esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Quantum Message Authentication with Single Qubit Unitary Operation
## 2 Quantum Message Authentication Protocol
### Preparation Phase  
In the preparation phase, [PERSON] and [PERSON] pre-share secret key sequences \(K_{AB}=\left(k_{AB}^{1},\ k_{AB}^{2},\ \ldots,\ k_{AB}^{N}\right)\) and \(K_{H}=\left(k_{H}^{1},\ k_{H}^{2},\ \ldots,\ k_{H}^{N}\right)\) that determine which single-qubit operation is chosen. The sequences \(K_{AB}=\left(k_{AB}^{1},\ k_{AB}^{2},\ \ldots,\ k_{AB}^{N}\right)\) and \(K_{H}=\left(k_{H}^{1},\ k_{H}^{2},\ \ldots,\ k_{H}^{N}\right)\) are a classical bit sequence with the size of \(2N\) and \(N\) respectively, where \(k_{AB}^{i}\in\left\{00,\ 01,\ 10,\ 11\right\}\), \(k_{H}^{i}\in\left\{0,\ 1\right\}\). The secret keys \(k_{AB}^{i}\) and \(k_{H}^{i}\) correspond to the Pauli operators \(\sigma_{k_{AB}^{i}}\in\left\{I,\ \sigma_{x},\ \sigma_{y},\ \sigma_{z}\right\}\) and the operator \(H^{i_{H}}\in\left\{H^{0}=I,\ H^{1}=H\right\}\). Here, operator is a linear combination of the Pauli operators \(\left\{I,\ \sigma_{x},\ \sigma_{y},\ \sigma_{z}\right\}\) and unitary operator \(H^{\dagger}H=HH^{\dagger}=I\).  
\[H=\left(I-i\sigma_{x}-i\sigma_{y}-i\sigma_{z}\right)/2 \tag{1}\]
# Nanomaterials for Healthcare Biosensing Applications
## 3 Applications of Inorganic Nanomaterials in Healthcare Biosensing
### Silver Nanoparticles
A detection  
Figure 9: Schematic representation of (**a**) Au-OmpW synthesis, (**b**) carboxylated CdTe conjugation with OmpW antibody, (**c**) CdTe-FRET immunoassay [192]. limit of 0.13 pg mL\({}^{-1}\) with a detection range of 0.01-80 ng mL\({}^{-1}\) was observed. Xia et al. reported a peptide-based aptasensor for human chorionic gonadotropin (HCG), a well-known biomarker for many kinds of cancer, using AgNPs as a redox reporter species [201]. The study converted an AgNP-based colorimetric assay into electrochemical analysis. The binding of HCG on the Au electrode induced the peptide probe to be deprived of its capability to trigger the on-site formation of AgNPs network architecture on the surface of the electrode, resulting in a highly attenuated LSV response. Consequently, an LOD of 0.4 mIU mL\({}^{-1}\) was reported. Yang et al. developed an ultrasensitive electrochemuluminescent biosensor for cholesterol monitoring by exploiting the exceptional catalytic activity of AgNP-BSA-MnO\({}_{2}\) nanosheets [202]. Cholesterol oxidase (ChsOx) was immobilised on the nanosheets. Efficient signal amplification in the luminol-H\({}_{2}\)O\({}_{2}\) CL system was observed and the linear detection response was obtained for cholesterol concentration between 0.21 and 1667 uM, with a detection limit of 0.07 uM. Another study reported a rapid, multifunctional, specific, and highly sensitive electrochemical platform to sense, eliminate, and inactivate _Staphylococcus aureus_ (_S. aureus_) using vancomycin (Van) immobilised silver nanoparticle-three-dimensional zinc oxide nanorod arrays AgNPs/3D-ZnO on a fluorine doped tin oxide electrode (FTO) [203]. The AgNPs were particularly chosen for their anti-microbial nature. The impedimetric sensor displayed a detection range of 10\({}^{3}\)-10\({}^{7}\) cfu mL\({}^{-1}\) with an LOD of 330 cfu mL\({}^{-1}\). The exceptional electrical conductivity possessed by inorganic materials like AgNPs makes them ideal candidates for biosensors relying on electrochemical detection, as listed in Table 4.
## 3 Guard Interval
#### Generation and Selection of GI  
The selection of a variable PN sequence as GI is one of the novel features of eOFDM, which inherits numerous benefits of the flexible proposed block structure; therefore, this section is specifically added to describe how the final subset of PN sequences is formed for the eOFDM transmission scheme. Initially, PN sequences are generated using the generator polynomial according to (4). It should be noted that (4) provides an option to use all possible polynomial equations of degree \(R\) for generating a very large set of PN sequences. This is achieved by setting either \(q_{i}=0,i\
eq R\) and/or any \(t_{i}=0\). Notably, with these two conditions, (4) becomes the most flexible equation for a polynomial of degree \(R\). It allows us to select a polynomial of degree \(R\) with any initial condition and any polynomial equation. Alternatively, we can say that the initial set of polynomials contains the PN sequences generated from all possible polynomials of degree \(R\) with all possible initial-state vectors. Hence, as a first step, all the possible PN sequences of degree \(R\) are generated to form a very large set of PN sequences. This initial large set is then scrutinized according to (6), and most of the PN sequences, which do not fulfill the cross-correlation condition of (6), are dropped. This results in a small subset of PN sequences that fulfill the conditions stated in (6). One of the PN sequence from this subset is then selected for an eOFDM block according to the data on scattered data subcarriers of set \(B^{\prime}_{0}\). The data symbols on these scattered data subcarriers are linked to the PN sequence of the eOFDM block, which is used as a GI; hence, the high detection probability of GI improves the detection probability of these scattered data symbols, which results in the improved BER performance of the system. Furthermore, these scattered data symbols can be used as pilot symbols in a way similar to CP-OFDM, which is an additional flexibility to be investigated for channel estimation as a future research problem for an eOFDM block structure.  
The procedure of generating a final subset of GI sequences is explained with the help of flow chart, as shown in Figure 4. As a first step, two empty sets, \(S\) and \(P\), are initialized, followed by the selection of an unused polynomial of degree \(R\) and the initial state vector. A set of possible PN sequences is generated, and set \(P\) is filled. This process is repeated until all the PN sequences of a given degree are generated. Each member of set \(P\) satisfying the condition stated in (6) is, then, copied to a subset \(S\). This subset \(S\) is used for eOFDM block formation.
## 5 Implementation
### ROH and WOH State-Machines and User Interfaces  
Figure 7a shows the OH interface, which can be used to generate different instances of the ROH and/or WOH. After registering an OH, a new UI will be automatically generated as shown in Figure 7b,d. A WOH UI and state machine are shown in Figure 7b,c respectively. After initializing a WOH, it goes automatically to the free-state. The WOH will move to the reserved-state when the SH finds an available and free cooperative IR to load the product order for him. After the IR loads at least one pump for the worker, the WOH will automatically move to the busy-state. The WOH will stay in the busy-state till a task done event is triggered from the WOH UI, therefore it will return back to the free-state. If the worker is temporary outside the workcell-for instance in a break-the WOH can be in non-available-state which is triggered by an away event. When a back event is triggered, the WOH returns back to the free-state. Finally if the worker is permanently outside the workcell-for instance for changing the work shift-the WOH can unregister from the workcell using an unregister event.  
An ROH is very similar to a WOH but simpler as it can be seen in Figure 7d,e. The only difference is that the ROH does not have a reserved-state. That is because the SH assigns a product order for one worker and one IR simultaneously in the same instance. Therefore, the IR moves directly to the busy-state to load the product order while the worker is in reserved-state. Then the IR returns back to the free-state.
## 3 Materials and Methods  
This study presents a novel approach for detecting water leakage in shield tunnels using 3D point cloud data. The proposed approach integrates intensity and geometric features with an XGBoost classifier to enhance detection accuracy and efficiency. As illustrated in Figure 1, the methodology comprises three core stages: data preprocessing, local feature generation, and tunnel leakage detection. The preprocessing stage begins with slicing the 3D point cloud data along the longitudinal axis of the tunnel. This slicing facilitates manageable data segmentation, aligning the dataset with the geometric structure of the tunnel. Noise in the point cloud data is then filtered using an enhanced RANSAC algorithm, which incorporates refined random point selection to improve precision of outlier removal. Following noise filtering, samples are selected for training and testing purposes. This step ensures that the dataset is properly balanced and representative of various tunnel leakage scenarios. Post-preprocessing, the framework focuses on extracting relevant features for classification. Geometric features are computed alongside smoothed intensity features, which are generated using a k-nearest neighbors (k-NN) algorithm. The optimal neighborhood scale for feature extraction is determined by analyzing the F1-score across varying k-values. Finally, the XGBoost classifier is employed to integrate these features for leakage detection. XGBoost was adopted for its robustness, scalability, and ability to handle both linear and non-linear relationships in multi-feature datasets. This model effectively differentiates between leakage and non-leakage points, achieving high detection accuracy while maintaining computational efficiency.  
Figure 1: The workflow of the proposed methodology.
# Navigation in GEO, HEO, and Lunar Trajectory Using Multi-GNSS Sidelobe Signals
## 3 Results
### Navigation Performance
#### 3.3.2 HEO Navigation Performance  
In this section, we focus on HEO navigation performance. The position strategies that applied to the HEO spacecraft were the same as those applied to the GEO spacecraft. The differences between BDS-3 SPP and nominal HEO are shown in Figure 17, and positioning RMSs are listed in Table 10. For the BDS-3/GPS combination, 3D position error decreased by 6.1% in contrast to BDS-3 only processing. Galileo and GLONASS increased SPP accuracy by 14.7% compared with dual-GNSS processing mode. The RMSs of code post-fit residuals were 3.73 m, 5.05 m, 4.8 m and 5.31 m for BDS-3, GPS, BDS-3/GPS and GREC combination, respectively. A total of 48.5% of the received signals were higher than 30 dB-Hz, which is 6.1% more than the GEO spacecraft. Therefore, lower code noises were derived. We can see that larger positioning error occurred near apogee; the trend of positioning error followed the trend of GDOP (Figure 13).
## 5 Conclusions  
In this study, a new method of combining high-resolution and field data was applied for the spatiotemporal mapping of certain surface water quality parameters, namely nitrate, dissolved oxygen and Chl-a in the Hassan Addakhill dam in southeastern Morocco. The field results show an excellent quality for most of the samples. In terms of the modeling approach, the models selected for the three parameters have shown a good correlation between the measured and estimated values with compliance index values of 0.62, 0.56 and 0.58 and root mean square error values of 0.16 mg/L, 0.65 mg/L and 0.07 ug/L for nitrate, dissolved oxygen and Chl-a, respectively. After the calibration, the validation and the selection of the models, the spatiotemporal variation of water quality was determined thanks to the multitemporal satellite data.  
In summary, this research represents an efficient and useful solution for the hydraulic basin agency in charge of water resources management in the region. Indeed, it will help to minimize the costs of quality surveys carried out throughout the year. It can also contribute to decision-making regarding agricultural profitability and its relation with water quality, as well as to the development of strategies for efficient water resources management.  
Conceptualization, A.E.O., A.L., M.E.H. and A.R.; data curation, A.E.O. and F.E.H.; formal analysis, A.E.O., M.E.H. and A.R.; methodology, A.E.O., M.E.H. and A.R.; project administration, A.L. and A.E.; resources, A.E.O.; software, A.E.O., M.E.H. and A.R.; supervision, A.L. and A.E.; validation, A.E.O., M.E.H. and A.R.; visualization, A.E.O., M.E.H., S.L., G.R., D.S.P., A.M. and A.R.; writing--original draft, A.E.O. and A.R.; writing--review and editing, A.E.O., M.E.H., A.R., A.E., A.L., S.L., G.R., D.S.P. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
The authors gratefully acknowledge the support of the staff of the Guir-Ziz-Rheris Hydraulic Basin Agency and Gaya Laboratory in Rabat for equipment and field mission assistance.  
The authors declare that they have no conflict of interest.
## 3 Method
### Deformable Model Matching
#### 3.2.1 Formulation  
The following demonstrates how to obtain object attribute parameters from keypoints including \(C\), \(S\), and \(R\). \(C\) indicates the object category, \(S\) is shape parameter vector defined in Equation (7), and \(R\) indicates a rotation matrix of object pose. \(P(R,S,C)\) indicates the probability distribution of object parameters. It is an optimal solution of target parameters by maximum the probability:  
\[\{R,S,C\}=\underset{R,S,C}{\text{max}}P(R,S,C) \tag{1}\]  
The deformable matching processing consists of model selection and model validation, which correspond to the prior probability and the conditional probability respectively:  
\[P(R,S,C)=P(C)\cdot P(R,S|C) \tag{2}\]  
\(P(C)\) is the probability of selecting the deformable model that is defined as  
\[P(C)\propto e^{-\lambda_{1}\cdot\underset{S}{\text{min}}(\underset{i}{\sum} \|\mathbf{M}_{i}^{C}(S)-\mathbf{X}_{i}\|^{2})} \tag{3}\]  
where \(\mathbf{M}\) indicates the node point coordinates of deformable wireframe model, which is defined in detail in Equations (6) and (7). \(\mathbf{X}\) represents 3D keypoint coordinates obtained by the network. \(\lambda_{1}\) is a constant. The probability of belonging to a certain class target is related to the distance between the 3D keypoints and deformable model point set.  
\(P(R,S|C)\) is the probability distribution of object parameters under the deformable model of a certain category, which is defined as  
Figure 3: Deformable model matching processing. Deformable model matching can be divided into two stages: model selection and model validation. The object parameters are obtained from the best fitting deformable model.  
\[P(R,S|C)\propto e^{-\lambda_{2}\cdot\sum\limits_{i}\left\|\mathbf{m}_{i}(S)- \mathbf{x}_{i}\right\|^{2}} \tag{4}\]  
where \(\mathbf{m}\) indicates the projection points of \(\mathbf{M}\) based on camera imaging model, and \(\mathbf{x}\) represents 2D keypoint coordinates obtained by the network. \(\lambda_{2}\) is a constant. Substituting Equations (2)-(4) into Equation (1), we have  
\[\{R,S,C\}=\max\limits_{R,S,C}e^{-\lambda_{1}\cdot\min\limits_{S}\left(\sum \limits_{i}\left\|\mathbf{M}_{i}^{C}(S)-\mathbf{x}_{i}\right\|^{2}\right)}\cdot e ^{-\lambda_{2}\cdot\sum\limits_{i}\left\|\mathbf{m}_{i}(S)-\mathbf{x}_{i} \right\|^{2}} \tag{5}\]  
It is worth mentioning that only the visible points are considered. Invisible points in \(\mathbf{M}\) and \(\mathbf{m}\) are discarded in Equations (3)-(5). In our method, the visibility of one node point in \(\mathbf{M}\) or \(\mathbf{m}\) is related to the distance with its nearest keypoint.
## 2 Theoretical Background and Related Work
### Related Work and Research Gaps  
Through the literature analysis, three significant research gaps have been identified in elderly technology product development:  
1. The current research lacks an integrated design framework. As [PERSON] et al. [5] pointed out, existing studies often focus on single aspects, such as interface design or functional development, lacking a framework that integrates human needs, interaction design, and technical implementation. This fragmented research approach struggles to effectively address the overall challenges of elderly technology use.
2. Design principles for intergenerational interaction lack systematic empirical research support. While [PERSON] et al. [15] and [PERSON] and [PERSON] [16] have affirmed the value of elderly-children co-play, there remains a lack of large-scale, long-term empirical research validating specific mechanisms for the design and implementation of intergenerational interaction. [PERSON] et al. [36] developed the intergenerational interactive game \"Age Invaders\", innovatively integrating a physical interactive space with virtual game elements to create a natural interactive environment for grandparents and grandchildren. According to [PERSON] et al.'s research analysis, elderly users show stronger gaming motivation when playing with their grandchildren. Additionally, physical interactive interfaces and puzzle-type game content help elderly users to interact with others in a joyful atmosphere while enhancing their social capabilities.
3. Effective long-term benefit evaluation mechanisms are lacking. Most existing research focuses on evaluating short-term usage effects, as seen in [PERSON] and [PERSON]'s [17] study, primarily concerning initial acceptance. However, systematic research on elderly technology products' sustained usage, long-term social impacts, and health promotion effects remains largely lacking.  
The identification of these research gaps through our systematic review of the existing literature not only emphasized the research questions proposed in the Introduction, but also highlighted the importance of establishing an integrated design framework. While previous studies have made valuable contributions to elderly technology design, our analysis reveals the need for a more comprehensive approach. Particularly in promoting intergenerational interaction and enhancing emotional connections, the synthesis of the existing research findings provides clear directions for future elderly technology product development and opens up new possibilities for advancement in this field.
## 4 Conclusions  
This study presents a new method of predicting the pyroshock response of a structure based on the hybrid FE-SEA method. The following conclusions can be achieved: Combined with the time-frequency domain transformation of the load shock signal and time history response reconstruction, the hybrid FE-SEA model is applied to the shock response prediction of the structure. This indicates that the numerical results based on the new method are in good agreement with the experimental results. The rationality and sufficient accuracy of the prediction results of the structural shock response based on the hybrid FE-SEA method is verified. The complex spacecraft structure is suitable to be modeled as a hybrid model. By means of this, we can better grasp the frequency response characteristics of the results. The reasonable finite element model of the structure with low modal density can be established. At the same time, the problems caused by units' division in the high frequency sections are avoided. Our new method can predict the middle- and high-frequency shock response more effectively and reasonably, and the computational efficiency is greatly improved, compared with the traditional finite element method. This paper provides a theoretical basis for predicting the shock response of the spacecraft structure by using more steady-state methods.  
Conceptualization, Y.S.; Data curation, X.W.; Investigation, W.L; Methodology, X.W.; Software, X.L.; Validation, X.W. and Y.S.; Writing--original draft, X.W.; Writing--review & editing, W.L. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Defense Science and Technology Enhancement Program of China.
# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
###### Abstract  
In Cone Beam Computed Tomography (CBCT) images, accurate tooth segmentation is crucial for oral health, providing essential guidance for dental procedures such as implant placement and difficult tooth extractions (impatations). However, due to the lack of a substantial amount of dental data and the complexity of tooth morphology in CBCT images, the task of tooth segmentation faces significant challenges. This may lead to issues such as overfitting and training instability in existing algorithms, resulting in poor model generalization. Ultimately, this may impact the accuracy of segmentation results and could even provide incorrect diagnostic and treatment information. In response to these challenges, we introduce PPA-SAM, an innovative dual-encoder segmentation network that merges the currently popular Segment Anything Model (SAM) with the 3D medical segmentation network, VNet. Through the use of adapters, we achieve parameter reuse and fine-tuning, enhancing the model's adaptability to specific CBCT datasets. Simultaneously, we utilize a three-layer convolutional network as both a discriminator and a generator for adversarial training. The PPA-SAM model seamlessly integrates the high-precision segmentation performance of convolutional networks with the outstanding generalization capabilities of SAM models, achieving more accurate and robust three-dimensional tooth segmentation in CBCT images. Evaluation of a small CBCT dataset demonstrates that PPA-SAM outperforms other networks in terms of accuracy and robustness, providing a reliable and efficient solution for three-dimensional tooth segmentation in CBCT images. This research has a positive impact on the management of dentofacial conditions from oral implantology to orthognathic surgery, offering dependable technological support for future oral diagnostics and treatment planning.  
Keywords:dual-encoder; 3D tooth segmentation; few-shot segmentation; CBCT; GANs +
Footnote †: journal: Applied Sciences
# A System for Controlling and Monitoring IoT Applications
## 4 Blocks 2 and 3  
In this work, the system is controlled and managed by the main server (Block 2). This system has to accommodate the website where different users can consult the state of their installations from a remote distance, through any device which supports a web browser (Block 3). The other main function is the management of users and data of the installations. For this reason, it is necessary to install a server, database (DB) management and programming language to develop the web dynamic content. In this case, the main server was implemented with the WAMP server (version for Windows of the Internet infrastructure system), which includes Apache as the server, MySQL as the DB management and PHP as the programming language. This infrastructure allows it to provide HTML pages through the Internet and also to manage data on these pages. At the same time, it provides programming languages such as PHP, Perl or Python to develop web applications.  
A relational DB is the most used model nowadays to implement planned DBs. These allow it to establish interconnections (relationships) between data (stored in tables), and through these connections it can link the data of both tables [16]. A unique DB was created with the name 'installations_management' composed of two related tables:  
* users_table: Personal data of the user and the server which corresponds to the installation.
* installations_table: RPi stores the sensor data after asking the Arduino more.  
To protect the information, an automatic backup of the database was implemented. This backup program (mysqIdump) permits the backup of the DB in the same server or the transfer of the DB to another MySQL server.