most used technics of titanium surface nanostructuring.
# Hyperspectral Imaging Techniques for the Study, Conservation and Management of Rock Art
## 2 Materials and Methods
### Pre-Processing
#### 2.4.2 Geometric Correction  
A hyperspectral image is a conical projection of reality. The geometric correction process is carried out to eliminate errors or distortions that occur in the image, and to adapt the image to an established 3D reference system that allows it to be compared.  
The steps to follow in the geometric correction of an image are as follows:  
1. Select control points.
2. Calculate the transformation.
3. Resample the image.
4. Verify the process.  
Figure 3: Spectral signatures of lights at working distance. Vertical axis represents relative spectral power measured and horizontal axis the wavelength in nanometers where it was measured.  
In this case, the correction was made by projecting the hyperspectral image onto the 3D model of the panel de las manos obtained by photogrammetry, which was subsequently re-projected following an orthogonal plane (Figure 1). The hyperspectral imaging camera had certain limitations regarding the imaging range and resolution. The pixel size was set to 1 mm and each recording had approximately 1 m\({}^{2}\).
## 4 Results
Maximum absorption occurs in the range of 100 MHz--1 GHz, which is a critical window for biosafety, as the energy absorbed by the tissue is the maximum. SAR decreases significantly with the increasing distance from the source, emphasising the importance of maintaining an adequate distance from electromagnetic sources to reduce exposure. Having generated the cloud-based database of the signals from the SAR and temperature sensors by discriminating the acquisition  
Figure 11: Temperature variation (\(\Delta T\)) as a function of electromagnetic wave frequency (Hz) for a stationary subject at different distances from the source. variables, we proceeded with the implementation of the FFNN for the prediction of the processed signals. The results obtained are shown in Figure 13.  
The results of the FFNN demonstrate that the relationship between the actual values and the predicted values, shown in Figure 13a, exhibits a good correspondence. The model maintains a linear relationship, despite the limited deviations from the regression line. Some dispersions occur for higher SAR values, as the model might overestimate or underestimate the extreme values. The distribution of the residuals, shown in Figure 13b, on the other hand, demonstrates the absence of a systematic bias in the model. Most of the residuals fall within the range \([-2,1]\), which implies limited errors for most predictions. The implemented model shows high overall accuracy, highlighting a good predictive capability for SAR values. To strengthen this study's contribution, we incorporated a comparative analysis with the existing literature on SAR and temperature monitoring in indoor environments. Prior research has examined SAR distribution and thermal effects using computational models and empirical measurements [56; 57; 58]. Our study advances this by integrating real-time data acquisition with AI-based processing, improving exposure assessments. The obtained results align with the previous studies on electromagnetic absorption [59; 60], but provide novel insights into dynamic human interaction with EMFs.
# Molecular Hydrogen: The Postharvest Use in Fruits, Vegetables and the Floriculture Industry
###### Abstract  
Molecular hydrogen (H\({}_{2}\)) has been found to have significant effects in a range of organisms, from plants to humans. In the biomedical arena it has been found to have positive effects for neurodegenerative disease and even for treatment of COVID-19. In plants H\({}_{2}\) has been found to improve seed germination, foliar growth, and crops: effects being most pronounced under stress conditions. It has also been found that treatment with H\({}_{2}\) can improve the postharvest preservation of fruits, vegetables and flowers. Therefore, H\({}_{2}\)-based treatments may be useful for the storage and transport of food products. H\({}_{2}\) can be delivered in a range of manners, from the use of the gas to creating H\({}_{2}\)-enriched solutions, such as hydrogen-rich water (HRW) or hydrogen nanobubble water (HNW). The exact action of H\({}_{2}\) at a biochemical level has yet to be established. Despite this, H\({}_{2}\) appears to be safe. Treatments of food with H\({}_{2}\) would leave no harmful residues, and H\({}_{2}\) itself is safe to use, as exemplified by its biomedical use. With H\({}_{2}\) production and transport being developed for other industries, H\({}_{2}\) is likely to become cheaper and its use for postharvest maintenance of food may be beneficial to explore further.  
hydrogen-rich water; flowers; fruit; molecular hydrogen; postharvest; vegetables +
Footnote †: journal: Medical Biology  
Therefore, developing new methodologies to prevent or reduce food waste is of major importance, especially because the world population is estimated to reach approximately 10 billion by 2050, which will require an increase of at least 70% in food production [9]).  
There is good evidence that H\({}_{2}\) has potential uses in agriculture, as previously reviewed [10; 11; 12], with effects on seed germination, plant growth and crop yields. It has been suggested that H\({}_{2}\) treatments may be adopted as part of agricultural practices and has already been used in field trials with rice [13], for example.  
Here, the evidence that H\({}_{2}\) can be used for the prevention of spoilage of food products, and therefore in their storage and transportation, will be reviewed. Furthermore, the current aspects of the mechanisms of action will be briefly visited.
# Adaptive Optimal Control of UAV Brake Based on Parameter Estimation
## 3 Adaptive Optimal Control Design with Guaranteed Parameter Estimation
### Filter Design  
The traditional adaptive control derives the adaptive law through the gradient algorithm and least square method to minimize tracking error, but the robustness is poor. Based on the previous research, an auxiliary filter is used to extract the parameter estimation error and drive parameter updating to obtain a better-estimated response [36].  
To facilitate the design of the adaptive law, the first equation in (4) is considered:  
\[\dot{x}_{1}=\Theta^{T}\Psi(x_{1})+F(x_{2}) \tag{6}\]  
where \(\Theta\) is the unknown parameter vector, \(\Psi(x_{1})\) is a known nonlinear function matrix, and \(F(x_{2})\) is a known nonlinear. The filtered variables \(\chi_{f}\) and \(\Psi_{f}\), \(F_{f}\) of the dynamics \(x_{1}\) and \(\Psi\), \(F\) are defined as [22]  
Figure 1: Structure of the control system.  
\[\partial\ddot{\chi}_{f}+\dot{\chi}_{f}=\dot{x}_{1},\dot{\chi}_{f}(0)=0\] \[\partial\ddot{\Psi}_{f}+\ \Psi_{f}=\Psi,\ \Psi_{f}(0)=0\] \[\partial\ddot{F}_{f}+F_{f}=F,F_{f}(0)=0 \tag{7}\]  
where \(\partial\) is a filter constant. Then it is obtained from (6) and (7) that  
\[\ddot{\chi}_{f}(t)=\frac{\dot{x}_{1}(t)-\dot{\chi}_{f}(t)}{\partial}\]  
and  
\[\frac{\dot{x}_{1}(t)-\dot{\chi}_{f}(t)}{\partial}-F_{f}=\Theta^{T}\Psi(x_{1}) \tag{8}\]  
The matrix \(G\) and vector \(Q\) are defined as  
\[\left\{\begin{array}{l}\dot{G}=-lG+\Psi_{f}\Psi_{f}^{T},G(0)=0\\ \dot{Q}=-lQ+\Psi_{f}\Big{[}\frac{\dot{x}_{1}(t)-\dot{x}_{1f}(t)}{\partial}-F_ {f}\Big{]},Q(0)=0\end{array}\right. \tag{9}\]  
where \(l>0\) is a design parameter. The solution of (9) is derived as  
\[\left\{\begin{array}{l}G=\int_{0}^{t}e^{-l(t-r)}\Psi_{f}(r)\Psi_{f}^{T}(r)dr \\ Q=\int_{0}^{t}e^{-l(t-r)}\Psi_{f}(r)\Big{[}\frac{\dot{x}_{1}(t)-\dot{x}_{1f}( t)}{\partial}-F_{f}\Big{]}dr\end{array}\right. \tag{10}\]  
Then, another auxiliary variable \(H\) is defined as  
\[H=G\dot{\Theta}^{T}-Q \tag{11}\]  
where the estimation of the unknown parameter \(\Theta\) is \(\dot{\Theta}\). Substituting (8) into (10), one has  
\[H=G\dot{\Theta}^{T}-G\Theta^{T}=-G\dot{\Theta}^{T} \tag{12}\]  
where \(\dot{\Theta}=\Theta-\dot{\Theta}\) is defined as the estimation error.
## 9 Stokes Space Polarization and Field Recovery Principle
### Stokes Space Field Recovery  
As previously mentioned, the main aim of Stokes space field recovery is to offer an appropriate connection between the Jones space optical field and the Stokes space optical intensity in order to address the second-order nonlinear signal representation [13]. In this section, the digital subsystem dimension that comprises modulation and detection is defined in accordance with the bandwidth utilization of a band-limited receiver rather than the optical spectrum. Furthermore, the reference point for this analysis is the 1D subsystem with the IM-DD. Furthermore, it is remarkable that the SSB subsystem with complex modulation is commonly considered as 2D in the optical domain, at most; it recovers the B-baud complex signal with the B-Hz bandwidth. This is mainly owed to its heterodyne-based detection nature. Consequently, it can be considered as 1D in relation to the 1D IM-DD reference point. Moreover, a polarization division multiplexed COH system is a 4D-based scheme due to the fact that, with homodyne detection, a B-Hz dual-polarized Co-Rx can recover the maximum of a 2B-baud complex signal. This is equivalent to 4B-baud single-polarization. In addition, it should be noted that the Stokes vector receiver is capable of recovering any signal being modulated in the Stokes space. For instance, the Stokes vector elements can be modulated and subsequently detected in the Stokes space [11].  
Figure 23: Stokes space analog polarization identification procedure.