esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Fragility Analysis
#### 4.3.2 Effects of Frequency Contents on the Seismic Vulnerability of the Cabinet  
Figure 15 compares the probability of the exceedance of the acceleration response of the cabinet from the defined damage threshold while subjected to the HF and LF earthquakes. In the case of without TMD, the median acceleration value for HF and LF groups are 0.521 g and 1.699 g, respectively (Table 3). Whereas for the model equipped with TMD, there is a dramatic change in the fragility curves, corresponding to values of 0.932 g and 2.833 g for HF and LF data, respectively. This leads to the conclusion that the structural model under HF motions would be more vulnerable. This can be attributed to the reason that the high-frequency pulses of the ground motions can amplify the structural response of high-frequency structures such as the electrical cabinet.  
Figure 14: Probability of exceedance of the acceleration responses of the cabinet from the collapse threshold: (**a**) HF; (**b**) LF earthquakes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{\(\theta\)} & \multicolumn{2}{c}{\(\beta\)} \\ \cline{2-5}  & **HF** & **LF** & **HF** & **LF** \\ \hline Wo & 0.521 & 1.699 & 0.500 & 0.746 \\ \hline W & 0.932 & 2.833 & 0.403 & 0.592 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of fragility functions.
## 4 Results
### Control Laws for Partial-Load and Full-Load Operating Region  
Before we come to the simulation results, the control laws are briefly presented. Details of the design approach are proposed in [11], for example. The control law in a parallel distributed compensation (PDC) scheme consists of a state and integral error feedback:  
\[u=-\sum_{i=1}^{N_{r}}h_{i}(z)\,K_{x,i}\,\Delta x+\sum_{i=1}^{N_{r}}h_{i}(z)\,k _{I,i}\,x_{I}\, \tag{27}\]  
where  
\[x_{I}=\int\limits_{0}^{t}\left(\omega_{r,ref}-\omega_{r}\right)d\tau=\int \limits_{0}^{t}e\,d\tau \tag{28}\]  
with the control error \(e=\omega_{r,ref}-\omega_{r}\) and the premise variables summarized in \(z=(\,x^{\mathrm{T}}\,,\,\hat{\vartheta}\,)^{\mathrm{T}}\). The state feedback of the full-load region controller is given as  
\[\Delta x=\begin{pmatrix}\omega_{r}-\omega_{r,ref}\\ \beta\end{pmatrix}=\begin{pmatrix}x_{1}-x_{1,ref}\\ x_{2}\end{pmatrix}, \tag{29}\]For the partial-load region, since the pitch angle is zero, only the rotor speed is fed back as a state.  
\[\Delta x=\omega_{r}-\omega_{r,ref}=x_{1}-x_{1,ref}\,. \tag{30}\]  
The control law for power optimization (partial-load region) and limitation (full-load region) differ in the control variable \(u=\{T_{g},\beta_{ref}\}\) and in generation of the reference value \(\omega_{r,ref}\) to be tracked. Thus, let us take a look at this in detail:  
* The partial-load controller adjusts the generator torque \(u=T_{g}\) to track the rotor speed following \(\omega_{r,ref}=\frac{\lambda_{r,total}}{R}\,v\) with \(\lambda_{rated}=\lambda_{opt}\), cf. Equation (3) where the pitch angle is set constantly to zero \(\beta_{ref}=0\). According to Equation (2), this results directly from the control objective of maximizing the rotor power \(P_{r}\) by tracking the rotor speed so that an optimum value \(c_{p,max}=c_{p}(\lambda_{opt})\) is reached. Here, the dynamics is limited by the achievable actuating variable effort (generator torque).
* On the other hand, the controller of the full-load region adjusts the pitch angle set point \(\beta_{ref}\geq 0\) so that the rotor speed is kept to its rated value, i.e., \(\omega_{r,ref}=\omega_{r,rated}\). To obtain rated power \(P_{g,rated}\) if \(v>v_{rated}\), the generator torque is simultaneously kept to its rated value \(T_{g}=T_{g,rated}\).  
Note that the synthesis of control law (27) for both operating regions can be converted separately into a convex programming problem written in terms of linear matrix inequality (LMI) conditions proposed in [11].
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 3 Adaptive Sub-Network Organization  
The devices periodically check the state of their sub-network by transmitting beaconing messages over the short-range network. An _interval period_ signifies the period during which a devices transmits one and only one beacon message. During this period, each device examines received beacons and characterizes as _consistent_ any beacon received from an already known nearby device. As long as the device only receives beacons from consistent neighbors, it remains in the _consistent_ state. If the device receives a beacon from an unknown device, i.e., an _inconsistent_ beacon, the device changes state and becomes _inconsistent_.  
Once the current interval of the device expires, it has to decide the duration of the next interval and therefore the message rate it will maintain. The next interval's duration is doubled when the device is consistent, reducing the messaging rate down to half. If the device is inconsistent, it immediately notifies its neighbors and reduces the next interval's duration to the minimum defined period while the beacon generation rate increases to the maximum defined. In this way, given the appearance of a new neighbor, the device is alerted and starts re-evaluating the state of the network. The actual values of the maximum and minimum intervals are set by the network administrator based on the expected deployment and the anticipated number of changes in the topology in every given period. Notice that the experiments reported in the Section 5 based on real-world deployment provide useful insights on how to set the minimum and maximum intervals and how such settings affect the performance of the network. Every time a device decides to use a new interval, the device sets a future time \(t\) when a re-evaluation of the consistency of the sub-network will be conducted. This future time \(t\) is selected randomly so that nearby devices do not start the re-evaluation of their network concurrently and thus congest the network.
###### Abstract  
This paper proposes an estimation scheme of the number iterations for optimal Gauss-Seidel (GS) pre-coding in the downlink massive multiple input multiple output (MIMO) systems for the first time. The number of iterations in GS pre-coding is one of the key parameters and should be estimated accurately prior to signal transmission in the downlink systems. For efficient estimation without presentations of the closed-form solution for the GS pre-coding symbols, the proposed estimation scheme uses the relative method which calculates the normalized Euclidean distance (NED) between consecutive GS solutions by using the property of the monotonic decrease function of the GS solutions. Additionally, an efficient initial solution for the GS pre-coding is proposed as a two term Neumann series (NS) based on the stair matrix for improving the accuracy of estimation and accelerating the convergence rate of the GS solution. The evaluated estimation performances verify high accuracy in the downlink massive MIMO systems even in low loading factors. In addition, an additional complexity for estimating the number of the optimal iterations is nearly negligible.  
GS; iteration; massive MIMO; NED +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Theoretical Backgrou
#### 2.1.4 Topic Modeling  
Topic modeling is an analytical technique used to discover topics (abstract themes) in a document set. Although one could read a large number of documents and identify their topics, this requires significant time and effort. Topic modeling allows for the identification of topics by analyzing the simultaneous usage patterns of keywords in the text data, thereby automatically extracting the topics, issues, or topic groups that represent the documents. [Figure 3] illustrates typical topic modeling, which is composed of words, topics, and documents. Firstly, words represent the words appearing in each document. Topics are composed of words and the value of their influence in forming the topic. Finally, documents refer to a set of documents subject to topic modeling.  
Latent Dirichlet Allocation (LDA) demonstrates the probability of a specific word appearing in a specific topic. As shown in Figure 3, a document contains various topics. The words in the document are not formed from a specific theme but extracted from the topics represented by yellow, pink, and blue. There are multiple topics in a document.  
Figure 3: Topic Modeling [14]—Seeking life’s bare (genetics) necessities’ refers to data analysis aimed at determining the number of genes necessary for an organism to survive within the framework of evolution. The words marked in blue, such as ‘computer’ and ‘prediction’, relate to data analysis. Those marked in pink, like ‘life’ and ‘organism’, pertain to evolutionary biology. The terms highlighted in yellow, ‘sequenced’ and ‘genes’, are associated with genetics.  
From these topics, we can determine which words were primarily used. Based on this, we can estimate the extent to which topics are included in a document. Each topic is a distribution of words. Thus, in the case of the yellow topic, the probability of the word \"gene\" appearing is 0.04, \"dnas\" is 0.02, and \"genetic\" is 0.01. This suggests that the topic marked with yellow is likely to be related with genetics. By looking at the entire document and the values of each topic, we can see that there are more words corresponding to the yellow topic than those corresponding to the pink and blue topics. From this, we can infer that the main topic of the document is related to genetics represented by the yellow topic.  
Topic modeling is divided into matrix factorization-based methods and probability-based methods. The matrix factorization-based methods apply mathematical techniques like singular value factorization to large matrices containing statistical information from the corpus, such as word frequency, to reduce the dimensions of vectors in the matrix. Meanwhile, probability-based methods estimate the probability of a word existing in a specific topic and a specific topic existing in a document. Latent Semantic Analysis (LSA), based on Singular Value Factorization (SVD), is an example of matrix factorization-based topic modeling, while LDA is an example of the latter probability-based topic modeling.
###### Abstract  
Hepatitis, most importantly hepatitis B and hepatitis C, is a significant global health concern, requiring an accurate and early diagnosis to prevent severe liver damage and ensure effective treatment. The currently employed diagnostic methods, while effective, are often limited in their sensitivity, specificity, and rapidity, and the quest for improved diagnostic tools is ongoing. This review explores the innovative application of Raman spectroscopy combined with a chemometric analysis as a powerful diagnostic tool for hepatitis. Raman spectroscopy offers a non-invasive, rapid, and detailed molecular fingerprint of biological samples, while chemometric techniques enhance the interpretation of complex spectral data, enabling precise differentiation between healthy and diseased states and moreover the severity/stage of disease. This review aims to provide a comprehensive overview of the current research, foster greater understanding, and stimulate further innovations in this burgeoning field. The Raman spectrum of blood plasma or serum provides fingerprints of biochemical changes in the blood profile and the occurrence of disease simultaneously, while Raman analyses of polymerase chain reaction/hybridization chain reaction (PCR/HCR)-amplified nucleic acids and extracted DNA/RNA as the test samples provide more accurate differentiation between healthy and diseased states. Chemometric tools enhance the diagnostic efficiency and allow for quantification of the viral loads, indicating the stage of disease. The incorporation of different methodologies like surface enhancement and centrifugal filtration using membranes provides the ability to target biochemical changes directly linked with the disease. Immunoassays and biosensors based on Raman spectroscopy offer accurate quantitative detection of viral antigens or the immune response in the body (antibodies). Microfluidic devices enhance the speed of detection through the continuous testing of flowing samples. Raman diagnostic studies with massive sample sizes of up to 1000 and multiple reports of achieving a greater than 90% differentiation accuracy, sensitivity, and specificity using advanced multivariate data analysis tools indicate that Raman spectroscopy is a promising tool for hepatitis detection. Its reproducibility and the identification of unique reference spectral features for each hepatic disease are still challenges in the translation of Raman spectroscopy as a clinical tool, however. The development of databases for automated comparison and the incorporation of automated chemometric processors into Raman diagnostic tools could pave the way for their clinical translation in the near future.  
Keywords:Raman spectroscopy; surface-enhanced Raman spectroscopy; chemometric analysis; hepatitis diagnosis; biomarker detection +
Footnote †: journal: Applied Sciences
## 3 Results
### Case Study: Proof-of-Concept Demonstration
traffic were traveling at normal speeds because residents would have the entire Denver Metro Area to find friends, family, hotels, or shelters. Results from the social media filter juxtaposed with the evacuation maps highlight the role the public plays in bridging information gaps between official reporting and Geographic Information System (GIS). Tweets from evacuees provide destinations and public perceptions about evacuation messaging that can be used in modeling. Furthermore, more efficient evacuation routes can be found using machine learning of observed traffic patterns from cameras, real-time navigational tools, and personal tweets. Furthermore,  
Figure 6: Better understanding evacuations: Utilizing data analytics to advance understanding of bottlenecks during natural disasters such as during the Marshall fire in Boulder County in December of 2021. The blue polygon is the Marshall fire perimeter. White lines are municipal boundaries within the evacuation zone. Gray lines represent the transport network. The first panel shows the normal traffic conditions where a driver can easily escape the fire perimeter within 20 min. The second panel shows moderate traffic conditions where the average travel speed is 8 miles per hour. The third panel shows heavy traffic conditions moving at an average of 4 miles per hour. Four miles per hour is approximately walking speed, showing that there are locations where it would be difficult to escape the fire. tweets from official sources can highlight key breakdowns in communication and the lack of cross-entity coordination during the evacuation. The results of our analysis that overlaid major road networks with increased fire hazard, characterized by larger and more frequent fires in the future as a result of climate change [36], shows that more places in the southeastern, midwestern and northeastern United States are likely to experience increased burden on infrastructure for evacuations (Figure 8). Figure 8: Climate-driven changes in future fire: We show increases in fires per year and burned area and as percent change between 1990–2019 and 2020–2060 with current major USA road networks to identify where roads are in the context of increasing fire hazard. Figure 7: Twitter in a disaster: Information content from social media can provide critical context to understand evacuation patterns during natural disasters, such as the Marshall fire in Boulder County in 2021.
# Pressure Fluctuation and Flow-Induced Noise of the Fin and Rudder in a Water Tunnel
###### Abstract  
The flow field and radiated noise resulting from water flowing through a fin and rudder were analyzed in this study. A hydrodynamic experiment was conducted in a water tunnel to measure the pressure fluctuations affecting a fin and rudder, and then the experimental data and Large Eddy Simulation (LES) results were compared and analyzed. The discussion presented herein focuses on the zero angle of attack and the Reynolds number based on a maximum width of the fin and rudder ranging from 3.6 \(\times\) 10\({}^{6}\) to 9.7 \(\times\) 10\({}^{6}\). Furthermore, a numerical model was developed using the LES turbulence model and [PERSON]'s acoustic analog theory to predict the flow-induced noise generated by the fin and rudder. The test data reveal that the pressure fluctuation decreases as frequency increases, and the average rate of decrease is obtained for frequencies up to 5.0 kHz. Additionally, as flow velocity increases, the overall sound pressure level of flow-induced noise also increases. The relationship between the sound power radiated by the fin and rudder and the flow velocity approximately follows a power law with an exponent of seven, and the noise radiated on both sides is greater than that radiated in the direction of flow. The findings presented in this paper have practical implications for designing quieter rudders and optimizing the noise performance of underwater vehicles and ships, thereby addressing concerns regarding the impact of anthropogenic noise on marine life and ecosystems.  
fin and rudder; flow-induced noise; large eddy simulation; Lighthill's acoustic analogy; pressure fluctuation +
Footnote †: journal: Applied
## 2 Related Works
### Single DSR Technology  
Single depth map super-resolution technology refers to recovering a high-quality depth map from a single low-resolution depth map as input. The difficulty of this method lies in how to restore details and structure information from LR depth images that lose part of the depth information.  
For traditional methods, techniques such as interpolation, filtering, and optimization are usually used to improve the resolution of the depth image. [PERSON] et al. [16] use Markov Random Field to optimize high-resolution edge maps constructed from edges of low-resolution depth images. Guided by the HR edge map, an improved joint bilateral filter is used to upsample the HR depth map. This not only avoids the generation of depth artifacts due to texture prediction, but also preserves sharp edges. [PERSON] et al. [17] learn multiple residual dictionaries from a single external image and utilize a shape-adaptive weighted median filter along the edges of the depth map to remove artifacts. [PERSON] et al. [18] apply a pair of sparse dictionaries to obtain the edge information of high-quality depth images. Then, guided by the edge information, they use an improved joint bilateral filter to interpolate the depth image.  
These traditional approaches help to improve the resolution and quality of depth images, but they often have limitations in capturing details and recovering accurate depth information compared to more advanced deep learning-based methods. Depth super-resolution based on deep learning utilizes deep convolutional neural networks, usually using an end-to-end progressive network to gradually restore high-quality depth maps. For example, [PERSON] et al. [13] regard the depth map super-resolution task as a series of super-resolution subtasks, and reconstruct high-quality depth maps by synthesizing these subtasks. [PERSON] et al. [14] cascade multiple dense residual blocks to design a pyramid structure based on deep dense residual networks. This structure utilizes dense skip connections to extract image features while employing residual learning to reconstruct high-quality depth maps. [PERSON] et al. [19] incorporate depth supervision into an iterative residual learning framework, using the total generalized variation (TGV) term and consistency loss to further refine the obtained HR depth map. The depth controllable slicing network proposed by [PERSON] et al. [20] divides the depth map into a series of depth slices for learning, weighted by a distance-aware method, and adaptively fuses slices of different depths to generate high-quality depth maps.
# Assessment of BeiDou-3 and Multi-GNSS Precise Point Positioning Performance
###### Abstract  
With the launch of BDS-3 and Galileo new satellites, the BeiDou navigation satellite system (BDS) has developed from the regional to global system, and the Galileo constellation will consist of 26 satellites in space. Thus, BDS, GPS, GLONASS, and Galileo all have the capability of global positioning services. It is meaningful to evaluate the ability of global precise point positioning (PPP) of the GPS, BDS, GLONASS, and Galileo. This paper mainly contributes to the assessment of BDS-2, BDS-2/BDS-3, GPS, GLONASS, and Galileo PPP with the observations that were provided by the international Global Navigation Satellite System (GNSS) Monitoring and Assessment System (iGMAS). The Position Dilution of Precision (PDOP) value was utilized to research the global coverage of GPS, BDS-2, BDS-2/BDS-3, GLONASS, and Galileo. In particular, GPS-only, BDS-2-only, BDS-2/BDS-3, GLONASS-only, Galileo-only, and multi-GNSS combined PPP solutions were analyzed to verify the capacity of the PPP performances in terms of positioning accuracy, convergence time, and zenith troposphere delay (ZTD) accuracy. In view of PDOP, the current BDS and Galileo are capable of global coverage. The BDS-2/BDS-3 and Galileo PDOP values are fairly evenly distributed around the world similar to GPS and GLONASS. The root mean square (RMS) of positioning errors for static BDS-2/BDS-3 PPP and Galileo-only PPP are 10.7, 19.5, 20.4 mm, and 6.9, 18.6, 19.6 mm, respectively, in the geographic area of the selected station, which is the same level as GPS and GLONASS. It is worth mentioning that, by adding BDS-3 observations, the positioning accuracy of static BDS PPP is improved by 17.05%, 24.42%, and 35.65%, and the convergence time is reduced by 27.15%, 27.87%, and 35.76% in three coordinate components, respectively. Similar to the static positioning, GPS, BDS-2/BDS-3, GLONASS, and Galileo have the basically same kinematic positioning accuracy. Multi-GNSS PPP significantly improves the positioning performances in both static and kinematic positioning. In terms of ZTD accuracy, the difference between GPS, BDS-2/BDS-3, GLONASS, and Galileo is less than 1 mm, and the BDS-2/BDS-3 improves ZTD accuracy by 20.48% over the BDS-2. The assessment of GPS, BDS-2, BDS-2/BDS-3, GLONASS, Galileo, and multi-GNSS global PPP performance are shown to make comments for the development of multi-GNSS integration, global precise positioning, and the construction of iGMAS.  
BeiDou-3; international GNSS Monitoring and Assessment System (iGMAS); PDOP; precise point positioning (PPP); positioning accuracy; convergence time; ZTD +
Footnote †: journal: Sensors