esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerostat-Based Observation of Space Objects in the Stratosphere
## 4 Observation Ability Analysis
### Observation Ability in the Visible Band  
To evaluate the observation ability of the space debris of the SOFOS, it can be judged according to the signal-noise ratio (SNR). It is generally believed that the object can be detected when the SNR of the object is greater than three. The SNR of five is, therefore, set as the object-detectable condition for observation ability analysis. Using the telescope with a 20\({}^{\circ}\)\(\times\) 20\({}^{\circ}\) large FOV as an example, whose focal length is 178 mm, optical efficiency is 70%, the CMOS detector (Gpixel Changchun Optotech Inc., Changchun, Jilin, China) has a 10 \(\upmu\)m pixel size with a resolution of 6144 \(\times\) 6144, peak quantum efficiency of 75% and readout noise of 4.2 e; meanwhile, considering that 80% energy of the starlight is concentrated in four pixels [22], assuming the sky background is 21.6 mag, it results in the decent value of a clean observation night without any light pollution. The calculation results for the observation ability of this telescope are shown in Figure 8. When the exposure time of the telescope is set to 1, 5, and 30 s, the observation ability reaches 14.5 mag, 15.6 mag, and 16.6 mag, respectively.  
For the NEA observation, a telescope with a 500 mm aperture is used to analyze the observation ability of the SOFOS. Its focal length is 800 mm, FOV is 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\), and optical efficiency is 70%, while the CMOS detector has a pixel size of 11 \(\upmu\)m with a resolution of 4096 \(\times\) 4096, whose peak quantum efficiency is 70% with a readout noise of 1.6 e. Considering 80% energy of the starlight is concentrated on nine pixels, the results (see Figure 8) show that the observation ability of this telescope reaches 17.0 mag, 18.0 mag, and 18.9 mag when the exposure time is 1 s, 5 s, and 30 s, respectively.  
Figure 8: The observation ability in the visible band of the telescopes with different apertures.
## 1 Introduction  
3D digitisation of museum resources is becoming a more and more frequent practice, but the vast majority is performed by specialised teams representing the scientific community or professional companies offering such services. In recent years, data acquisition technologies have been developed to create three-dimensional models that use data belonging to various types of information. In photogrammetry [1] and Structure from Motion (SfM) [2], a collection of photos is used to build 3D models, which in successive shots of an object cover at least 60% of the surface. Laser scanning or structured-light 3D scanning (3D SLS) provides a cloud of points (x, y coordinates from the object's surface), which ultimately allows us to build its 3D model. In addition to the acquisition of surface point coordinates, colour data are also collected, thanks to which the generated digital model obtains realistic features. In the era of the global pandemic caused by the COVID-19 [3] virus, when excursion and pilgimage traffic has been halted or significantly restricted, many cultural heritage museum institutions wish to maintain their presence in the tourism market by introducing traditional exhibitions into digital space. The attractiveness of such digital exhibitions, increasingly known as Virtual Museum (VM), will largely depend not only on the uniqueness of the exhibits presented, but also on their digital quality. SfM technology belongs to the so-called low-cost technologies, which may also suggest that the models obtained in this way will not be of sufficiently good quality. The paper presents the results of comparative studies of digital models of the same museum artefacts from the Silk Road area generated by two completely different technologies: SfM and 3D SLS. The developed three-dimensional models based on the point cloud obtained from the scanning were created from data collected in the Afrasiyab museum in Samarkand (Uzbekistan) during \"The 1 st Scientific Expedition of the Lublin University of Technology to Central Asia\" in 2017 [4]. Photos for creating 3D models in SfM technology were taken during a virtual expedition carried out as part of the \"3D Digital Silk Road\" [5] program in 2021. The purpose of the article can be summarized as follows:  
* Comparison of data acquisition procedures for artefacts of material cultural heritage in SfM and 3D SLS technologies.
* Generating 3D objects of museum artefacts in SfM and 3D SLS technology.
* Assessment of parameters and quality of 3D models obtained in SfM and 3D SLS technologies.
# Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
###### Abstract  
Massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) are two key services in fifth-generation (5G) mobile wireless networks. These networks have been developed with extremely high service quality requirements: scalability for mMTC and reliability with low latency for URLLC. Fifth-generation network slicing will play a key role in supporting the distinct requirements of various services. Software-defined networking (SDN), a promising technology for network softwarization, physically separates the network control plane from the data plane by centrally controlling switches with an SDN controller. However, control channel bottleneck and processing delays due to this centralized control may reduce the scalability, reliability, and security of SDN. This paper proposes an SDN framework with programming protocol-independent packet processor (P4) switches (SDNPS), and defines a packet format containing in-band network telemetry data to simultaneously support heavy Internet of Things and URLLC traffic in 5G network slices. The method both satisfies the requirements of mMTC and URLLC and alleviates the load on the SDN controller. P4 is an advanced switch interface technology that provides enhanced stateful forwarding and reveals a persistent state on the SDN data plane. To demonstrate the superiority of SDNPS, simulations are performed on conventional SDNs and SDNPS. SDNPS outperforms the other schemes in terms of average throughput, packet loss ratio, and packet delay for both the mMTC and URLLC network slices.  
mMTC; URLLC; network slicing; INT; P4 +
Footnote †: journal: Electronics Electronics  
1
Footnote 1: [[https://doi.org/10.3390/electronics1114211](https://doi.org/10.3390/electronics1114211)]([https://doi.org/10.3390/electronics1114211](https://doi.org/10.3390/electronics1114211))
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{4}{c}{**Whole Data**} & \multicolumn{4}{c}{**For Individual Groups**} \\ \cline{2-7}  & & **Training** & **Testing** & **Dementia** & **MCI** & **Healthy** \\ \hline \multirow{2}{*}{Image level} & No pre-processing & 78.3 \(\pm\) 0.9 & 76.2 \(\pm\) 1.7 & 80.2 & 72.3 & 80.6 \\  & Proposed procedure & 88.1 \(\pm\) 0.6 & 87.7 \(\pm\) 1.6 & 91.7 & 83.0 & 92.3 \\ \hline \multirow{2}{*}{Subject level} & No pre-processing & 72.9 \(\pm\) 1.9 & 70.2 \(\pm\) 7.2 & 79.1 & 63.9 & 72.5 \\  & Proposed procedure & 83.6 \(\pm\) 1.5 & 80.3 \(\pm\) 6.6 & 91.7 & 80.9 & 75.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of an ablation study on the performance of the EPVS rating estimation model with and without pre-processing with the Haar transformation approach (testing and training accuracy is reported as mean \(\pm\) standard deviation; cross-entropy was used for loss function during training). Figure 4: The prediction results of individual subjects: (**a**) All correctly predicted subjects (vertical axis; the number of images indicates the number of incorrectly predicted images per subject (out of six images per subject)). At subject level, all these subjects were rated correctly, although there were some incorrect predictions at image level. (**b**) All incorrectly predicted subjects (numbers corresponding to each subject in the brackets shows the number of EPVS found in each image). The numbers highlighted red and purple are the incorrect EPVS numbers identified in the corresponding image that have affected the final rating of the subject. We can also observe that the numbers highlighted in red are close to the decision boundary and caused under rating of EPVS when compared to ground truth. We compared our model to the automatic method of EPVS quantification using Haar-like features and random forest as a baseline approach (see [25]). The compared method is a traditional approach that is one of the most represented techniques for the quantification of EPVS. We trained and tested the baseline method with the same dataset used in our estimation model. Compared to the other study, our estimation model has ~17% increased accuracy at both image and subject level (the baseline approach showed 69.73 \(\pm\) 0.5% accuracy at image level and 63.02 \(\pm\) 1.4% accuracy at subject level for our dataset).
# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 3 Material and Methods
### Dimension Reduction Using PCA  
PCA uses orthogonal transformations to identify the correlated variables and convert them into uncorrelated ones. For efficient classification, the dimension of the fused feature (FF) vectors is reduced by employing PCA. Consequently, the most discriminant features are obtained for the construction of the model. FF has the \(n\) dimensions \(FF=x_{1},x_{2},\ldots,x_{n}\), which needs to be reduced into \(k\ll n\) dimensions. The following steps are used by the PCA to obtain a reduced fused feature (RFF) set:  
1. Data scaling: \[x_{j}^{i}=\frac{x_{j}^{i}-\overline{x_{j}}}{\sigma_{j}}\] (26)
2. Co-variance matrix computation: \[\sum=\frac{1}{m}\sum_{i}^{m}(x_{i})(x_{i})^{T},\sum\in R^{n\times n}\] (27)
3. Eigenvector and Eigenvalue calculation: \[u^{T}\sum=\lambda\mu U=\begin{bmatrix}|&|&|\\ u_{1}&u_{2}...&u_{n}\\ |&|&|\end{bmatrix},u_{i}\in R^{n}\] (28)
4. Eigenvalue selection. We have selected the top 100 Eigenvalues from K-dimensional space to be used in LDA classification, as shown in the following equation: \[x_{i}^{new}=\begin{bmatrix}u_{1}^{T}x^{i}\\ u_{2}^{T}x^{i}\\.....\\ u_{k}^{T}x^{i}\end{bmatrix}\in R^{k}\] (29)
# A Semi-Analytical Approach for the Linearized Vibration of
## 3 Linearized Vibration of Beams with the Effect of Static and Thermal Load  
### Formulation  
The pressure load \(P\) is the combined load of static pressure \(P_{0}(x)\) and acoustic pressure \(P_{1}(x,\,t)\). The transverse displacement \(w\) can be divided into static deflection \(w_{0}(x)\) and vibration \(w_{1}(x,\,t)\), which respectively corresponds to \(P_{0}(x)\) and \(P_{1}(x,\,t)\). Then Equation (5) can be expressed as follows:  
\[\begin{array}{l}\rho A\ddot{w}_{1}+c\dot{w}_{1}-\frac{3}{2}EAw^{\prime 2}_{0}w^{ \prime\prime}{}_{0}-3 EAw^{\prime}{}_{0}w^{\prime\prime}{}_{0}w^{\prime}{}_{1} -\frac{3}{2}EAw^{\prime 2}_{1}w^{\prime\prime}{}_{0}-\frac{3}{2}EAw^{ \prime 2}_{0}w^{\prime\prime}{}_{1}\\ -3 EAw^{\prime}{}_{0}w^{\prime}{}_{1}w^{\prime\prime}{}_{1}-\frac{3}{2}EAw^{ \prime 2}_{1}w^{\prime\prime}{}_{1}+EIw^{(4)}_{0}+EIw^{(4)}_{1}-N_{x}w^{ \prime\prime}{}_{0}-N_{x}w^{\prime\prime}{}_{1}=P_{0}+P_{1}\end{array} \tag{7}\]  
Due to the static pressure being substantially greater than the acoustic pressure, the vibration amplitude is significantly less than the static deflection. Consequently, the nonlinear vibration problem can be viewed as a linearized oscillation around the nonlinear static equilibrium state. If the rotations under acoustic pressure are a small quantity and ignored, the governing equation can be written as  
\[\rho A\ddot{w}_{1}+c\dot{w}_{1}-\frac{3}{2}EAw^{\prime 2}_{0}w^{\prime\prime}{}_{0}- \frac{3}{2}EAw^{\prime 2}_{0}w^{\prime\prime}{}_{1}+EIw^{(4)}_{0}+EIw^{(4)}_{1}-N_{x}w^{ \prime\prime}{}_{0}-N_{x}w^{\prime\prime}{}_{1}=P_{0}+P_{1} \tag{8}\]  
Omitting the impact of \(w_{1}\) on \(w_{0}\), the static deflection \(w_{0}\), and static pressure \(P_{0}\) can be represented in terms of a relationship:  
\[-\frac{3}{2}EAw^{\prime 2}_{0}w^{\prime\prime}{}_{0}+EIw^{(4)}_{0}-N_{xx}w^{ \prime\prime}{}_{0}=P_{0} \tag{9}\]  
By inserting Equation (9) into Equation (8), the linearized equation of nonlinear vibrations can be obtained.  
\[\rho A\ddot{w}_{1}+c\dot{w}_{1}-(\frac{3}{2}EAw^{\prime 2}_{0}+N_{x})w^{ \prime\prime}{}_{1}+EIw^{(4)}_{1}=P_{1} \tag{10}\]  
Thus the nonlinear dynamic problem is analyzed in two parts: the nonlinear static problem and the linearized vibration around the nonlinear static equilibrium state.
## 6 Conclusions  
While the classical statistical approach to the problem of time series forecasting is still practically relevant, recent advances in the machine learning field have introduced to the scientific community a variety of different models and a multitude of possible combinations of such models and their statistical counterparts. Nevertheless, the ARIMA method is prevailing in several cases, due to the nature of specific forecasting applications and datasets. Furthermore, even considering the sheer force of the machine learning models and their numerous predictive capabilities, they have significantly larger computational demands in contrast to the classic statistical models. The details of the forecasting problem at hand, the available resources, and the previous scientific approaches to a specific task should all be considered prior to proceeding with a single forecasting strategy.  
Conceptualization, Investigation, V.I.K.; Methodology V.I.K. and A.D.P.; Validation, V.I.K., A.D.P. and I.K.; Writing--original draft V.I.K., A.D.P. and I.K.; Writing--review & editing V.I.K., A.D.P., I.K. and G.K.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to express our gratitude to the reviewers of this paper for their insightful comments and suggestions, which have helped us improve the quality of the first version of our manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criterion** & **ARIMA** & **Artificial Intelligence** \\ \hline \multirow{6}{*}{Model} & Explainable & Regarded as a “black box” \\  & Flexible specification & Need training \\  & Reliable performance & Need optimization \\  & Designed to model dependencies & Better at modelling \\  & that can be linearizable & non-linear time dependencies \\  & through a single transformation & \\  & Parameter specification & Standard training procedure \\  & depending on user-experience & \\ \hline \multirow{6}{*}{Dataset} & Suitable for small datasets & Need large datasets to train \\  & Missing values not important & Difficult modelling \\  & Multiple seasonality & Complex modelling \\  & not handled natively & \\  & Designed for univariate series & Handle multivariate datasets \\  & Sensitive to outliers & Depend on model complexity \\  & Assume data integrated & Data agnostic \\  & of a finite order & \\  & Handles independent forecasting tasks & Joint forecast of multiple time series \\ \hline \multirow{6}{*}{Complexity} & Low time complexity & Training and validation needed \\  & in general (depending on the model) & \\ \cline{1-1}  & Small computational requirements & Hardware and computational \\ \cline{1-1}  & [61] & demands higher (depending on model) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Advantages and disadvantages of ARIMA over Artificial Intelligence models, regarding time series forecasting tasks.
# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 4 Methodology
### Front-End, SCPD and Embedding Extractor Blocks  
The first block for all the systems under analysis is an MFCC [43] front-end. For a given audio \(\Omega\), a stream of 32 coefficient feature vectors is estimated according to a 25 ms window with a 15 ms overlap. No derivatives are considered.  
Simultaneously, DNN-based Voice Activity Detection (VAD) labels are estimated [44]. The network, consisting of a joint work of convolutional layers and Long-Short Term Memory (LSTMs) [45], estimates a VAD label each 10 ms.  
Both feature vectors and VAD labels are fed into the Speaker Change Point Detection (SCPD) block. This stage, dedicated to infer the speaker turn boundaries, makes use of \(\Delta\)BIC [22], the differential form of BIC. This estimation works in terms of a 6-second sliding window, in which we assume there is, at most, a speaker turn boundary. Each involved speaker in the analysis is modeled by means of a full-covariance Gaussian distribution. Besides, the VAD labels delimit the parts of the audio in which the analysis is performed. In the given data, the described configuration provides segments of an approximately 3-second length on average.  
The identified segments are converted into embeddings using a modification of the extended x-vector [46] based on Time Delay Neural Networks (TDNNs) [47]. The modification is the inclusion of multi-head self-attention [48] in the pooling layer. This network, trained on VoxCeleb1 and 2, provides embeddings of dimension 512. These embeddings undergo centering and LDA whitening (reducing dimension to 200), both trained with MGB as well as the Albayzin training subset, and finally length normalization [49]. These embeddings will be referred to as \(\mathbf{\Phi}\). A similar extraction pipeline working offline is in charge of the enrollment audios. The enrollment embeddings will be named \(\mathbf{\Phi}_{\text{\emph{erroll}}}\).
## 5 Discussion  
### VI Variability  
The existence of cloud causes a drastic decrease of VI values. In particular, NDVI is more sensitive to atmospheric effects than EVI. The contamination of residual clouds leads to the large variation in NDVI values as we can see in Figure 5. Though we used the cloud mask to reduce cloud effects on per-pixel surface reflectance, there are still uncertainties in the MODIS cloud mask [48, 49]. The inclusion of cloud and aerosol corrupted EVI has been found to misidentify the vegetation changing trend [50]. Therefore, the usage of quality assessment information for atmospheric and cloud screening is essential for the reliability of vegetation change detection results. Additional masking may be required to exclude contaminated pixels [51]. These indicate that uncertainties can be caused when using NDVI time series for vegetation variability detection.  
High VI variability (\(>\)30%) can be observed in mountain areas (Figure 7). This might be caused by the highly variable meteorological conditions in this mountainous environment. Medium VI variability (10%-30%) exists in several grassland-dominated areas, as well as in the agricultural areas in the northern XUAR. The variability here might be caused by a mixture of small crop fields  
Figure 8: Histograms of the two phenological metrics detected from VI time series for each vegetation type for 2001–2012 for the XUAR. The red line denotes NDVI, the black line denotes SAVI and the yellow line denotes EVI.  
and changing agricultural practices. The zones with semi-arid to arid conditions in the Taklamankan desert and Gurbantunggut desert show very stable mean annual VI within the observed time period. In particular, EVI exhibits more variability than NDVI and SAVI for the sparsely vegetated areas. For these areas, the soil background is mainly composed of dry sand. NDVI has been reported to be susceptible to the spectral influence of soil texture and moisture in gaps between vegetation for desert grass and desert shrubland [52, 53]. The high annual variability detected from EVI may be related to variation of soil background at low VI levels.
## 2 Materials and Methods
### Monitoring System Design
#### 2.2.2 Sensor and Device Selection  
The choice of sensor devices and products to integrate into the sensing system were based on application and user requirements. These requirements help to guide the selection of appropriate devices to collect data at the desired rate and operate effectively in the deployed environments. The sensors selection criteria for this specific study were:  
* Commercial or consumer grade: The sensors should be purchasable by end-users without considerable modifications for purpose. Development boards, such as Arduino, ESP32, and Raspberry Pi-based alternatives were, therefore, ruled out;
* Avoid vendor lock-in issues: Vendor lock-in refers to situations where the cost of switching is at such a steep cost that the customer is 'locked-in' with the vendor. This is a situation that should be avoided where possible;
* Availability: Due to the global chip shortage and supply-chain issues, in this project, whenever a sensor model is unavailable or with an extended shipping timeframe, the next feasible option will be selected.  
The selection criteria for indoor environment monitoring studies were:  
* Data acquisition in near real-time: Ability to monitor the environment in near real-time (a delay of 1-10 min) grants organisations greater insight into their workplace. Furthermore, it enhances the organisations' ability to adjust on the fly. This can also prolong the battery lifetime of the devices as fewer data transmissions are needed;
* Parameters: The key parameters to be measured to achieve the study's purpose, which include temperature, humidity, pressure, noise, TVOCs, PM\({}_{2.5}\), PM\({}_{10}\), CO\({}_{2}\), and the open and close status of doors;
* Connectivity: Wireless communication between device and gateway should conform to Australian Industry Scientific and Medical (ISM) Band requirements to avoid interference with other electrical and electronic devices operating nearby. These radio bands are typically grouped around 433 MHz and 920 MHz, with the 920 MHz range varying between 915 and 928 MHz. Another typical band is 2.4 GHz which is commonly used for consumer Wi-Fi and Bluetooth. Such a design also makes the framework more accessible for the public as no special operating licence is needed;
* Operation requirements: Each sensing device manufacturer should operate under open standards and/or with API capability for exporting and streaming the data to an external database;
* Physical properties: The form factor of the sensor should be with equivalent footprint as ordinary smoke detectors or HVAC panels to avoid interference and disturbance with day-to-day activities at the monitoring site. Battery-powered devices are highly preferred as they provide the system with extra flexibility in the deployment process. All devices should have wireless networking capability to avoid extra networking wiring cost.
## 3 Experiment
### Performance Evaluation  
To further evaluate the practical applicability of the TGAN, we compare its computational complexity and training efficiency with state-of-the-art methods. As shown in Table 5, the TGAN achieves a balance between model capacity and efficiency. While its calculation complexity (16.9G) is higher than that of lightweight models like the L-CGAN (9.8G), the hybrid Transformer-GAN architecture ensures superior layout quality without excessive resource consumption. The adaptive weight adjustment mechanism further reduces training time by 25% compared to pure Transformer-based approaches (e.g., LayoutTransformer). This demonstrates the TGAN's scalability for larger datasets and real-world deployment scenarios.