esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Kinematic Data Analysis  
A two-way ANOVA of kinematic variables found significant interactions for stride duration (F\({}_{(2,22)}\) = 3.885, \(p\) = 0.036, Figure 4A) with a main effect for speed where stride duration decreased (F\({}_{(2,22)}\) = 1249, \(p\) < 0.0001). There was no main effect for shoes (F\({}_{(1,11)}\) = 2.518, \(p\) = 0.144). Further breakdown of the gait cycle presented no speed*shoe interaction (F\({}_{(2,22)}\) = 1.896, \(p\) = 0.176) for ground contact time (Figure 4B), with no main effect of shoes (F\({}_{(1,11)}\) = 2.266, \(p\) = 0.163), but a decreased effect of speed (F\({}_{(2,22)}\) = 1076, \(p\) < 0.0001). There was a significant interaction (F\({}_{(2,22)}\) = 22.47, \(p\) < 0.0001) for time to mid-stance (Figure 4C) with an increased main effect for shoes (F\({}_{(1,11)}\) = 30.77, \(p\) = 0.0002) and a decreased effect of speed (F\({}_{(2,22)}\) = 1203, \(p\) < 0.001. However, shoes only presented post hoc differences at 6 km\(\cdot\)h\({}^{-1}\) (0.276 vs. 0.301s\({}^{-1}\), \(p\) < 0.0001). There was also no interaction (F\({}_{(2,22)}\) = 0.147, \(p\) = 0.864) or main effect for shoe (F\({}_{(1,11)}\) = 2.728, \(p\) = 0.130) for swing time. There was, however, a main effect for speed (F\({}_{(1,11)}\) = 43.96, \(p\) < 0.0001, Figure 4D).  
Figure 3: Mean \(\pm\) SD data for each treadmill running speed (6, 10, and 14 km\(\cdot\)h\({}^{-1}\)) whilst wearing minimalist and modern-day cushioned shoes for (**A**) peak vertical impact (N\(\cdot\)BW\({}^{-1}\)), (**B**) peak active force (N\(\cdot\)BW\({}^{-1}\)), (**C**) average loading rate (N\(\cdot\)BW\({}^{-1}\).s\({}^{-1}\)), and (**D**) impulse (N\(\cdot\)s-BW\({}^{-1}\)), where \(\blacksquare\) is the minimalist shoe and \(\blacksquare\) is the cushioned shoe. * Indicates significant interaction (speed*shoe), and \(\bigstar\) indicates post hoc significance between shoes. Symbol number indicates significance level, e.g., *\(p\) < 0.05, \(\bigstar\)\(\bigstar\)\(p\) < 0.01, ***\(p\) < 0.001.  
Overall, the mean number of steps analysed from all trials were 23 \(\pm\) 2 steps. It is also important to note that, even with such few steps, there is a certain degree of variance within the participants, as shown in Table 1.
###### Abstract  
The context of this study is the geolocation of signal emitters on the Earth's surface through satellite platforms able to perform Angle of Arrival (AOA) measurements. This paper provides the theoretical framework to solve the optimization problem for the orbital deployment of the satellites minimizing the variance on the position error estimation with constraints on the line of sight (LOS). The problem is theoretically formulated for an arbitrary number of satellites in Low Earth Orbit (LEO) and target pointing attitude, focusing on minimizing the Position Dilution of Precision (PDOP) metric, providing a methodology for translating mission design requirements into problem formulation. An exemplary numerical application is presented for the operative case of the placement of a second satellite after a first one is launched. Simulation results are on angles of true anomaly, right ascension of the ascending node, and spacing angle, while accounting for orbital radius and emitter latitude. New insights on trends, parameter dependencies, and properties of symmetry and anti-symmetry are presented. The topic is of interest for new technological demonstrators based on CubeSats with AOA payload. Civil applications of interest are on interceptions of non-cooperative signals in activities of spectrum monitoring or search and rescue.  
angle of arrival; dilution of precision; geolocation; low earth orbit; mission; optimization; satellite; simulation; space sensors; space systems Article
## 3 Data Assimilation
### Trajectory Filtering  
In the case of experimental flows (real data), estimated velocity (u \(\approx\Delta\text{r}/\Delta\text{t}\)) cannot be used directly in the data assimilation system. To resolve this issue, a statistical description of individual trajectories in the form of a space-time averaging filter is applied. \(V_{averaged}\)\((x,t)\) is defined as the mean velocity observed at time t and location X in a space-time window W = Wt \(\times\) Ws, where Wt is a temporal window around t and Ws is the spatial neighbourhood of X(t).  
In Figure 2, the spatial window (red box) is indicated, representing the location of Lagrangian drifters from time t1 to time t2. As drifters move along the water, some of them enter and depart from this spatial window during the corresponding time of the window (Wt). We \"average out\" the velocities of all the drifters traveling through the specific spatial window during a given time interval \((V_{averaged}\)\((x,t)\), green arrow in Figure 2. This filtered velocity represents the local flow velocity at a specified time and is subsequently used in the flow computations.  
Depth averaged velocity is obtained from the drifter surface velocities using a correction factor of 0.85 following the logarithmic-law-of-the-wall for velocity profiles measurement in open channels [44].  
\begin{table}
\begin{tabular}{c c c} \hline Parameter & Description & Value \\ \hline \(\sigma_{WI}\) (\(m\)) & Water level standard deviation & 0.05 \\ \hline \(\sigma_{Discharge}\) (\(m^{3}\)/s) & Discharge standard deviation & 1 \\ \hline \(\rho_{WI}\) (\(h\)) & Water level temporal correlation & 6 \\ \hline \(\rho_{Discharge}\)(\(h\)) & Discharge temporal correlation & 6 \\ \hline \end{tabular}
\end{table}
Table 2: Noise model parameters.
# Diagnosis of Pressure Ulcer Stage Using On-Device AI
## 3 Results
### Pressure Ulcer Detection Performance
The total loss, which is a combination of these losses, was decreased on both the training and validation dataset during the training process. Hence, precision, recall, and mAP metrics were gradually enhanced with the increasing epoch. From this confusion matrix, we calculated all metrics for all six classes (Table 5). As a result, all metrics varied across the pressure ulcer types and showed good results for pressure ulcer detection. The YOLOv8m model correctly detected with nearly 90% of precision, recall, F1 score, and mAP@50. Examples of output images by the trained YOLOv8m model are described in Figure 4. Both the bounding box outlining each pressure ulcer area and the corresponding stage with classification probability were simultaneously displayed. Individual pressure ulcers such as stage4, DTPI, and unstageable were successfully detected in the test images with a single class (Figure 4a-c). When there were two or more stages of pressure ulcers in the test images, the trained YOLOv8m model was also able to encase the ulcer region and identify its stage (Figure 4d-f). Hence, the trained YOLOv8m model provided reasonable judgments to predict the affected area and the severity of the individual pressure ulcer. Figure 3 describes the confusion matrix providing a graphical representation of the detailed classification results of the YOLOv8m model. The trained YOLOv8m model predicted the detailed type of pressure ulcers in the test dataset with a high score. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Label** & **Precision** & **Recall** & **F1 Score** & **mAP@50** & **mAP@50-95** \\ \hline Overall & 0.897 & 0.891 & 0.894 & 0.908 & 0.685 \\ \hline Stage 1 & 0.959 & 0.857 & 0.905 & 0.900 & 0.630 \\ \hline Stage 2 & 0.794 & 0.843 & 0.818 & 0.839 & 0.564 \\ \hline Stage 3 & 0.874 & 0.843 & 0.858 & 0.874 & 0.645 \\ \hline Stage 4 & 0.938 & 0.957 & 0.947 & 0.975 & 0.922 \\ \hline DTPI & 0.905 & 0.926 & 0.915 & 0.936 & 0.664 \\ \hline Unstageable & 0.911 & 0.918 & 0.914 & 0.924 & 0.682 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detailed performance evaluation of the trained YOLOv8m model for pressure ulcer detection. Figure 3: Confusion matrix of pressure ulcer classification by the trained YOLOv8m model. Figure 2: YOLOv8m training results.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 3 Tracking Error Analysis of Delta Doppler Aided PLL
### Stable Tracking Criterion of INS-Aided PLL  
According to Equation (5), we can get the output of the phase discriminator \(e(k)=\Psi(k)+n_{\theta}(k)\), then the mean of the steady-state output of the phase discriminator \(e_{ss}\) is the dynamic stress error, which can be expressed as:  
\[E[e_{ss}]=E[\Psi_{ss}] \tag{32}\]  
And the variance of the steady-state output of the phase discriminator due to the input noise \(n_{\theta}=n_{\theta}^{T}+n_{\theta}^{osc}\) can be calculated as:  
\[\mathrm{var}[e_{ss}]=\mathrm{var}[\Psi  
In order to ensure stable tracking, we select the phase tracking error of discriminator output as the criterion which can be expressed as:  
\[\sigma_{pd}=\sqrt{\mathrm{var}[e_{ss}]}+\frac{1}{3}E[e_{ss}]\leqslant 15^{\circ} \tag{34}\]
## 3 Results and Experimental Verification
The sample with the greatest overall deviation (\(E=0.35\,\mathrm{J/mm}\), \(z_{\mathrm{focus}}=1.1\) mm) has a comparably large number of dark areas, and it was unfavorably labeled with a black pen in the area of the weld seams. Initially, we related the dark areas to our marking of the samples. They often occur below the marking but also occur independently of it. However, due to the specified cleanliness in industrial applications, particles and markings are a minor problem, and the detection rate will probably increase. Even if weld seam identification is successful in only 16% to 33% of B-scans, the average distance between detected seams is less than 0.1 mm in our case, with a spacing of 0.007 mm between consecutive B-scans. This is far more than is practical with microtome sections for post-process evaluation. For potential in-process monitoring, our current setup allows for approximately 200 B-scans per second and, therefore, about 50 successfully identified seams per second. We believe this to be sufficient in most applications, and a faster acquisition is possible by reducing the lateral field-of-view (the horizontal scan range) or sacrificing lateral resolution. We assume that the weld seam is clearly visible in PA6 because of changes in morphology during melting and solidification, even if the morphology of the weld seam has not yet been analyzed. This hypothesis is based on the findings by [PERSON] that changes in morphology can be detected using a similar (not polarization-sensitive) OCT during extrusion [35]. Since the weld seam is clearly visible in polarized transmitted light (Figure 1 right and Figure 4a left), the use of a polarization-sensitive OCT should improve detection and might enable it with amorphous materials as well, since birefringence caused by stress becomes visible [26; 36; 37; 38]. However, polarization-sensitive OCT is much more complex, which might limit its industrial use.
## 2 Frequency and Active Power Control Schemes
### Alternative aFRR Participation Methods for BESS
#### 2.2.1 Filter-Based AGC  
The principle behind the \"filter-based AGC\" method is that battery systems take up the fast-varying components of secondary regulation, while thermal units follow slow-varying dispatch orders for load-following purposes. Variants of this AGC control strategy are proposed in papers [36; 37; 38; 39], using either a low pass filter or rate limiters to derive the regulating signals for thermal units. A proportional SOC control proposed in [36; 37] is implemented at the local BESS level, to ensure energy reserves. Figure 4 illustrates the schematic diagram of the filter-based AGC method. A 1 min time constant is chosen for the AGC filter (\(T_{fagc}\) = 60 s) to maximize battery utilization. As in [36; 37], BESSs are not participating in the economic dispatch of the system, since their SOC is regulated through the always active p-controller. The participation factors of thermal units (\(pf_{gi,ED}\)) sum to unity; the same holds for the participation factors of battery systems (\(pf_{bi,ED}\)) [37].  
Figure 3: BESS converter controller.
# Organic Materials Used for Giant Buddhas and Wall Paintings in Bamiyan, Afghanistan
## 2 Materials and Methods
### Synchrotron Radiation (SR)-Based Micro-Analyses  
To obtain clues about the chemical composition of different paint layers, and particularly about the organic binders hypothesized from UV-optical microscopy (OM), we decided to apply a combination of SR-based micro-analytical techniques.  
SR-uFTIR analyses were carried out at the former infrared end-station at the ID21 beamline at the ESRF [14]. We used the Nexus infrared spectrometer coupled with the Continuum microscope from Thermo Nicolet. Spectra were measured in transmission mode. The beam size was reduced to 12 \(\times\) 12 \(\upmu\)m\({}^{2}\) or 8 \(\times\) 8 \(\upmu\)m\({}^{2}\), exploiting the high brightness of the SR beam. Two-dimensional maps were acquired through one FTIR spectrum (a sum of 25 scans in the range of 4000-800 cm\({}^{-1}\) and with a resolution of 8 cm\({}^{-1}\)) per pixel and by raster scanning of the sample with a step size of 8-12 \(\upmu\)m. Data acquisition and analysis were carried out using the Omnic software from Nicolet and the regions of interest (ROI) imaging tool from PyMca software [15]. Molecular maps were obtained by calculating the integrated intensity over specific ROI and through non-negative matrix approximation analysis (NNMA). From these maps, average spectra or NNMA vectors were extracted and analyzed in order to identify the main components.  
The preparation of samples is particularly complex for such analyses because any standard embedding medium would contribute to the FTIR signal [16]. Some samples were prepared as thin sections from resin-embedded blocks (primarily prepared for OM and micro X-ray analyses), but the FTIR signal was usually not exploitable. Thus, we decided to prepare specific samples for SR-\(\mu\)FTIR analyses. In brief, under a binocular stereomicroscope, small (\(\sim\)10-20 \(\upmu\)m) sections were sliced from paint fragments with a scalpel, deposited on a diamond cell (with the stratigraphic surface parallel to the diamond surface), and gently pressed to reduce the paint thickness and the infrared absorption. This inevitably led to some disruption of the stratigraphic structure but, in many cases, the multilayer structure is still visible, and the sample effectively represents each individual layer. The sample thickness could not be well controlled, and some parts were still too thick and absorbent, leading to saturated spectra. Such issues impacted data analysis in a few places, especially in the red layers.
## 3 Methodology  
### Artificial Neural Networks  
ANNs are mathematical models falling in the class of non-linear parametric functions [50; 51; 52; 53; 54]. Such models are the result of the weighted and biased connection of logistic regression units (called artificial neurons), organized in several sequential layers: an input layer that receives the features vector, one or more hidden layers, and an output layer that produces the network outcome. The connections, which establish the complexity and computing power of the ANN, link neurons belonging to different layers so that the information can travel from the input to the output layer in only 1 direction. Nevertheless, the activity of each neuron is ruled by a non-linear activation function that determines whether the processed output should be transmitted or interrupted. Thanks to this network architecture, ANNs can identify the relationship or pattern that links input predictors to target variables: connection weights and biases are set by a supervised training process that aims to minimize the prediction error of the experimental targets.  
This study focuses on shallow neural networks (SNNs), i.e., 3-layer perception networks, which have been shown to solve arbitrarily well any multidimensional input-target fitting problem by providing a sufficient number of neurons in their only hidden layer [55]. The proposed SNN consists of a 3-neuron input layer (1 neuron for each input feature), an \(N\)-neuron hidden layer whose processed output is passed to a non-linear activation unit, and a 7-neuron output layer associated with a linear activation function. The number of neurons \(N\) and the activation function \(FUN\) characterizing the hidden layer were identified through a grid search procedure, choosing the combination that yielded the lowest score on the performance function. In this study, the integer range \(X_{N}=\{1,\cdots,100\}\) was used for selecting the number of hidden neurons and the following activation functions were considered: the exponential linear (ELU), the hyperbolic tangent (TanH), and the logistic sigmoid (LogS) units (Figure 3). The input features were the bitumen content (% by volume of mix), percentage of filler (%), along with a categorical variable that distinguishes the filler materials (values: 0 for Silica Fume, 1 for OPC). The output variables were air void content (%), the voids in the mineral aggregate (%), the Marshall stability before and after aging (kN), the Marshall quotient (kN/mm), the indirect tensile strength (kPa), and the water sensitivity of asphalt mix (%). Each variable belonging to both input and target vectors was standardized before being processed by the network, i.e., all variables were rescaled to have 0 mean and unity standard deviation. Such an adjustment served to reduce computational time and improve the efficiency of the neural model.
## 2 Industrial Cloud-Edge Collaboration
### Cloud-Edge Security  
For data storage security in cloud service environment, a series of data privacy management, data transmission encryption, data access control [18], authentication service [19], security audit [20] and other security mechanisms ensure the availability, confidentiality, and non-repudiation of the data in the cloud storage system, thus improving the security of the data. In order to guarantee the data security of edge computing, the network communication encryption technology of computer can be utilized to encrypt the channel and effectively guarantee the security of edge computing data communication.  
Currently, some scholars believe that the different computing paradigms from cloud computing to edge computing have formed a unique ecosystem [21]. And for the edge end, [PERSON] et al. [22] conduct an objective comparison and evaluation using different deep learning models to protect the security well with intrusion detection techniques. [PERSON] [23] proposes a new model of network intrusion detection based on matched filter optimization called Network Intrusion Detection based on Matched Filter Optimization NIDeMFO; the evaluation proves more competitive and effective than the existing detection models compared. Edge data transmission can use data encryption; [PERSON] [24] [H] proposes a hybrid cryptosystem based on state secret algorithms SM2 and SM4 and their system solution for realizing secure communication, which improves the security of information transmission and key sharing. [PERSON] [25] proposes a novel low-latency, bit-length parameterizable encryption method to provide the possibility of low-latency communication encryption. In the process of edge computing, if there is a problem in the edge node itself, the impact is quite serious; for example, in the automotive industry chain, if a tire company's products do not meet the standard, then it will lead to quality problems in all the downstream products. At present, for the previous problems, there are many scholars [26; 27; 28; 29] researching this, and regulation based on the third-party trustworthy organization is a good strategy to effectively avoid the the previous situation.  
With the continuous development of the industrial Internet, the production efficiency is gradually improved, the amount of data is growing, and it is becoming more and more important for the data to be secured. Every node in the industry chain has to face malicious attacks from all angles. In this paper, we use the credibility mechanism to establish a good evaluation system for the edge nodes, and use the adaptive encryption technology to take care of both the efficiency and the security, and ultimately ensure the security of the data.
# Three-Band Spectral Camera Structure Design Based on the Topology Optimization Method
## 6 Conclusions  
This paper proposes a topology optimization approach for the support structure of a three-band multispectral camera, enhancing both system performance and weight reduction. Through a combination of topology optimization and parameter optimization using the MOGA algorithm, a lightweight design for the support structure was developed, ultimately reducing the total mass of the housing and support by 42.4%.  
The engineering analysis shows that the entire structure experiences the maximum deformation of 6.15 nm under a gravitational load. When subjected to a 1 g gravity load, the deformation of each lens was evaluated using Zernike polynomials to assess its impact on the optical system's imaging quality. The results showed that all values complied with the RMS requirements, remaining below 4 \(\upmu\)m, 10 \(\upmu\)m, and 15 \(\upmu\)m for the visible light system, the mid-wave infrared optical system, and the long-wave infrared optical system,  
Figure 15: Three-band multispectral camera testing images: (**a**) visible light image; (**b**) mid-wave infrared image; (**c**) long-wave infrared image.  
respectively. Additionally, all values satisfied the MTF requirements, exceeding 0.25, 0.25, and 0.3 for the respective systems. In terms of dynamic performance, the structure achieved a satisfactory fundamental frequency exceeding 100 Hz. Test results further confirmed that the designed optical support structure met the imaging requirements at 18.5 km, demonstrating excellent mechanical performance.  
The feasibility of combining topology optimization with parameter optimization has been demonstrated, providing valuable insights for the structural design of three-band multispectral cameras. However, a key challenge remains in reducing the overall volume of the support structure, which could further enhance the camera's compactness and integration capabilities. Future research could explore the use of advanced materials, such as composite materials, or additive manufacturing technologies to further improve the performance while reducing the costs and production time.  
[PERSON], [PERSON] and [PERSON] were responsible for the whole experiment; [PERSON] wrote the manuscript; [PERSON] carried out the topology optimization method and the numerical simulations; [PERSON] designed the optical system; [PERSON] was responsible for image collection. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62271484, and in part by the National Science Fund for Distinguished Young Scholars, grant number 61925112.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.