esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone
## 2 Related Work
For finger veins, the acquisition prototype described in [8] was composed of twelve 850-nm NIR LEDs arranged in three groups of four LEDs in a PCB to provide an adequate illumination. In addition, the power of each group of LEDs was adjusted by means of a software program. In this way, it is possible to provide very homogeneous lighting, avoiding saturation in the central point or insufficient lighting at the edges. Since the veins are deeper in the fingers, optimal illumination is needed. A CMOS camera was employed toacquire images from a minimum distance of 10 cm. The prototype also included a 740-nm high-pass filter to reduce the effect of ambient light. The ROI was extracted, and the images were enhanced. Recognition performance results have not been reported yet. For wrist veins, the UC3M-CV2 database [7] was acquired using the infrared cameras and NIR illumination of two Xiaomi smartphones. The acquisition considered several environmental conditions (outdoor/indoor and external artificial or natural light without direct sunlight at different daytimes). A software program to guide the wrist position was developed as part of the acquisition protocol and images were enhanced. From the evaluation of the recognition performance, the resulting EER values ranged from 6.82% to 18.72%. The acquisition of veins in the visible spectrum was preliminarily explored in [11,12] from images captured with ordinary smartphone cameras. In [11], veins are extracted and processed in the RGB domain and performance of the acquisition is evaluated for medical applications (e.g., vein puncture guidance). In [12], the identification capability of three kinds of neural networks processing the information obtained from the ROI is evaluated. The highest success identification rates range from 94.11% to 96.07% in the three structures.
# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 4 Performance Evaluation
### Absolute Execution Time vs. Number of Worker Nodes  
Figure 5(a) provides a direct representation of execution time as a function of the number of worker nodes, offering a straightforward comparison of how different dataset sizes respond to variations in cluster size. These results reinforce the findings from the logarithmic plot (Figure 5(b)), further emphasizing the significant reductions in execution time achieved by increasing the number of worker nodes, particularly for larger datasets. For example, with the 5045.5 MB dataset, the execution time decreases markedly as more worker nodes are added. These runtimes demonstrate the algorithm's capacity to distribute and process large workloads across a distributed cluster effectively.  
This significant reduction in execution time for larger datasets highlight the algorithm's ability to scale effectively within the Spark framework, making it potentially applicable to big data scenarios, where fast processing of large numbers of data is essential. In practical terms, the algorithm can be deployed in environments where data processing speed is critical, such as in real-time analytics, streaming data processing, and large-scale simulations. In addition, Figure 5(a) also highlights the diminishing returns associated with adding more worker nodes for smaller datasets. For datasets around 108.1 MB, the execution time reduction is less pronounced, indicating that the additional overhead of managing more nodes may outweigh the performance gains beyond a certain point. This insight is vital for optimizing resource allocation in distributed systems, where it is essential to balance the number of worker nodes with the dataset size to achieve the best possible performance. It suggests that a more conservative approach to node allocation may be more efficient for smaller workloads, avoiding unnecessary costs and complexity.  
Figure 6: Performance evaluation results of the EDC placement problem implemented with the Apache Spark framework: **(a)** execution time as a function of the number of worker nodes; **(b)** execution time as a function of the number of worker nodes on a logarithmic scale; **(c)** achieved speedup by increasing the number of worker nodes.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### DNA Encoding
The proposed algorithm consists of three encryption steps: permutation and update, DNA sequence operation, and diffusion. All three steps are plaintext image-related. The algorithm begins by generating a SHA-256 hash of the plaintext image and then stretches the plaintext image into a 1D sequence. The algorithm also generates three integer chaotic sequences (ICSs) from the 2D-LSCM based on a secret key to be used in each step. For the permutation and update step, the algorithm permutes and updates the pixel values using the first generated ICS and the hash value using an equation based on modular and discrete logarithmic operations to calculate the permutation coordinates. Next, in the DNA sequence operation step, the algorithm performs DNA encoding, DNA XOR, and then DNA decoding on the resulting permuted image, utilizing DNA rules selected based on the second generated ICS and the hash value. For the diffusion step, the algorithm changes the pixel values in forward or backward diffusion based on the secret key, hash value, the three ICSs, and the discrete logarithm operation. The three steps of the algorithm are performed twice, and the final 1D cipher sequence is converted into a 2D image to produce the cipher image. Image encryption algorithms based on DNA encoding can have high security and require less storage requirements. Yet the decryption process requires additional DNA rule tables to be transmitted along with the encrypted image [34]. Furthermore, DNA encoding-based encryption is still experimental and only explored in academic research; it requires specialized lab equipment to convert binary values into synthetic DNA sequences.
## 4 Bridge Degradation Identification and Model Update Results  
### Introduction to Design Parameters and Point Cloud Data of Hedong Bridge  
The main bridge of Hedong Bridge is a six-span (66 m + 4 \(\times\) 120 m + 66 m) prestressed concrete continuous rigid frame + continuous box girder, with a total length of 612 m; the approach bridge on the Huangwei side consists of a continuous precast concrete small box girder with eight spans of 30 m, and the Qianshan side consists of a continuous precast concrete small box girder with five spans of 30 m. Figure 12 illustrates the elevation and plan view of the bridge.  
The bridge point cloud data used in this study were obtained using a ground-based fixed three-dimensional laser scanner. The scanning process primarily utilized TOF (Time of Flight) pulse ranging, calculating the distance S from the scanner to the scanned point based on the time it takes for the pulsed laser to be emitted and reflected back. This method can measure distances up to several hundred meters, but due to lower accuracy at larger ranges, a phase laser scanner, which measures distance based on phase shift technology, was also used [47; 48; 49]. This scanner modulates the amplitude of the laser beam and measures the phase delay caused by the round trip of the modulated light along the scanning line. By converting this phase delay into distance S using the wavelength of the modulated light, high-precision point cloud data of the bridge's details were collected.
## Materials and Methods
### Statistics and Principal Component Analysis (PCA)  
The KFT water content and values for KFT kinetic parameters were provided as mean (\(\pm\)standard deviation, SD). Moreover, the results from KFT analysis and kinetics for bread core, pre-dried bread core, raw and pre-dried bread shell samples were compared for significant difference using [PERSON]'s HSD (honestly significant difference) test from the one-way ANOVA module in Statistica 7.1 software (StatSoft, Inc., Tulsa, OK, USA). KFT volume/sample mass ratio (\(V/m\) (mL/g)), water content (\(W\)) (%), variation of the KFT volume/sample mass on time (\(\Delta(V/m)/\Delta t\)) (mL/g/s) and the mean KFT reaction rate (\(v\)) (mM/s) were used as dependent variables. The categorical predictor (factor) was the bread sample code (_HC_ (white wheat bread core), _MC_ (whole meal bread core), _GC_ (Graham bread core_, _BC_ (brown wheat bread core), _MS_ (whole meal bread shell, _BS_ (brown wheat bread shell), _MCd_ (pre-dried whole meal bread core), _BCd_ (pre-dried brown wheat bread core), _MSd_ (pre-dried whole meal bread shell)) and _BCd_ (pre-dried brown wheat bread shell)). The parameterization was sigma-restricted, while the confidence limit and significance level were set at 0.95 and 0.05, respectively. PCA is a multivariate statistical tool providing information on similarity/dissimilarity between bread samples, as well as the influence of independent variables for grouping. Both titration and kinetic parameters from the KFT analysis were used for PCA. This technique approximates the data matrix as a product of two reduced matrices that retain only the useful information. _PC\({}_{1}\)_ (Factor 1, Principal Component 1) is that direction from the property space characterized by a maximum variance of the data. _PC\({}_{2}\)_ (Factor 2, Principal Component 2) also has a maximum variance of the data but restricted by orthogonality to the first component. Other _PCs_ can be obtained in the same manner, but only few _PCs_ will retain the main information from all KFT data. The scores matrix vectors will provide information about similarity/dissimilarity of the samples (grouping of the cases). The loadings matrix vectors will provide the similarity/dissimilarity of properties, as well as the influence of the properties to the model. PC&CA module from Statistica 7.1 package (StatSoft), with centered data and cross-validation method, were used for PCA analysis of the KFT data.
## 3 Algorithmic Errors
### AI-Enhanced Navigation Systems
#### 3.5.1 Implementation and Operationalization  
AI systems utilize a variety of technologies and processes to optimize shipping routes. Data collection is the first step, involving sensors and other sources that gather real-time information on weather conditions, ocean currents, and vessel positions. These data are then processed by machine learning algorithms, which analyze them to identify patterns and predict future conditions. The AI system evaluates multiple potential routes, considering factors such as distance, fuel consumption, and safety risks. It then ranks these routes based on efficiency and safety criteria, providing recommendations to the ship's navigational team.  
For example, the Wartsila FOS (Fleet Operations Solution) integrates real-time data from various sources to provide dynamic route optimization. This system uses machine learning algorithms to continuously update and optimize routes based on changing conditions, helping vessels avoid adverse weather and optimize fuel consumption. By analyzing historical and real-time data, Wartsila FOS can suggest the most efficient and safe routes, significantly improving operational efficiency and safety [59; 60].
## 3 Experimental Analysis
### Experimental Setup  
Comparative deep learning models and FPHFA models were built using TensorFlow. We utilized two layers of 1D CNN for learning local trends of features. Each layer was set to use the same filter size and kernel size, i.e., (62, 2), and ReLU was used as the activation function. We used two Bi LSTM layers for temporal feature learning with 128 hidden neurons per layer. The loss function of the FPHFA model is the mean squared error (MSE). Additionally, to prevent underfitting or overfitting of the model, The Beijing dataset was packaged and separated into a training set (80%) and a test set (20%).  
In this article, we use root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R\({}^{2}\)), index of agreement (IA), and Mean Absolute Percentage Error (MAPE) to evaluate the performance of the prediction models. The calculation formulae are shown below.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{25}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{26}\]  
Figure 3: Time series plots of factors of influence.  
\[\text{R}^{2}=\frac{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-\overline{\hat{y}}_{i})^{2}} }{\sum\limits_{i=1}^{n}{(y_{i}-\overline{\hat{y}}_{i})^{2}}} \tag{27}\]  
\[\text{IA}=1-\frac{\sum\limits_{i=1}^{m}{(y_{i}-\hat{y}_{i})^{2}}}{\sum\limits_{ i=1}^{n}{(|y_{i}-\overline{\hat{y}}|+|\hat{y}_{i}-\overline{\hat{y}}|)^{2}}} \tag{28}\]  
\[\text{MAPE}=\frac{1}{n}\sum\limits_{i=1}^{n}{\frac{|y_{i}-\hat{y}_{i}|}{y_{i}}} \tag{29}\]  
Here \(n\) is the number of samples, \(y_{i}\) is the actual value of PM\({}_{2.5}\), \(\hat{y}_{i}\) denotes the corresponding predicted value, and \(\overline{\hat{y}}\) denotes the average of all PM\({}_{2.5}\) values.
## 5 Conclusions  
In this work, a prototype of a VA was designed and implemented. It was trained with AI capable of performing natural language recognition and comparisons between different user queries, storing them in cache memory for faster responses in future similar queries. This system also adds a layer of security by keeping the information within a private network.  
The prototype successfully reduces energy consumption by an average of 2.95% of the total energy when using the cache, as opposed to what is required for cloud-based queries. Additionally, this implementation achieves significantly lower response times, on average 33 s faster than VAs that query the cloud, improving response times in critical processes and enhancing user experience.  
Another contribution of this work is a mathematical model based on the system's own attributes. This model allows representation of the elements and parts of the process that VAs perform and their relationship with energy consumption when connected to the cloud versus local connections.  
The presented metric allows for determining the energy consumption of voice assistants as a function of time. This was verified through mathematical calculations of power and energy, along with experimental tests on the prototype created. The results showed a high difference in metric (0.21 vs. 0.99) between queries to the cache memory and those made to the cloud. The large-scale implementation of this solution could significantly reduce the environmental impact by achieving considerable global energy savings.  
The proposed solution has several potential applications, particularly in situations where mobility affects communication, in remote or isolated areas with no signal coverage, in populations without Internet access, in areas with high concentrations of people and devices, and for scenarios where VAs are required to handle concurrent similar queries.  
This work opens up new lines of research, allowing for further exploration into energy-saving techniques by implementing cache memory for encrypted information outside the local device.  
Conceptualization, A.O.M.B., A.S.S., E.M.M.M.L. and J.H.-R.; methodology, A.O.M.B., A.S.S. and E.M.M.M.L.; software, J.H.-R.; validation, A.O.M.B. and A.S.S.; formal analysis, A.O.M.B.; investigation, A.O.M.B. and J.H.-R.; resources, A.O.M.B. and J.H.-R.; data curation, E.M.M.L. and J.H.-R.; writing--original draft preparation, A.O.M.B.; writing--review and editing, E.M.M.L. and A.S.S.; visualization, J.H.-R.; supervision, A.S.S. and E.M.M.L.; project administration, A.O.M.B., E.M.M.M.L. and A.S.S.; funding acquisition, A.O.M.B. and J.H.-R. All authors have read and agreed to the published version of the manuscript.  
The development of this research was supported by the ULPGC--University of Las Palmas de Gran Canaria in the discount voucher received for the APC payment of this article.  
The data may be provided free of charge to interested readers by requesting via the corresponding author's email.  
The authors declare no conflicts of interest.
## 4 Discussion
### Salinity Shifts Size Structure of Spirulina  
The exposure to salinity caused significant shifts in the size structure of the _Spirulina_ strain, which demonstrated high flexibility in its morphological response to osmotic stress. Cultures maintained at salinity levels between 10 and 22 ppt contained dense cell populations of small filaments that were under 400 \(\upmu\)m in length. The cultures grown at elevated salinity ranges (27-45 ppt) showed lower cell numbers but increased percentages of cells with sizes between 400 and 799 \(\upmu\)m. The results show that higher salt concentrations preferentially promote the growth and survival of longer filamentous cell shapes [33].  
The Principal Component Analysis (PCA) results showed that size structure variations are directly linked to salinity treatment conditions. The first principal component (PC1) used most of the data to distinguish between lower salinity conditions through their combinations of small and large filament numbers, yet linked larger intermediate filament sizes to higher salinity conditions. The observed cluster pattern reveals distinct size structure patterns across the salinity range, which most likely resulted from salt-triggered morphological adaptations. The results of the PERMANOVA analysis support the hypothesis that salinity has an effect on the filament size of _Spirulina_. Other environmental and physiological factors, including nutrient levels, light conditions, and genetic characteristics, might also influence filament size [33].  
The observed morphological variation is consistent with earlier findings that _Spirulina_ can undergo structural alterations under stress. Previous studies have demonstrated that _Spirulina_ undergoes architectural changes in filaments when exposed to stressors such as high light intensity, nutrient limitations, and temperature fluctuations [13; 34]. [PERSON] et al. [26] observed linear _Spirulina platensis_ variants showing altered protein expression patterns that included metabolic energy components and stress responses, as well as translation machinery elements, during stress-induced morphogenesis.  
Studies have suggested that stress-induced filament elongation or increased size might serve functional purposes by reducing the membrane ion exchange surface area and improving ionic homeostasis [14]. The larger filament dimensions may result in modifications to buoyancy and motility patterns, which offer survival advantages in salty waters with non-uniform light and nutrient distribution.  
These findings support the hypothesis that _Spirulina_ displays adjustable morphological traits through phenotypic plasticity in response to salinity changes. The ability to modify filament structure allows _Spirulina_ to survive in various aquatic ecosystems with changing environmental conditions.
## 4 Experimental Results
### Additional Experiments  
#### 4.4.1 Impact of the Segmentation Accuracy  
In addition to our evaluation experiments, we aim to assess the impact of segmentation accuracy on calibration quality. To achieve this, we calibrate and evaluate our system again using the ground truth segmentation data we created (see Section 3.2). Evaluations are performed image-wise across all scenes meeting the preliminary criteria, with results presented in Figure 12a.  
The comparison reveals no significant advantage of using ground truth over the InternImage segmentation. Quantitative analysis shows an average Chamfer distance of 0.51 m (ground truth) versus 0.41 m (InternImage) with the UniDepth backbone, 0.53 m (ground truth) versus 0.46 m (InternImage) with the Depth Pro backbone, and 0.64 m (ground truth) versus 0.65 m (InternImage) with the MiDaS backbone. These results suggest that calibration using ground truth segmentation can sometimes yield poorer outcomes than using InternImage. A possible explanation is that segmentation by a model like InternImage may act as a natural low-pass filter, mitigating the effects of inaccuracies by excluding challenging parts of the image through missed classifications, thereby improving overall depth prediction.  
Figure 11: Average Chamfer distances of average scaled point clouds combined with average extrinsic calibration.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{3}{c}{**Average Chamfer Distance**} \\ \cline{2-4}  & **UniDepth** & **Depth Pro** & **MiDaS** \\ \hline Pure Image-wise & **0.80 m** & 0.95 m & 0.98 m \\ Av. extr. Image-wise scale & **0.81 m** & 0.94 m & 0.96 m \\ Average Extr. and Scale & **1.08 m** & 1.64 m & 1.19 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the experimental results on scaling combined with extrinsic calibration.
# Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System
## 3 Agent Motion by Forces
### Local Trajectory Planning
```
Count the error sum \(\widetilde{\mathbf{p}}_{i}\), \(\widetilde{\mathbf{v}}_{i}\) in time step \(\mathbf{p}_{io}=\overrightarrow{p}_{i}o*(d\_react/||p_{io}||)^{2}\,\mathbf{p}_{iv}= \overrightarrow{p}_{iv}*||p_{iv}||\) \(\mathbf{p}_{ij}=\overrightarrow{p}_{ij}*||p_{ij}||\) \(\mathbf{v}_{ij}=\mathbf{v}_{i}-\mathbf{v}_{j}\), \(\mathbf{v}_{iv}=v_{i}-\mathbf{v}_{vl}\) Calculate equilibrium location \(\mathbf{p}_{i}\) \(\
abla_{i}[U_{i}(||\widetilde{\mathbf{p}}_{ij}||)+U_{io}(||\widetilde{\mathbf{p}}_{ io}||)+U_{ip}(||\widetilde{\mathbf{p}}_{ii}||)]\) Calculate the state \(X_{n+1}\{p_{i},v_{i}\}\) in next time \(X_{n+1}=X_{n}+h\sum_{i=1}^{K}a_{i}k_{i}\) Check \(||p_{io}||<d\_\text{min}\) \(||v_{i}||<v\_\text{min}\) end
```  
**Algorithm 1**Local Trajectory Planning  
Where \(d\_react\) is the sensing distance based on the radius of the obstacles, \(d\_\text{min}\) is the minimum distance between agents and obstacles, and \(v\_\text{min}\) is the lower bounds of velocity, which can be described as:  
\[\left\{ \begin{array}{l}v_{\text{min}}\leq||\mathbf{v}_{i}\ ||\leq v_{\text{max}}\\ u_{\text{min}}\leq\ \mathbf{u}_{i}\leq u_{\text{max}}\end{array} \right. \tag{18}\]  
Furthermore, to mitigate the impact of local minima limitations in the implementation of APF, such as a formation navigating through narrow openings between obstacles, it  
Figure 5: Illustration for one agent’s local trajectory planning in formation. The forces in green are between agents, agents and virtual leaders, and agents and obstacles. The dotted line in black represents the original and iterated trajectory. (To simplify the calculations of obstacle forces, the obstacles are enclosed within a circle). The agent is red, virtual leader red-dotted (Both arrows show the direction). is essential to reduce the velocity of the virtual leader as the forces applied to the real agents increase.