esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results  
### Impact Resistance Performance Simulation in the Capture Phase  
To show the performance of the proposed controller, simulations are carried out on a planar space robot with the RSEA and target spacecraft systems shown in Figure 3. The actual parameters of the system are as follows: \(m_{0}=80\) kg, \(m_{1}=5\) kg, \(m_{2}=5\) kg, \(m_{8}=30\) kg; \(l_{0}=30\) kg\(\cdot\)m\({}^{2}\), \(I_{1}=3\) kg\(\cdot\)m\({}^{2}\), \(I_{2}=3\) kg\(\cdot\)m\({}^{2}\), \(I_{8}=15\) kg\(\cdot\)m\({}^{2}\), \(I_{1m}=0.05\) kg\(\cdot\)m\({}^{2}\), \(I_{2m}=0.05\) kg\(\cdot\)m\({}^{2}\); \(k_{1m}=k_{2m}=1000\) N\(/\)m; \(l_{0}=1\) m, \(l_{1}=l_{2}=2\) m, \(d_{1}=d_{2}=1\) m.  
The equivalent stiffness of joints [30] is as follows:  
\[\mathbf{K}=2\mathbf{K}_{m}(3R^{2}+r^{2})(2\cos^{2}\mathbf{\varphi}-1) \tag{46}\]  
where \(\mathbf{K}_{m}=\text{diag}(k_{1m},k_{2m})\), \(R=0.1\) m, \(r=0.01\) m. \(\mathbf{\varphi}\) is the angle of sweeping arm when the force \(F=[20\) N\(\cdot\)m, \(20\) N\(\cdot\)m\(,0]^{\mathrm{T}}\) acting on the end of the space manipulator, select \(\mathbf{\varphi}=\text{diag}(3^{\circ},2^{\circ})\).  
In order to verify impact resistance performance simulation in the capture phase, the space robot system with/without RSEA device was used to carry out acquisition simulation tests on spacecraft with different velocity. The simulation results are shown in Table 1.  
In Table 1, the first column of velocity terms, the first two are linear velocities, and the third is angular velocities. In the second and third columns, the preceding and the following items are the impact torques of joints without and with RSEA devices respectively. The fourth column has the maximum percentage reduction in joint impact torque with the RSEA device. As can be seen from Table 1, for the capture phase of spacecraft at different initial velocities, the configuration of RSEA device can effectively reduce the impact torque acting on joints, and effectively realize the protection of the joint.
## 5 Comparative Analysis  
This section deals with the detail comparison among different topologies based on various performance indicators such as total number of switches, total blocking voltage per voltage (TBV/V), total device rating per power (TDR/P), and total harmonics distortions (THDs). The first important comparable parameter is number of switches. This parameter has a direct relationship with the cost and size of a converter topology. For this reason, to design a converter topology, it must be ensured that the proposed cycloconverter topology uses the lowest amount of switches.  
From Figure 17a, it is clear that the proposed topology uses the second-lowest number of switching devices. On the other hand, [PERSON] [10] and Idris [13] topologies use the lowest number of switches, and [PERSON] [13] also uses 4 diodes. The lowest number of switches has been observed for [PERSON] [11], [PERSON] [12], and Khedekar [14] topologies.  
Figure 17: Comparative analysis among different cycloconverter topologies based on (**a**) no. of switches, (**b**) TBV/V, (**c**) TDR/P, and (**d**) THDs.  
The other significant indicator is total blocking voltage/voltage (TBV/V), which also has a direct impact on the cost of a converter topology. To minimize the cost of the converter topology, the TBV/V has to be kept as small as possible. A comparison among different cycloconverter topologies based on TBV/V is presented in Figure 17b. This figure indicates that the proposed topology has the lowest TBV/V, whereas the highest number is observed for [PERSON] [11], Agarwal [12], and Khedekar [14]. Therefore, the proposed topology will offer a low cost for designing.  
Another parameter is total device rating per power (TDR/P), which is calculated by the following equation:  
\[\text{TDR}=\sum_{i=1}^{sw}(V_{si}*I_{si}) \tag{11}\]  
where \(V_{si}\) and \(I_{si}\) are the voltage and current of a switching device, respectively. TDR also has a direct impact on the size and cost of a converter. The large value of TDR/P means a high cost and size of the converter topology. Therefore, as TDR/P becomes smaller, the topology is more cost effective. Figure 17c presents the comparative analysis among different types of cycloconverter topologies based on total device rating per power (TDR/P), and also illustrates that the proposed topology achieves less than the other mentioned topologies. Therefore, in the case of TDR/P, the proposed topology offers the optimal performance.  
The last comparable indicator is the total harmonics distortions (THDs), which reduces the lifetime of switching devices. Besides, the high value of THDs creates heating losses and can cause false triggering. Therefore, for the design of cycloconverter, achieving a low value of THDs is essential in this era. Figure 17d displays comparative results among different cycloconverter topologies based on THDs. It is observed from Figure 17d that the proposed topology has the lowest amount of total harmonics distortions than other topologies, which indicates that the proposed topology drives the systems to the finest performance in the case of THDs.
## 4 Performance Analysis of Islanding Detection Methods
### Non-Detected Zone  
The non-detected zone can be defined as the space of power mismatch in which the islanding detection method can fail to detect the islanding, as presented in Figure 4. Therefore, when the power of DGs matches the load power, the deviation amount of voltage and frequency can be very small, which affects the detection effectiveness greatly. Passive methods have large NDZ compared to active methods, and their effectiveness is lower in this aspect.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Drift Detection Algorithms
#### 6.2.4 Semi- and Unsupervised  
Especially for novel class detection, we find several clustering-based algorithms. Within the OLINDA method, a \(k\)-means algorithm is executed as soon as incoming data points cannot be assigned to one of the existing clusters of the classes [164]. The produced clusters are then verified. A valid cluster must be cohesive, meaning that instances must meet a similarity threshold. Further, the number of instances within a cluster must exceed a minimum number. Depending on the location of the new cluster, an existing class (macro level) is extended by this cluster (micro-level), or a concept drift is recognized. The MINAS algorithm works similarly. Here, a cluster is connected to an existing class's cluster if the distance of the centroids does not exceed a predefined threshold [165].  
Accordingly, a grid-based clustering approach can handle changes in the data stream and emerging classes [166]. The Evolving Micro Clusters (EMC) algorithm determines class memberships by grid-based evolving microclusters. For this method, a grid is applied over the entire data search space. Each observation is assigned to a cell within the grid. New classes can be detected by measuring the density of data points in the grid. Accordingly, grid density-based clustering approaches can be used to detect changes in the input data stream [140]. The grid density information is used to sample the training instances of the actual classifier. When a grid reaches a predefined density threshold, the grid is considered for training. Areas that do not reach this threshold indicate changes in the stream or outliers. Depending on the individual situation, clusters are re-formed, split, or combined when a new dense grid emerges. Following the same idea, the SAND classification algorithm also uses the grid's density to detect new classes [142]. Additionally, the algorithm contains a drift detection method based on the confidence values of the classification algorithm.
## 3 Results
### Interacting with the GDC Public Apis  
We search and extract data and other information from the GDC through its public APIs. In particular we use three main API endpoints:  
* _cases_: to find all files related to a specific case (i.e., sample donor);
* _files_: to find all files with specific characteristics such as the file name, MD5 checksum and data format;
* _data_: to download GDC data files.  
As an example of interplay among two of these endpoints, consider a scenario where we want to download all publicly available _Gene Expression Quantification_ data for the tumor _Breast Invasive Carcinoma_ in the context of the _TCGA_ program. First, we query the GDC for all file unique identifiers (UUIDs) related to this particular case. To this end, we make an HTTP POST request to the _files_ endpoint. As a result, the GDC returns a list of file UUIDs (_file_id_ fields). Starting from this list, we then download the associated files; this is done by querying the _data_ endpoint specifying a single file UUID, e.g., [[https://api.gdc.cancer.gov/data/1837](https://api.gdc.cancer.gov/data/1837) ad2a-4 edf-4d80-9050-f78115e54454]([https://api.gdc.cancer.gov/data/1837](https://api.gdc.cancer.gov/data/1837) ad2a-4 edf-4d80-9050-f78115e54454) (i.e., one HTTP GET request for each result _file_id_ in the previous query response). For a detailed description about the syntax of the payload and the other ways to query the GDC, the interested reader may refer to the GDC API documentation available at [[https://docs.gdc.cancer.gov/](https://docs.gdc.cancer.gov/)]([https://docs.gdc.cancer.gov/](https://docs.gdc.cancer.gov/)). For additional details about the OpenGDC software and its usage we point the reader to the user guide and readme file, available as Supplementary File 2 and Supplementary File 3, respectively.  
Figure 2: OpenGDC architecture. Graphical representation of the flowchart describing the OpenGDC software architecture. Every feature is differentiated in two pipelines, i.e., Download and Convert, represented by red and green arrows, respectively. Software modules are additionally enclosed in a dotted line to delineate their function (i.e., User Interface, Instruction Redirection, Genomic Data Commons APIs, External Data Sources, and the List of Parsers).
## 3 Results  
### Patient Characteristics  
94 patients met the inclusion criteria and were included in this retrospective analysis of patients treated between 2009 and 2017 (50 patients at the main COH site and 44 at the satellite sites). Clinicopathologic patient characteristics are presented in Table 1. Majority of patients were p16 positive (\(n=72,77\%\)) with primary sites including base of tongue, tonsil, or oropharynx not otherwise specified (Figure 1A). The variables were well-balanced between the two groups, except for T stage demonstrating a significantly higher rate of T3/4 disease treated at the main campus and T0/T2 disease treated at the satellite sites (\(p=0.008\), Table 1 and Figure 1B).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Main Campus** & **Satellite Sites** & \(p\)**-Value** \\  & & \(n\)**, \%** & **\(n\)**, \%** & **\(p\)**-Value** \\ \hline Chemotherapy agent & Cisplatin & 39 (78) & 21 (48) & \(p=0.001\) \\  & Carboplatin & 4 (8) & 1 (2) & \\  & Cetuximab & 6 (12) & 21 (48) & \\  & Platinum/Paclitaxel & 1 (2) & 1 (2) & \\ Chemotherapy drug change & & 5 (10) & 2 (5) & \(p=0.31\) \\ Chemotherapy interruption/missed dose & & 11 (22) & 9 (20) & \(p=0.86\) \\ Chemotherapy dose reduction & & 4 (8) & 3 (7) & \(p=0.83\) \\ Radiation therapy interruption & & 7 (14) & 6 (14) & \(p=0.96\) \\ PEG tube placement & Yes & 12 (24) & 24 (55) & \(p=0.002\) \\  & No & 38 (76) & 20 (45) & \\  & & 4 (33) & 20 (83) & \(p=0.003\) \\ PEG tube dependency & \textgreater{}6 months & 2 (17) & 11 (46) & \(p=0.08\) \\ Weight loss during treatment & \textless{}10\% & 37 (74) & 26 (59) & \(p=0.12\) \\  & \textgreater{}10\% & 13 (26) & 18 (41) & \\ Weight loss baseline to 6 months & \textless{}10\% & 24 (48) & 14 (32) & \(p=0.11\) \\ post-treatment & \textgreater{}10\% & 26 (52) & 30 (68) & \\ Hospitalizations during treatment & & 6 (12) & 4 (9) & \(p=0.65\) \\ Treatment Response & Complete response & 35 (70) & 37 (84) & \(p=0.24\) \\  & Partial response & 13 (26) & 5 (11) & \\  & Progressive/persistent disease & 2 (4) & 2 (5) & \\ Recurrent disease & & 5 (10) & 7 (16) & \(p=0.39\) \\
3-month narcotic use & Yes & 16 (32) & 13 (30) & \(p=0.98\) \\  & No & 34 (68) & 25 (57) & \\  & Unknown & 8 (16) & 6 (13) & \\
6-month narcotic use & Yes & 9 (18) & 10 (23) & \(p=0.83\) \\  & No & 33 (66) & 28 (64) & \\  & Unknown & 8 (16) & 6 (13) & \\ Progression-free survival & 2-year & 47 (94) & 37 (84) & \(p=0.12\) \\  & 5-year & 46 (92\%) & 37 (84) & \(p=0.23\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Treatment and functional outcomes.
## 4 Discussion
### Variant 2: Determination of the Overturning and Stabilizing Moments  
Generally, when the tower crane is not in service and the slewing platform device is not locked, the crane jib can rotate freely with the wind direction, as shown in Figure 9. The crane operator must unlock the slewing platform mechanism when the wind speed increases above 20 m/s. However, if the wind speed, measured at the jib level using a cup anemometer, is close to this value but does not exceed it, it is possible that the situation when the crane jib is in the position against the wind as depicted in Figure 10. It is also possible that the slewing platform mechanism is locked. This potentially means that the crane is in service, and the additional payload influences the crane's global balance.  
In this case, the values of the overturning and stabilizing moments are shown in the tables below, Tables 7 and 8. In Table 8, the sum of the overturning moments is shown using the bold font.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Counter-Jib, Counterweight,** & **Tower** & & & & **Central Ballast** & **Cruciform Base** & & & \\
**Slewing Platform** & & & & & & & & & & \\ \hline Mass & & & & & & & & & & \\ \hline Q10 & Q11 & Q12 & \(QT1\) & \(QT2\) & \(QT3\) & \(QB1\) & \(QB2\) & \(QK1\) & \(QK2\) & \\
9750 & 2190 & 3310 & 2450 & 2450 & 3320 & 30,000 & 30,000 & 1950 & 1730 & (kg) \\
95,648 & 21,484 & 32,471 & 24,035 & 24,035 & 32,569 & 294,300 & 294,300 & 19,130 & 16,971 & (N) \\ \hline Distance from the tipping line & & & & & & & & & & \\ \hline \(l10\) & \(l11\) & \(l12\) & \(l11\) & \(l2\) & \(l13\) & \(lb1\) & \(lb2\) & \(lk1\) & \(lk2\) & \\
12.91 & 9.30 & 2.57 & 2.45 & 2.45 & 2.45 & 3.85 & 1.05 & 2.45 & 2.45 & (m) \\ \hline Moments & & & & & & & & & & \\ \hline \(Ml10\) & \(M11\) & \(M12\) & \(M11\) & \(M2\) & \(M3\) & \(Mb1\) & \(Mb2\) & \(Mk1\) & \(Mk2\) & \\
1,234,338 & 199,850 & 83,372 & 58,885 & 58,885 & 79,795 & 1,133,055 & 309,015 & 46,867 & 41,580 & (Nm) \\ \hline \hline \end{tabular} where Q10—counterweight mass, \(Q11\)—counterweight jib mass, \(Q12\)—turntable device mass, \(QT1\)—mass of the 1 st part of the tower, \(QT2\)—mass of the 2 nd part of the tower, \(QT3\)—mass of the 3 rd part of the tower with turntable, \(QB1\)—mass of the right lower ballast, \(QB2\)—mass of the left lower ballast, \(QK1\)—mass of the 1 st part of the base cruciform, and \(QK2\)—mass of the 2 nd part of the base cruciform.  
\end{table}
Table 6: The values of the masses—stabilizing moments, variant 1.  
Based on the schemas of the mass distributions, shown in Figures 9 and 10 for Variants 1 and Variants 2, the sums of the overturning and stabilizing moments are determined and collected in Table 9. In the further analysis, Variants 1 with payload (Case 1, Table 9) and Variants 2 without payload (Case 4, Table 9) are considered.
## 2 System Description
### Transceiver Structure  
The transceiver structure of the proposed CPM UWA communication system based on Laurent decomposition is shown in Figure 1. As shown in Figure 2, a frame structure of the transmitted MSK UWA communication signal is designed to construct the circulant matrix and estimate the channel. The transmission sequence **x** = {\(x_{-M}\),..., \(x_{-1}\), \(x_{0}\),..., \(x_{N-2}\), \(x_{N-1}\)} comprises a CP, transmission information, and tail symbol, where \(x_{n}\)\(\in\) {\(\pm 1\)}. The transmission information sequence has a length of \(L\). A unique word sequence is employed to construct the CP **x**uw. As a recommendation, the length \(M\) of CP should be set to be longer than the maximum multipath delay of the channel, where the start and end states of **x**uw should be equal to zero. With the addition of the tail symbol sequence of length \(S\), the inclined phase path of **x** returns to zero at \(n\) = \(N-M\), so that the phase continuity of the CPM signal is preserved, even after adding CP.  
Figure 1: Block diagram of the CPM with the PF-SCFDE UWA communication system.  
Figure 2: Complete frame structure.  
After the CPM signal has been transmitted through the UWA channel, it can be described as  
\[r(t)=\int_{-\infty}^{+\infty}h(\tau)s(t-\tau)d\tau+z(t) \tag{4}\]  
where \(h(t)\) is the time-domain response of the UWA channel and \(z(t)\) is the zero-mean additive white Gaussian noise with variance \(N_{0}\).  
At the receiver, the signal is passed through a bandpass filter first. Since we only consider the equalization algorithm, it is assumed that the received signal is synchronized and Doppler-compensated perfectly. [PERSON] et al. [12] reported that the matched filter based on the Laurent decomposition of the CPM signal comprises a low-pass filter and a CPM coherent receiver based on \(c(t)\). Passing the received sequence \(\mathbf{r}\) through the matching filter, the sequence can be expressed as  
\[\begin{array}{l}r_{n}=\int_{-\infty}^{+\infty}r(t)c(t-nT)dt\\ =\sum\limits_{k}s(n-k)h_{k}+z_{n}\end{array} \tag{5}\]  
\[\begin{array}{l}s_{n}\triangleq\int_{-\infty}^{+\infty}s(t)c(t-nT)dt\\ z_{n}\triangleq\int_{-\infty}^{+\infty}z(t)c(t-nT)dt\end{array} \tag{6}\]  
With respect to the sparse nature of the UWA channel and the CP-based channel estimation method proposed by [PERSON] et al. [17], the front-end CP and the tail of \(\mathbf{r}\) are extracted and combined before using the orthogonal matching pursuit (OMP) algorithm [24] to estimate the time-domain channel impulse response.
## 3 Materials and Methods
### Simulations
#### 3.1.1 CoMTUS Discontinuous Effective Aperture  
Previous results showed that the discontinuous effective aperture obtained by CoMTUS determines the quality of the resulting image [9; 11]. To investigate the effects of the discontinuous aperture, determined by the relative location of the CoMTUS arrays in space, different CoMTUS systems with the arrays located at different spatial locations were modelled. Simulations were performed in the same control medium, where only soft tissue material was considered. To modify the relative location of the probes while keeping the imaging depth (fixed at 75 mm), the angle between the arrays was changed. The array T1 was always positioned at the centre of the \(x\)-axis of the simulation grid, while the array T2 was rotated around the centre of the propagation medium. Then, different cases of CoMTUS with two arrays located at different angles (\(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\) and \(75^{\circ}\)) were simulated. Figure 3a shows a schematic representation of the probes in space, where the different spatial parameters (angle between probes, \(\theta\), and gap, \(Gap\), in the resulting effective aperture, \(Ef\)) are labelled. Note that, at larger angles, both the effective aperture of the system defined by both probes and the gap between them increase. The relationships between probe position and the resulting effective aperture and gap are shown in Figure 3b.
# Multiparameter Approach for Damage Propagation Analysis in Fiber-Reinforced Polymer Composites
###### Abstract  
Assessing the damage evolution in carbon-fiber-reinforced polymer (CFRP) composites is an intricate task due to their complex mechanical responses. The acoustic emission technique (AE) is a non-destructive evaluation tool that is based on the recording of sound waves generated inside the material as a consequence of the presence of active defects. Proper analysis of the recorded waves can be used for monitoring the damage evolution in many materials, including composites. The acoustic track associated with the entire loading history of the sample or the structures is usually followed by using some descriptors, such as the amplitude of the sound waves and the number of counts. In this study, the acoustic emission in CFRP single-lap shear joints was monitored by using a multiparameter approach based on the contemporary analysis of multiple features, such as the absolute signal level (ASL), initiation frequency, and reverberation frequency, to understand whether a proper combination of them can be adopted for a more robust description of the damage propagation in CFRP structures. For selecting the best features, principal component analysis (PCA) was used. The selected features were classified into different clusters using fuzzy c-means (FCM) data clustering for analyzing the damage modes.  
acoustic emission; CFRP; fuzzy c-means; principal component analysis (PCA); initiation frequency; reverberation frequency; single lap shear (SLS)
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 1 Introduction
CoAP realizes a subset of REST that is common with the Hypertext Transfer Protocol (HTTP), but is optimized for Machine-to-Machine (M2M) applications, such as smart energy and building automation. With the introduction of CoAP, a complete open standard networking stack suitable for constrained devices is now available [7]. Constrained devices are turned into embedded web servers that make their resources accessible via the CoAP protocol. In many IoT application domains, the devices need to be addressed in groups in addition to being addressable individually. For example, in a smart building, it should be possible to manually or automatically control the opening of all window shutters at a certain side of the building based on the position of the Sun. This operation of the shutters as a group should not hinder the ability of the user to control a single shutter individually or to control only those shutters that belong to his or her room. Two main approaches are currently being proposed in the IoT community for CoAP-based group communication. The main difference between these two approaches lies in the underlying communication type: multicast versus unicast. The experimental standard for CoAP group communication [8] relies on Internet Protocol Version 6 (IPv6) multicasts, while our approach proposed in [9] relies on IPv6 unicast messages. The main contributions of this article are the following:  
* An experimental evaluation of CoAP-based multicast and unicast group communication solutions using two wireless sensor testbeds representing different testing environments. The first testbed is located in a large, shielded room, which allows testing under controlled environments. The second testbed is located inside an operational office building and thus allows testing under normal operation conditions. We highlight the pros and cons of each of the group communication approaches. To our knowledge, no other work has done such evaluations at a large scale. * A novel hybrid group communication approach providing flexibility regarding which method, unicast or multicast, is being used. This flexibility can better deal with varying use case requirements. * An extension of our group management framework enabling the use of CoAP for the management of the multicast group membership. Using this simple extension, it becomes possible to ask CoAP-enabled devices to join and leave multicast groups without the need for manual intervention or a dedicated management protocol. This is an essential feature in dynamic contexts, where group membership is changing frequently. The remainder of this article is organized as follows. In Section 2, we provide the motivation for this work and briefly show the need for group communication in various application domains of the IoT We provide a brief introduction to CoAP and introduce the different approaches for CoAP group communication. We then discuss related work in Section 3. In Section 4, we introduce our CoAP group communication implementation and evaluate it in Section 5 before concluding this paper in Section 6.