esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 4 Applications
### Tai-Tung Fire Bureau Building  
#### 4.2.1 Building Description  
In this section, OLR was implemented with the \(CAV\) and \(R_{CAV}\) as features on Tai-Tung Fire Bureau (TFB) building located at Tai-Tung city in Taiwan. TFB was a four-story reinforced concrete building with a partial basement. The first story height for the building was 5 m (16.4 ft) which was taller than the other stories, 3.5 m (11.4 ft). Furthermore, the first story had fewer partition walls for the purpose of parking fire engines and other large equipment causing the first story to behave as a soft story. TFB was instrumented with 22 accelerometers located as shown in Figure 8. It experienced five earthquakes, listed in Table 6, and suffered from severe damage during the 2006 M 6.2 earthquake (#5 in Table 6). Several columns of the first story and partition walls on first and second stories experienced major damage. The building was deemed not feasible to repair and was subsequently demolished.  
Two MDOF analytical models were developed to simulate the behavior of the lateral force resisting systems in the two directions (X and Y shown in Figure 8) based on details (period, stiffness, damping, etc.) found in [47]. The vibration responses from NTHA of the MDOF models to Set-1 ground motions described in Section 2.1.2 were used to  
\begin{table}
\begin{tabular}{c c c} \hline
**Event \#** & **Date (mm/dd/yyyy)** & **Magnitude** \\ \hline
1 & 8/4/1999 & 4.8 \\
2 & 9/24/2002 & 5.2 \\
3 & 12/10/2003 & 6.4 \\
4 & 12/28/2005 & 4.7 \\
5 & 4/1/2006 & 6.2 \\ \hline \end{tabular}
\end{table}
Table 6: Earthquake events recorded by the sensors of TFB building.  
Figure 8: Sensor locations at TFB building.  
extract damage features and the corresponding damage states. Two training sets of features were generated for the two directions which were subsequently used to develop two OLR models (\(\text{OLR}_{\text{X}}\) and \(\text{OLR}_{\text{Y}}\)). For the test sets, the recorded responses of sensors 7, 9, 16, & 19 were used as Test set-1, to evaluate \(\text{OLR}_{\text{X}}\), and responses of sensors 8, 10, 17, & 20 were used as Test set-2, to evaluate \(\text{OLR}_{\text{Y}}\), where ground floor \(CAV\) and roof \(R_{CAV}\) were paired as features. Three classification tasks were performed using these data sets, namely binary damage detection, multi-class damage assessment, and damage location detection making use of Equation (21) in this practical application.
# A New Method of Time-Series Event Prediction Based on Sequence Labeling
## 2 Definitions and Methods
### CX-LC Framework  
Aiming at the shortcomings of the traditional classification prediction method, which does not consider the timing between subsequence sets, and has a too-high complexity, this paper proposes a new framework for time-series event prediction, the Cluster-XGBoost-LSTM-CRF model (CX-LC). Because it converts the time prediction problem into a labeled problem, this model can also be called a \"labeling\" prediction method.
## 2 Materials and Methods
### Slab Phantom Tests  
The I'mRT Phantom (IBA Dosimetry, Schwarzenbruck, Germany), shown in Figure 1, consisting of slabs of RW3 material of nearly water equivalence, was used. Its central cubic insert was filled in three different ways: homogeneously with RW3 slabs (H configuration) or replacing the central slab with air and bone inserts (A and B configuration, respectively). Square fields with different sizes of 2, 3, 4, 6, 8, 10, 12, and 15 cm were calculated by Monaco 5.11.03 TPS (Elekta Oncology Systems, Crawley, UK) on the three phantom configurations by using 100 Monitor Units (MU) per beam. The beam isocenter was placed at the phantom center for all beams. The accuracy of the entrance fluence reconstructed by the deconvolution kernel algorithm was assessed by comparing the reconstructed entrance fluence of the square fields with the nominal fluence planned by the TPS and delivered.
## 2 Overview of the Study Area
### Mining Status of the Mining Area  
The West Second Mining Area of the Longshou Mine was completed in 2010 and subsequently put into production with a design capacity of 1.65 million t/a, a design ore dilution rate of 7%, a loss rate of 5% and a service life of 20 years [38]. The West Second Mining Area of Longshou Mine was originally designed to adopt the natural caving method. At a late stage of the project construction, due to changes in the economic situation and for other reasons, it was finally decided to change the mining method used at the West Second Mining Area to the lower-layer filling method. The height of the segments is an important structural parameter and an important indicator of the amount of work to be carried out, as is the stability of the roadway and the faults, and multiple segments make up a central section. The lower-layer filling method was formally adopted in the West Second Mining Area in 2012, and the elevations of the two middle sections were 1554 m and 1430 m, and the elevations of the first mining section were 1630 m and 1534 m. The horizontal middle sections of the West Second Mining Area are 1430 m, 1554 m and 1640 m and are composed of multiple sections, with the mining area designed to be simultaneously mined in two middle sections, namely, at 1640 m and 1554 m. In March 2016, due to the influence of the mine-control fault F8 in the mining area, the elevation of the 1554 m middle section, which was east of the eight rows of the 1610 m level and the overburden, was changed to 1610 m. The collodion-filling body and overburdened rocks underwent a large overall collapse, and parts of the production activities went into a state of shutdown.  
In 2018, we carried out an industrialized test of the sublevel caving method without the sill pillar, including research on its supporting system in the West Second Mining Area of Longshou Mine, and divided the remaining ore body in the 1554 m middle section into two stages for mining. The first stage was the test stage, with a mining ore body east of the 1595 m level for eight rows; the second stage was the promotion stage, where after the success of the test stage, the sublevel caving method without the sill pillar could be extended to the mining of all the remaining ore bodies in the 1554 m middle section. In April 2020, the first stage of the test work was completed, the expected indicators were reached, and the start of the second stage was planned. The actual height of the sublevel caving method without the sill pillar was 15 m. The specific structural parameters of the caving method are shown in Figure 2, and the height of the lower-layer filling method was 20 m [34]. The relative position of the ore body, the fault and the exploration line division are shown in Figure 3, below.  
Figure 1: Geological structure sketch of Longshou Mine.
# Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications
## 2 Related Work
In order to account for the changes of vehicles' motion parameters with the platoon control, event-based message generation rules can be considered, which rely on observed changes of the ego or of surrounding vehicles. Such generation rules for the cooperative perception mechanism are discussed in [27], with the application of a similar approach in platooning scenario being presented in [28], where specific service profiles are considered for message generation. Following the idea of an event-triggered scheduling of communications and the need to address specific features of wireless communications, such as the use of scheduling grants in C-V2X, we consider in this work an event-based design of quasi-periodic scheduling of transmissions, where the interval between the transmission of subsequent messages for a platoon vehicle is dynamically selected based on the road situation.
## 5 Experimental Tests of SOM Classifiers of IM Winding Faults
### Results of Stator and Rotor Fault Classification
As the first category, the detection effectiveness of IM individual failures (damaged/undamaged) was assessed. As can be seen from Table 3, the fault detection efficiency is on average 93% for a single fault type. The second category of KN efficiency was aimed at assessing its ability to classify a particular degree of damage. In the case of an unfaulty motor and for rotor faults the classification efficiency is similar to the fault detection accuracy (93-95%). In the case of stator faults, the classification is correct in 74% of cases. This fact is related to slight changes in diagnostic signals due to the increase of fault level. On the Kohonen map the areas characteristic of different numbers of shorted turns changed gradually (Figure 13a). \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Shorted Turns** & **Broken Cars** & **Mixed Faults** & **No Fault** \\ \hline Approximate effectiveness of & 90 \% & 93\% & 72\% & 95\% \\ faults detection & & & & \\ Approximate effectiveness of & 74\% & 93\% & - & 95\% \\ faults classification & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effectiveness of Kohonen network in fault detection and classification. Figure 15: Classification of the rotor failures for no-load operation mode: (**a**) Kohonen map 10 \(\times\) 10: dotted lines—IM damage categories involved in the classification, hatched areas—IM damage categories not involved in the classification; \(N_{sh}\)—number of shorted turns in the stator winding, \(N_{bb}\)—number of broken rotor bars; (**b**) specification of test data parameters; \(T_{L}\)—load torque, \(T_{LN}\)—rated motor torque, \(f_{s}\)—frequency of the supply voltage.
## 7 IoRT Systems Based on Aerial NTN Networks
### Enhanced Throughput/Transmission Rate  
As a solution for the 6G heterogeneous IoT, Clustered Non-Orthogonal Multiple Access (C-NOMA) is proposed in [135] in order to address spectrum efficiency, system throughput, and receiving-end complexity. It uses UAVs as Low-Altitude Platform Stations for localized communication reinforcement and high-altitude platform stations for broader coverage. The study aims to optimize four key factors: spectrum allocation, power control, horizontal coordinates, and hovering altitude for UAV 3D location planning and resource allocation. Simulations and theoretical analysis demonstrate that the proposed two-stage solution substantially improves the system uplink sum rate with relatively low complexity compared to previous methods.  
The study in [136] proposes a Clustered NOMA (C-NOMA) system that utilizes UAVs as aerial base stations for Wireless-Powered Communication (WPC) to maximize the uplink average achievable sum rate of IoT terminals [136]. WPC enables IoT terminals to harvest energy from downlink radio frequency signals and subsequently use the harvested energy to transmit data to the uplink. By optimizing UAV trajectory and subslot allocation in a synergistic manner, the proposed system ensures that the uplink sum rate and UAV mobility constraints are met. The complex and nonconvex optimization problem is efficiently solved using an iterative algorithm. Numerical results validate the effectiveness of this scheme for a UAV-supported C-NOMA system.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **NTN Architecture** & **Description** \\ \hline
[133] & LAP-UAV & Development of a low-cost, UAV-enabled LoRa IoT relay system for enhanced energy efficiency and high-speed environmental data acquisition. \\ \hline
[134] & UAV & Development of a spatial NOMA scheme combined with multiple access and spatial modulation techniques to enhance energy efficiency in 6G wireless networks for massive IoT applications. \\ \hline \hline \end{tabular}
\end{table}
Table 14: IoRT systems based on aerial NTN networks: **enhancement of energy efficiency**.
# Review of Automatic Estimation of Emotions in Speech
## 7 Neural (Machine Learning) Techniques for Emotion Classification of Speech
### Common Architectures for ANNs
#### 7.2.2 Recurrent Neural Networks (RNNs)  
To take better advantage of the uneven distribution of pertinent data in speech across frequency and across time, more selective use of nodes is useful. CNNs exploit data  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Architecture** & **Pluses** & **Minuses** \\ \hline Fully connected & & Excessive number of \\ feedforward (FFNN) & Simple structure & parameters \\ \hline Convolutional neural & & Only operates locally \\ network (CNN) & Smooths local data & Only operates locally \\ \hline Recurrent neural network & & \\ (RNN) & Exploits distant data & Many feedback parameters \\ \hline Attention & Emphasis on correlations & Ignores time ordering \\ \hline \hline \end{tabular}
\end{table}
Table 2: Major ANN components.  
correlations over small local ranges, but recurrence can be helpful for correlations over long durations. RNNs use models with temporal feedback, with distributed hidden states that store information about past input [103]. Network gates (input, forget, and output) control flow of data across layers. Typical RNNs are termed long short-term memory and residual networks (ResNet) [93,104]. For SER, effective high-level features are found with RNNs, which are more robust to long-range contextual effects [105,106]. In addition, multi-scale CNNs can be used to extract temporal and spectral features over variable durations [107, 108, 109, 110].
## 4 Security Requirements in Current Offline Payment Functions
### Offline Payment Function with E-Cash Proposed by [PERSON]  
In the research by [PERSON] et al. [72], the offline payment functions considering the security requirements of no double spending, verifiability, and anonymity were researched, and the following solutions were proposed for the security measure, no double spending.  
To satisfy the security requirements in terms of no double spending, two additional methods were proposed by [PERSON] et al. [72]. The first method proposed in the research was to provide the authority to the bank to identify the double spenders. The second method proposed was to utilize observers to prevent double spending. The major difference between the first and the second method proposed is whether the double spending is identified prior to its occurrence by the bank or after the event by the third-party observer.  
In the first method, the bank becomes authorized to identify the double spender through a cryptographic technology called a Schnorr signature. The detection and identification of the double spender are enabled through the calculation of the secret key in both signatures, as the double spender signs more than one message, whereas it is only possible to detect an event that occurred in the past. However, considering the characteristics of the first method, the preventive measures cannot be taken, whereas it would be possible to detect double spending in advance of its occurrence in the presence of a trustee.  
The second method introducing the trustee, considered to be the third-party observer, in the transaction, is capable of preventing double spending prior to or as soon as it occurs. However, since it requires the additional entity as an observer in the system, it could result in a greater cost.  
Regarding the security requirements related to anonymity and verifiability, [PERSON] et al. [72] stated that fraudulent actions would not occur as long as the offline payment is processed with anonymity among the users. However, under the circumstance where anonymity is violated, fraudulent actions, such as money laundering and blackmail, could occur. Thus, as a measure for the violation, [PERSON] et al. [72] proposed anonymity revocation to verify the identity of the members who participated in the transaction. The anonymity revocation proposed by [PERSON] et al. [72] is to remove the anonymity of the violating users and provide them verifiability for transactions.  
Among the requirements for the offline payment function presented in Section 2, three security requirements are not covered by [PERSON] et al. [72]: unforgeability, non-repudiation, and DDoS attack prevention. It is necessary to devise a new method to satisfy the security requirements of unforgeability and non-repudiation in [PERSON] et al. [72].
# Directed Path 3-Arc-Connectivity of Cartesian Product Digraphs
## 3 A General Lower Bound
: Let \(S=\{x,y,z\}\), \(r=x\), and we only examine the case where \(x,y,\) and \(z\) are not all within the same \(\overrightarrow{C}_{n}(u_{i})\) or the same \(\overleftrightarrow{T}_{m}(v_{j})\) for any \(i\in[n],j\in[m]\). The rationale for the remaining cases follows a similar line of argument. Without loss of generality, let us assume \(x=u_{1,1},y=u_{2,2},z=u_{3,3}\). We can obtain three arc-disjoint \((S,r)\)-paths in \(\overrightarrow{C}_{n}\square\overleftrightarrow{T}_{m}\), say \(P_{1}\) and \(P_{2}\) such that  
\(P_{1}:xu_{2,1}yu_{3,2}z\), \(P_{2}:xu_{1,2}yu_{2,3}z\). Then we have \(2=\min\{\delta^{+}(D),\delta^{-}(D)\}\geq\lambda_{3}^{p}(\overrightarrow{C}_{ n}\square\overleftrightarrow{T}_{m})\geq 2\). This completes the proof. Proposition 9: _We have \(\lambda_{3}^{p}(\overleftrightarrow{C}_{n}\square\overleftrightarrow{T}_{m})=3\), with \(n\geq 3\)._  
Proof. : Let \(S=\{x,y,z\}\), \(r=x\), and we only examine the case where \(x,y,\) and \(z\) are not all within the same \(\overleftrightarrow{C}_{n}(u_{i})\) or the same \(\overleftrightarrow{T}_{m}(v_{j})\) for any \(i\in[n],j\in[m]\). The rationale for the remaining cases follows a similar line of argument. Without loss of generality, let us assume \(x=u_{1,1},y=u_{2,2},z=u_{3,3}\). We can obtain three arc-disjoint \((S,r)\)-paths in \(\overleftrightarrow{C}_{n}\square\overleftrightarrow{T}_{m}\), say \(P_{1}\), \(P_{2}\), \(P_{3}\) such that  
\(P_{1}:xu_{2,1}yu_{3,2}z\), \(P_{2}:xu_{1,2}yu_{2,3}z\), \(P_{3}:xu_{m,1}u_{m,2}u_{m,3}u_{m-1,3}\ldots zu_{3,2}y\). Then we have \(3=\min\{\delta^{+}(D),\delta^{-}(D)\}\geq\lambda_{3}^{p}(\overleftrightarrow {C}_{n}\square\overleftrightarrow{T}_{m})\geq 3\). This completes the proof. Proposition 10: _We have \(\lambda_{3}^{p}(\overrightarrow{T}_{n}\square\overleftrightarrow{T}_{m})=2\)._  
Proof. : Let \(S=\{x,y,z\}\), \(r=x\), and we only examine the case where \(x,y,\) and \(z\) are not all within the same \(\overleftrightarrow{T}_{n}(u_{i})\) or the same \(\overleftrightarrow{T}_{m}(v_{j})\) for any \(i\in[n],j\in[m]\). The rationale for the remaining cases follows a similar line of argument. Without loss of generality, let us assume \(x=u_{1,1},y=u_{2,2},z=u_{3,3}\). We can obtain three arc-disjoint \((S,r)\)-paths in \(\overleftrightarrow{T}_{n}\square\overleftrightarrow{T}_{m}\), say \(P_{1}\) and \(P_{2}\) such that  
\(P_{1}:xu_{2,1}yu_{3,2}z\), \(P_{2}:xu_{1,2}yu_{2,3}z\). Then we have \(2=\min\{\delta^{+}(D),\delta^{-}(D)\}\geq\lambda_{3}^{p}(\overleftrightarrow {T}_{n}\square\overleftrightarrow{T}_{m})\geq 2\). This completes the proof. According to Propositions 1-9, we find that the directed path 3-arc-connectivity of some Cartesian products of digraphs is equal to the minimum semi-degrees. Based on this discovery, we can consider under what conditions the directed path 3-arc-connectivity of Cartesian products of digraphs can be equal to the minimum semi-degrees, which is a problem we can consider next.
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 5 Conclusions
Despite the fact that there is evidence that the coherent component exists, and that it can be related to long waves, such as swell, etc. [19], as the receiver's height increases, the coherent scattering component decreases and the incoherent one increases (Table 1 of [20]). In the incoherent case, the received power is the sum of the different power contributions from the fields scattered from different individual surface patches. Therefore, the temporal averaging of our experimental results can be reasonably extrapolated to other configurations, as long as the averaging time is long enough to include many long waves (i.e., mean values in Table 4). Detectability will have to be assessed separately, after an appropriate compensation for the fraction of the surface occupied by damped waves, and the signal-to-noise ratio including geometry, integration time, and receiver parameters, notably antenna directivity. Conceptualization, all authors; methodology, all authors; software, A.P.-P., A.G. and A.C.; validation, A.G., A.P.-P. and A.C.; formal analysis, A.C.; investigation, A.C.; resources, A.C. and D.P. ; data curation, A.P.-P. and A.G.; writing--original draft preparation, A.C.; writing--review and editing, all authors; visualization, A.G. and A.C.; supervision, A.C. and D.P. ; project administration, D.P. ; funding acquisition, A.C. and D.P. All authors have read and agreed to the published version of the manuscript. This research was funded by ESA project GLobal MonItoring of MicroPlastics using GNSS-Reflectometry (GLIMPS) 4000132488/20/NL/GLC through a subcontract with Deimos Space UK Ltd \"GLIMPS Subcontract DMU-UPC Ref. DMU-CP51 no. 2021-015; and project \"GENESIS: GNSS Environmental and Societal Missions--Subproject UPC\", Grant PID2021-126436 OB-C21, sponsored by MCIN/AEI/10.13039/501100011033/ and EU ERDF \"A way to do Europe\". Selected data sets can be requested from the authors. The authors would like to express their gratitude to [PERSON] who started this activity while she was at Deimos UK, and to [PERSON] (ESA/ESTEC) for his insights and support, and the Deltares staff who supported the execution of the experiment. The authors declare no conflict of interest.
# A New Approach to Border Irregularity Assessment with Application in Skin Pathology
## 3 Results
### Evaluation Metrics
Figure 22: The splitting dilemma: the central region in (**a**) can be treated either as a single rete with two tips as in (**b**) or as two merged retes as in (**c**). Before computing Jaccard indexes we fine-tune the sets of manually and automatically segmented retes. We reject all retes which roots are located at most \(\tau_{\text{L}_{\text{sp}}}=250\,\text{px}\) from main axis endpoints, as edges of a skin specimen are often deformed due to mechanical slicing. Similarly, we reject all retes which fail to meet the following morphometric criteria: (1) their width \(w\) is too small (\(w<w_{\text{min}}\)), and (2) their length \(l\) is too small (\(l<l_{\text{min}}\)). We set \(w_{\text{min}}=25\,\text{px}\) and \(l_{\text{min}}=50\,\text{px}\), as for smaller projections the decision whether to consider it as a rete or just a slicing artifact is arbitrary (even by experienced pathologists). For automatically segmented retes we also reject retes for which their length-to-width ratio is less then 0.5. However, all those three criteria are not applied to automatically segmented retes which are adjacent to holes in epidermis base (as such holes are always signs of bridging between retes). For retes matching exclusively we calculated standard statistical measures describing the distribution of relative errors for both lengths of rete bases and rete lengths. For the true value of a quantity, \(x\), and the inferred value, \(\hat{x}\), the relative error is defined by  
\[\delta x=\frac{\hat{x}-x}{x}. \tag{15}\]