esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### EPR Spectroscopy
The obtained results evidence a synergistic effect between daylight and US irradiation (Figure 7). As control, a sample containing a-TiO\({}_{2}\)-NH\({}_{2}\) NPs was analyzed after being exposed to visible light without any US stimulation and actually no ROS detection was observed (Figure 7 (1)). Thus, visible light alone is not sufficient to enable the ROS generation from a-TiO\({}_{2}\)-NH\({}_{2}\) NPs, while US irradiation and visible light seem to have a synergistic effect by increasing the generation of ROS with respect to US stimulation only (Figure 7 (2)). Figure 7: Influence of visible light and US stimulation on ROS production. **1. ** EPR spectra of a-TiO\({}_{2}\)-NH\({}_{2}\) NPs (200 \(\mu\)g/mL) in water using DMPO (10 mM) as spin trap, experiment performed at daylight without US irradiation; **2. ** EPR spectra of a-TiO\({}_{2}\)-NH\({}_{2}\) NPs (200 \(\mu\)g/mL) in water using DMPO (10 mM) as spin trap, irradiated with different US powers—0.9 W/cm\({}^{2}\) of US output power in: (**a**) daylight conditions and (**b**) dark conditions; and at 1.2 W/cm\({}^{2}\) of US output power in (**c**) daylight conditions and (**d**) dark conditions. Black spectra: control sample—water without NPs; red spectra: water with 200 \(\mu\)g/mL of a-TiO\({}_{2}\)-NH\({}_{2}\) NP.
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
### Previous Research  
In order to protect GNSS users, several studies have addressed the problem of the detection and localisation of interference. Experiments testing the localisation of jammers have been performed indoors in laboratories [22; 23; 24], and in some cases, also in real open-field conditions. The latter have been performed in maritime conditions in the United Kingdom [25] and in the northern part of Norway [26]; in-car jammers have been tested in Germany [27], South Africa [28] and Slovenia [29]; while studies of jamming in aviation have been performed in Switzerland [30]. Since the prohibition of the use of jammers, civilian research studies in real scenarios are now limited, restricted or even prohibited in most countries. However, several military exercises, which can also impact in-car or network receivers, have been or are due to be performed in 2020 in the United Kingdom [31]. Several algorithms have been introduced for in-car GNSS jammer localisations [32] (see, for example, [PERSON] et al. [33]). The possible effect of terrain on the jamming signal propagation was tested in van Niekerk and Combrinck [28]. The main finding of the study was that terrain characteristics can mitigate the effects of a ground-based jammer. A localisation method in urban canyons for in-car jammers, which work on monitoring networks and follow the principle of pattern recognition, is described in [PERSON] et al. [34]. In Kuusniemi and Aires [24], the effect of single-frequency jammers on consumer grade receivers was analysed. The goal was to estimate the availability and positioning quality of the solution. In the severe jamming scenario, an enormous amount (75%) of availability of positioning was enabled, which led to the quality of positioning of the remaining amount being severely affected.  
In an extensive study of the performance of GPS in maritime environments under jamming conditions in the North Sea in 2014 [26], the Leica GS10 geodetic receiver and the Leica AS10 antenna were included. An important finding of this study was that by using an L1/E1 jammer, GLONASS signals remained resistant to jamming. Based on the authors' knowledge, no experiments have yet been conducted on the jamming of several geodetic GNSS receivers of different manufacturers, as well as generations that operate simultaneously in the same location. To the contrary, several studies have analysed the effects of various jammers, for example, the use of a single-frequency or dual-frequency jammers [24; 35].
## 2 Materials and Methods
### Scanning Electron Microscopy and Energy Dispersive X-ray Spectrometry Analysis  
Each specimen was treated with 18% EDTA (Sigma-Aldrich, St Louis, MO, USA) for 30 s to remove the smear layer from the prepared dentin specimen to confirm the efficacy of NCP pre-treatment in occluding the dentin tubules. The prepared specimens were divided into five experimental groups (\(n\) = 9): non-treated group (NT) after EDTA treatment, HA treatment group, APF treatment group, HA and APF treatment group, and a group treated with a mixture of HA and APF after NCP pre-treatment. In total, 0.1 g powdered HA was converted into gel form using 0.5 g CMC and distilled water for ease of handling. The treatment time for all experiments was 3 min. The samples were brushed with distilled water, followed by treatment with artificial saliva for 30 min and drying in a dry oven. To confirm the occlusion of the dentinal tubules, images were captured using a SUPRA 25 FE-SEM (ZEISS, Oberkochen, Germany). All dried dentin specimens were fixed on an aluminum plate coated with platinum using a vacuum, and scanning electron microscopy (SEM) was performed at an accelerated voltage of 10 kV. The photographed image was obtained by randomly selecting the area of the specimen at a magnification of 1000\(\times\). Closure of the dentinal tubules in each sample was evaluated by counting the number of exposed dentin tubules, and the average value was determined. Energy dispersive X-ray spectrometry (EDS) (Supra 25 VP, Carl Zeiss, Germany) was performed to quantify the elements formed on the dentin specimens. Area scan component analysis was performed using EDS at 15 kV and 200\(\times\), and the content of Ca and P was measured by designating an area of the same size by dividing the dentin specimen into the upper, lower, left, and right quadrants [31]. The measured Ca and P values were used to calculate the Ca/P ratio and displayed on a graph.
## 4 Semantic Management Component
### Import Data Model Service  
To enrich the output results of the getMappingSuggestion service, models built based on data generated by machines and sensors can be imported for further processing and analysis. The format for importing models is comma-separated values (CSV), which presumes data storage in a tabular way. It offers a simple way for data exchange in a tabular form. From the name itself, the data are represented in rows and columns, where the first row usually represents a set of headers that determines the meaning of the data in each column. This format can be adopted to exchange the data among the applications using the proprietary format. One of the main advantages of this format is its  
Figure 5: The output of the getMappingSuggestion service.  
simplicity. The standard which specifies CSV is RFC 4180 [48], although separation can be made using, for instance, semicolons instead of commas.  
An example of a CSV file can be a document containing data on the functioning of a machine or its parts. For instance, data about the functioning of cylinders of a tabber stringent machine may contain headers such as the current time stamp, number of cylinders, movement types, and maximal and minimal speeds. CSV files are imported into Neo4j by loading the headers first followed by all other records regarding the corresponding header. If the value in the record is complex, in other words, it consists of several parts, it can be separated and parts can be inserted independently. This service is just an auxiliary or supporting service that can enable data in CSV format to be loaded, thus enriching the knowledge base with the new data. In principle, this service treats the imported data as graph entities to apply the getMappingSuggestion service or other services that can be added on demand.
# An Intelligent Parking Management System for Urban Areas
## 3 Description of the System
### System Architecture
#### 3.1.3 Data Centre  
The data centre will receive information transmitted by the gateway and from the sensor nodes of the network, giving readings which indicate the passage of vehicles. It will also send the relevant instructions to the sensor network and store system data.  
A server component will be installed to read and send data to the wireless sensor network while, for storing system information, a registration component will be used in the data centre.  
The server component for reading and sending data to the sensor network was developed as a web service, using Play Framework with Java technology. This framework is inspired by Ruby on Rails and Django, and enables flexible web applications to be built. It is a modular framework with integrated unit testing (it includes support for JUnit and Selenium) that allows static methods, non-blocking asynchronous communication, and simple corrective maintenance.  
The registration component consists of a database which will store information on registered users, the sensor networks deployed, the number of parking spaces available, _etc._ Specifically, we used PostgreSQL to set up the database. PostgreSQL functions include transactions, referential integrity, views and a multitude of features. It also incorporates Multiversion Currency Control--MVCC--which allows other clients to access it while a process is being written in a table without the need for locking. These features are suitable for systems that need to handle large amounts of data and a high number of concurrent users accessing the system simultaneously, as will be required by the proposed system.
## 2 Project Overview  
This study originated from the construction of the Kuitun River water diversion project, specifically the secondary hydropower station, the Xinlongkou Station. The water source was drawn from the upstream forebay of the station, and the pressure pipeline was designed in a one-pipe, four-machine configuration consisting of open pipe sections, vertical shafts, and branch pipe sections. The pressure water pipe was a single steel pipe, approximately 1450 m long with a diameter of 4.1 m, designed to divert a flow rate of 48.5 m\({}^{3}\)/s, with an internal flow velocity of 3.83 m/s, a design head of 342.304 m, and the design maximum water hammer pressure for this project is 4.2 MPa. Three branch pipes were arranged at the end of the pipeline, configured in a \"T\" shape, with branch diameters of 2.2 m (for large units) and 1.4 m (for small units). The branch pipelines were positioned parallel to each other on the same horizontal plane with valves installed at the end of each branch. The lengths of the branch pipelines X2, X1, D2, and D1 were 33.5, 29.5, 25.5, and 21 m, respectively. Reducer pipes were installed along the branches at distances of 18, 14, 10, and 5.5 m from the branch origins. The diameters of branch pipelines X1 and X2 were reduced to 1.1 m, whereas those of branch pipelines D1 and D2 were reduced to 1.9 m. The valve diameters matched those of the pipelines after the reductions. The actual layout of the onsite water diversion pipeline and the proposed model setup are illustrated in Figure 1, which includes the direction of water flow, upstream and downstream positions, and installation sequence of the various units in the hydropower station.  
Based on the actual pipeline layout, the longitudinal section of the pipeline (Figure 2) illustrates the elevation changes along the pipeline. Combined with Figure 1, it can be observed that this pipeline was arranged according to the gravitational drop, utilizing the gravitational potential energy to convert it into mechanical energy for electricity generation at the hydropower station.  
Figure 1: Three-dimensional section of the model.
## 2 System Model
### Proposed Hybrid RIS-UAV  
For the proposed hybrid RIS-UAV relaying scheme, the RIS mounted on the UAV consists of \(N\) discrete elements, including \(L\) active and \(N-L\) passive reflecting elements. Therefore, the hybrid RIS-UAV can be modified to become an active RIS-UAV or passive RIS-UAV by changing the number of active elements. For instance, the hybrid RIS-UAV becomes a passive RIS-UAV if \(L=0\), whereas it becomes an active RIS-UAV if \(L=N\). After being reflected by hybrid RIS-UAV, the received signal at the UE is given as [8; 26]:  
\[y_{hyb}=\sqrt{P_{bs}^{hyb}}\mathbf{h}_{rd}^{H}\mathbf{\Psi}_{hyb}\mathbf{h}_{sr }s+\sqrt{P_{bs}^{hyb}}\mathbf{h}_{rd}^{H}\mathbf{\Phi}_{hyb}\mathbf{h}_{sr}s+ \mathbf{h}_{rd}^{H}\mathbf{\Phi}_{hyb}\mathbf{n}_{r}+n, \tag{16}\]  
where \(P_{bs}^{hyb}\) represents the BS transmit power of the hybrid RIS-UAV relaying scheme. In addition, \(\mathbf{\Psi}_{hyb}\) and \(\mathbf{\Phi}_{hyb}\) are the reflection matrix of the passive and active RIS elements, respectively, where \(\mathbf{\Psi}_{hyb}=\text{diag}\{\Psi_{1},\dots,\Psi_{N}\}\) and \(\mathbf{\Phi}_{hyb}=\text{diag}\{\Phi_{1},\dots,\Phi_{N}\}\),  
\[\Psi_{n}=\begin{cases}0,&n\in\mathbb{L}\\ e^{i\varphi_{n}},&\text{otherwise}\end{cases} \tag{17}\]  
\[\Phi_{n}=\begin{cases}\rho_{n}e^{i\varphi_{n}},&n\in\mathbb{L}\\ 0,&\text{otherwise}\end{cases}. \tag{18}\]  
The notation \(\mathbb{L}\subset\{1,2,\cdots,N\}\) is the index set that denotes the positions of \(L\) active RIS elements. Similarly, \(\rho_{n}=\rho\), and it can be formulated as:  
\[\rho^{2}=\frac{P_{ris}^{hyb}}{L\Big{(}P_{bs}^{hyb}l_{sr}^{2}+\sigma_{r}^{2} \Big{)}}, \tag{19}\]  
where \(P_{ris}^{hyb}\) is the transmit power of the hybrid RIS-UAV,  
\[P_{ris}^{hyb}=P_{bs}^{hyb}\parallel\mathbf{\Phi}_{hyb}\mathbf{h}_{sr} \parallel^{2}+\sigma_{r}^{2}\parallel\mathbf{\Phi}_{hyb}\parallel^{2}. \tag{20}\]  
Furthermore, the SNR of the proposed hybrid RIS-UAV is expressed as:  
\[\gamma_{hyb}=\frac{P_{bs}^{hyb}\Big{(}|\ \mathbf{h}_{rd}^{H}\mathbf{\Psi}_{hyb} \mathbf{h}_{sr}\ |^{2}+|\ \mathbf{h}_{rd}^{H}\mathbf{\Phi}_{hyb}\mathbf{h}_{sr}\ |^{2}\Big{)}}{\sigma_{r}^{2}\parallel \mathbf{h}_{rd}^{H}\mathbf{\Phi}_{hyb}\parallel^{2}+\sigma^{2}}, \tag{21}\]  
and can be simplified as:  
\[\gamma_{hyb}=\frac{P_{bs}^{hyb}\big{(}(N-L)^{2}h_{sr}^{2}h_{rd}^{2}+\rho^{2}L^{ 2}h_{sr}^{2}h_{rd}^{2}\big{)}}{\rho^{2}\sigma_{r}^{2}lh_{rd}^{2}+\sigma^{2}}. \tag{22}\]
## 2 Materials and Methods
### Yield, Biometrics, and Economic Viability  
In phase BBCH 89 (mature pods) (Figure 2), the plants were harvested, and plant height, first pod height, pod count, seed yield, and 1000 seed weight were determined. Protein content and fat content were measured. Protein content was determined using the Kjedahl method by multiplying the percentage of nitrogen by a factor 6.25 [37], and fat content was determined using acid hydrolysis (Extraction System B-811, BUCHI Labortechnik AG, Flawil, Switzerland) [37].  
The economic effect of biostimulant application was calculated based on the value of the resulting yield increase and the cost of biostimulant application [38]. The value of the yield increase (EUR\(\cdot\)ha\({}^{-1}\)) was evaluated as a product of the average price of soybean seeds in a given study year and the difference between the seed yield from the combination with the biostimulant and the seed yield from the control combination. The costs of the treatment with the biostimulant (EUR\(\cdot\)ha\({}^{-1}\)) were computed as a sum cost of purchase of the extracts, cost of water used for the treatment, and cost of performing the treatment. The average price for the seeds was determined from market offers (363.64 EUR\(\cdot\)t\({}^{-1}\) in 2020 and 510.64 EUR\(\cdot\)t\({}^{-1}\) in 2021). The cost of Kelpak biostimulant (12.50 EUR\(\cdot\)L\({}^{-1}\) in 2020 and 12.77 EUR\(\cdot\)L\({}^{-1}\) in 2021) was taken as the average price from the 3 stores offering it. The cost of water was the average price of 1 m\({}^{3}\) (2.35 EUR\(\cdot\)m\({}^{-3}\)) in Lublin Province. The cost of the procedure was the average price of the plant spraying service (14.15 EUR\(\cdot\)ha\({}^{-1}\)). Revenue from cultivation was calculated as the product of the yield and the price of soybeans in a given research year. The change in revenue was calculated as the ratio of the revenue from the cultivation with biostimulant application to the revenue from the control.  
Figure 1: Nozzles tested: (**a**) AP12003; (**b**): 6 MS03C.
## 1 Introduction
In this paper, we propose an innovative non-parallel voice conversion framework that relies on an autoregressive model, a fusion of PPGs and speaker-embedding linguistic features, and an LPCNet vocoder for any-to-one voice conversion. This method allows us to transform the voice of an arbitrary speaker, including those who were not part of the training data, into the voice of a known speaker. Our approach focuses on improving the robustness of VC techniques in terms of speech quality, naturalness, and speaker similarity. In summary, the main contributions of this paper are as follows:  
* We propose a VC framework using an autoregressive conversion model to obtain acoustic features with higher precision, thereby generating a smooth trajectory and reducing speech error problems. * We use a high-fidelity LPCNet-based vocoder, which improves the efficiency of speech synthesis and can generate speech in real time. * We leverage the use of SI-PPGs, which exclude the attention-based duration conversion module. Additionally, we incorporate speaker embeddings obtained from the speaker encoder network as auxiliary features, which improves the overall training stability and minimizes pronunciation artifacts. * We evaluate the effectiveness of our system by performing \"any-to-one\" voice conversion pairs on the popular American CMU-ARCTIC database. Experiments on both objective and subjective evaluations showed that the proposed method outperforms state-of-the-art systems, demonstrating clearer pronunciation and greater speaker similarity. The remaining sections of this paper are organized as follows: Section 2 introduces the related work that has motivated our research. Section 3 describes the method we propose. Section 4 details the experimental setup. Section 5 presents the results and discussion. We conclude in Section 6.
## 3 Results
### Quantitative Analysis  
The survey results demonstrated strong participant engagement and generally positive perceptions of the serious games platform across multiple dimensions (Figure 4). Care-givers reported overall high satisfaction with platform usability, with 89% (\(n=166\)) rating it as \"easy\" or \"very easy\" to use (Likert score \(\geq\) 4). Importantly, 78% of respondents (\(n=145\)) noted that their children maintained high motivation to use the games throughout the two-week intervention period, suggesting good engagement potential for sustained rehabilitation.  
Game-specific analysis revealed notable variations in user ratings (Table 3). The racing-themed game Crazy Racing emerged as the clear favorite, receiving the highest scores for both enjoyment (4.6 \(\pm\) 0.3) and perceived therapeutic benefit (4.2 \(\pm\) 0.5). Qualitative comments associated these high ratings with the game's intuitive controls (\"He understood immediately how to steer by leaning\"), rewarding progression system, and immediate visual feedback. In contrast, Up-You Go received significantly lower ratings (enjoyment: 3.3 \(\pm\) 0.8; benefit: 3.0 \(\pm\) 0.9), with 18% of respondents (\(n=34\)) specifically citing technical issues like input lag as primary detractors.  
We then conducted a Spearman correlation analysis which showed a moderate positive correlation (\(p=0.51\), \(p<0.01\)) between game enjoyment and perceived therapeutic benefit. Feedback was analyzed per game; \"Crazy Racing\" and \"Hungry Woo\" were favorites, with high ratings in enjoyment and improvement in balance and motor function.  
Figure 4: Results of users’ experience on the use of serious games rehabilitation platform.  
\begin{table}
\begin{tabular}{c c c} \hline
**Serious Games** & **Enjoyment (Mean \(\pm\) SD)** & **Therapeutic Benefit (Mean \(\pm\) SD)** \\ \hline Crazy Racing & 4.6 \(\pm\) 0.3 & 4.2 \(\pm\) 0.5 \\ \hline Hungry Woo & 4.2 \(\pm\) 0.6 & 3.9 \(\pm\) 0.7 \\ \hline Super Surfer & 4.1 \(\pm\) 0.4 & 4.0 \(\pm\) 0.6 \\ \hline Hex & 4.0 \(\pm\) 0.5 & 3.8 \(\pm\) 0.8 \\ \hline Roxer & 3.8 \(\pm\) 0.7 & 3.7 \(\pm\) 0.8 \\ \hline Crusher & 3.7 \(\pm\) 0.7 & 3.6 \(\pm\) 0.7 \\ \hline Paddle Waddle & 3.5 \(\pm\) 0.8 & 3.2 \(\pm\) 0.9 \\ \hline Pongo Paddle & 3.4 \(\pm\) 0.9 & 3.1 \(\pm\) 0.8 \\ \hline Up-You Go & 3.3 \(\pm\) 0.8 & 3.0 \(\pm\) 0.9 \\ \hline \end{tabular}
\end{table}
Table 3: Serious game ratings for enjoyment and perceived therapeutic benefit.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Exploitable Protocols
#### 4.1.4 Selection of a Suitable Protocol  
At first glance, the underlying RLC layer has more capacity in the header than the PDCP. This is due to the fact that the RLC supports the segmentation of payload, and therefore more overhead is created by information necessary for the segmentation. However, these additional header fields probably cannot be used for a covert channel without affecting the correct assembly of the RLC segments. Otherwise, the RLC offers only two bits of suitable header space and is therefore less capable of hiding information compared to the header of PDCP. So finally, PDCP is selected as the primary protocol for the covert channel. However, it is also conceivable that the available capacities of the RLC header are used in addition to the PDCP. This combination of the PDCP and RLC would increase either the covert capacity or the robustness of the covert channel, since information can also be stored redundantly in the PDCP and RLC headers.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation
### Experimental Results and Analysis
#### 4.4.4 Experiments in the Simulation Environment  
We used the PyBullet simulation platform to build a grasping environment with the UR5 robotic arm, and selected 10 common objects in YCB [38] as the objects to be grasped. The grasping experiment was divided into two scenarios: a single-object grasping scenario and a multi-object grasping scenario, and the positions of the objects in each scenario were randomly generated, and the experimental process is shown in Figure 11. In each of the two scenarios, we performed 250 grasping tasks and counted the number of successful tasks.  
We conducted the same experiments with the GR-ConvNet algorithm, and the experimental results are shown in Table 4. In Table 4, the ODGNet has a higher grasping success rate than the GR-ConvNet in two grasping scenarios, and the grasping success rate in the two grasping scenarios are above 90%. The results show that ODGNet has excellent grasp detection performance even in unstructured scenes with multiple objects.  
Figure 11: Experimental process of single-object and multi-object grasping in the simulation environment.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline ODConv & \(\surd\) & & & \(\surd\) & \(\surd\) & & \(\surd\) \\ SA module & & & \(\surd\) & & \(\surd\) & & \(\surd\) \\ Primary feature fusion & & & & \(\surd\) & & \(\surd\) & \(\surd\) \\ \hline Accuracy (\%) & 95.3 & 98.1 & 97.9 & 96.4 & 98.2 & 97.8 & 98.1 & 98.4 \\ \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments on the Cornell dataset.  
The above experiments can show that the ODGNet can perform fast and accurate grasp detection for unknown objects. The experimental results with the Cornell dataset and multi-object dataset show that the ODConv in ODGNet enhances the ability of feature extraction, the GRFEF module enhances the ability of feature learning for the graspable region, and the primary feature fusion module enhances the ability to utilize multi-scale features. The ablation experiment proves that each module contributes to the ODGNet accordingly, and the simulation grasping experiment proves the effectiveness of the proposed method.