esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Scalability Analysis in a Larger-Scale System  
The IEEE 118-bus system is investigated to verify the scalability of the proposed method. Suppose the expected contingency is a three-phase fault on bus 5 in the IEEE 118-bus system. Figure 8(a) illustrates the rotor angle trajectories of generators after the specific contingency in the IEEE 118-bus system without security control. The initial rotor angle trajectory of G40 deviates from other generators, thus directly causing the maximum angle difference of over 200 degrees after the contingency. The system is not secure according to the angle consistency criterion. From the rotor angle trajectories of 54 generators, G40 is the critical unit affecting the security of the system.  
Table 3 shows the iteration process of adjustment for the IEEE 118-bus system using the proposed SC strategy. The iteration process is converged within three epochs. A total of three generators (G21, G40, and G54) are determined to adjust their active power output to achieve SC according to the interpretation results. G40 is adjusted in each iteration, indicating that G40 is one of the critical units.  
The total elapsed time for iteration is 8.583 s. Compared with the classical differential evolution-based global SCOPF method, which takes 59.260 s on average, the calculation speed of this method is increased by 85.5%.  
Figure 8: Time consumption of I-FIA.  
Figure 9b illustrates the rotor angles of generators after the contingency in the IEEE 118-bus system taking the SC. G40 is accurately identified as the generator with the most significant impact on system security from the results of control generators obtained by the proposed SC strategy. Its power output is correctly guided to be reduced. After taking the security control, the initial rotor angle of G40 is reduced. Thus, the maximum angle difference is reduced to below 180 degrees after the expected fault, which is considered stable according to the criterion.
## 2 Circuit Description
### Proposed Filter  
Figure 3a illustrates the doubly terminated third-order LC ladder low-pass filter, where \(R_{s}\) and \(R_{L}\) are connected to the input and output terminals, respectively. Figure 3b presents the signal flow graph of the LC ladder low-pass filter, corresponding to the topology in Figure 3a. It can be achieved using the voltage and current relationships (KCL routine analysis) from Figure 3a, as follows:  
\[I_{1}=I_{in}-\frac{V_{1}}{R_{s}}-I_{2} \tag{8}\]  
\[V_{1}=\frac{I_{1}}{sC_{1}} \tag{9}\]  
\[I_{2}=\frac{V_{1}-V_{3}}{sL_{1}}=\frac{V_{2}}{sL_{1}} \tag{10}\]  
\[V_{3}=\frac{I_{2}-I_{RL}}{sC_{2}} \tag{11}\]  
\[I_{3}=I_{2}-I_{RL} \tag{12}\]  
where \(I_{RL}=I_{out}\) and \(V_{3}=V_{out}\).  
The proposed fully current-mode third-order low-pass filter is shown in Figure 4. CDBA1, \(R_{s}\), \(C_{1}\), and \(R\) function as the first lossy integrator, while CDBA3, \(R_{L}\), \(C_{2}\), and \(R\) serve as the second lossy integrator. Additionally, CDBA2, \(C_{L1}\), and \(R\) act as a gyrator for implementing inductor \(L_{1}\). The inductor \(L_{1}\) can be designed according to the expression \(L_{1}=C_{L1}R^{2}\). Finally, the CDBA4 is employed to sense the output current flowing through  
Figure 3: (**a**) Third-order LC ladder low-pass filter; (**b**) corresponding signal flow graph of LC ladder low-pass filter.  
the load resistor \(R_{L}\), while maintaining a high output impedance, which is essential for current-mode circuit applications.
## 4 Discussion
In fact, a lack of communication between the two defensive players (i.e., announcing the upcoming offensive screen, its direction, and the agreed-upon defensive coverage according to the game plan) may lead to higher PnR duration and physical load, as players would need to recover from the initial incorrect movements performed during PnR and the advantages obtained by offensive players. Consequently, great footwork, help and recovery, and team rotations are instantly required by both the defensive players directly involved in the PnR and the off-ball screen players. These defensive multidirectional displacements induce a significant physical load coupled to the timing of its development. Another possible explanation could be that offensive players confused the defense coverage by faking to go right of the screen, and then reversed direction at the last second and ran the ball from the left side (the speed and the angle of the screen set by the screener, in order to interrupt the trajectory of the ball handler's defender) [12]. This deceptive PnR offensive strategy leads to a disorganized defensive reaction, as players start running randomly to stop the ball and cover the open gaps due to the unexpected nature of the move. These findings are crucial for basketball coaches, since unsuccessful defense during a PnR action not only produces a negative impact on the ball possession outcome but also generates greater physical load volumes and intensities for the two defensive players involved. Therefore, coaching staff should consider this information when planning team rotations during official games. Although this study provides interesting insights for basketball coaches, some limitations should be acknowledged. Firstly, the recruited basketball players competed in the male Lithuanian third division, and therefore the findings might not be generalizable to basketball players competing in higher-division leagues, where games are characterized by a higher physical load, nor to female players. Secondly, the current investigation only focused on quantifying the external load associated with defensive PnR actions, while no information is available on the physical load associated with offensive PnRs. Therefore, future investigations should focus on assessing the physical loads of PnR actions in high-level basketball players from both an offensive and a defensive standpoint.
## 3 Classifier Training and Results
### Person Independent Recognition of Head Gestures  
Here we report on the performance of the classifiers for the test data recorded for the 12 trial participants for whom the recorded IMU signals were not used for training the classifiers. In Figures 5 and 6, we compare the recognition rates of head gestures for the new 12 users and the classification results obtained for the test dataset recorded for 53 trial participants. Figure 5 contains the results for the shortest time window \(T=0.1\) s for which 42 statistical parameters were computed and used for training the classifiers, whereas Figure 6 shows the results for the classifiers trained on IMU signal samples, i.e., on six-element vectors.  
For the person-independent test datasets, i.e., for the 12 participants who did not take part in training the classifiers, the best head movement recognition results for vectors with 42 parameters were obtained for the SVM and random forests. For all of these classifiers, about 90% accuracy or higher was achieved (Figure 5). Note also that for the person independent test datasets, the results obtained were inferior to those for the T_53_set test data (i.e., person dependent dataset) except for the SVM classifiers which performed equally on the two test datasets (for the _TDR_ training procedure).  
Figure 6: Comparison of the classifiers’ accuracy obtained for the test data from participants who trained the classifiers (T_53_set) and the test data from individuals who did not record the training data (T_12_set). The test data collected directly from the accelerometer and gyroscope (vectors with 6 parameters).  
On the other hand, for the classifiers trained directly on the IMU signal samples, head gesture recognition accuracy exceeded 90% for all classifiers, except the decision tree classifier (see Figure 6). Interestingly, the accuracy for all classifiers except SVM was better for T_12_set, however, the SVM performed best for both T_53_set and T_12_set test datasets.
# IoT Privacy and Security Challenges for Smart Home Environments
## 5 Some Existing Security Support for IoT
### CoAP and Security  
CoAP [22] is a HTTP-like application layer protocol designed for constrained device networks. As there are some special requirements such as group communications in IoT networks, CoAP provides multicast support which HTTP does not have. To better suit the low-bandwidth connections and low-computational-power device environments, the User Datagram Protocol (UDP) protocol is adopted by CoAP. UDP is a simpler, low-latency and connectionless transport layer protocol compared with its counterpart Transmission Control Protocol (TCP). CoAP is a stateless protocol and is based on the client-server architecture model. It uses request/response-style operations to exchange messages between the client and server. Similar to HTTP, CoAP is also based on a representational state transfer (REST) model, where each resource on the server has its own Uniform Resource Identifier (URI), a client can access a resource by making a request to the server, and the request can be one of these four methods: GET, POST, PUT and DELETE.  
Nowadays, Transport Layer Security (TLS: RFC 5246) [28] is the predominant encryption protocol for HTTP, but the implementation of TLS is overcomplicated for the resource-confined IoT devices. To secure communications, CoAP employs Datagram Transport Layer Security (DTLS: RFC 6347) [29]as its security protocol. DTLS offers the same security services that TLS provides. The main difference between TLS and DTLS is that TLS is based on the TCP protocol and DTLS is based on the UDP protocol. The CoAP specification has defined four different security modes. A device can be in one of four security modes: NoSec, PreSharedKey, RawPublicKey and Certificate.  
Again, IETF standards provide secure mechanisms for secure web-based communications within constrained networks.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 8 Performance-Based Design for PEF
### Importance of Sequential Analysis and Performance Correlation  
Sequential analysis is critical for evaluating the performance of structures under post-earthquake fire (PEF) conditions. This analytical approach involves a series of steps that simulate the loading history of a structure, including gravity loads, earthquake loads, and subsequently, fire loads [50; 75]. By incorporating the residual deformations and damage inflicted by the earthquake, this method provides a more realistic assessment of the structure's fire resistance [66; 68]. Understanding the correlation between performance levels is essential in evaluating how earthquake-induced damage influences structural behavior under fire. This process involves correlating seismic performance levels, such as Immediate Occupancy (IO) and Life Safety (LS), with fire resistance [3]. For example, studies on steel moment-frame beam-column connections have shown that while various parameters influence seismic performance, only the intensity of the vertical load and the presence of stiffeners significantly affect the connection's performance under PEF conditions. In conclusion, sequential analysis offers a comprehensive evaluation of structural performance in PEF scenarios by considering the cumulative effects of multiple loading conditions. This approach enhances our understanding of the interplay between seismic damage and fire resistance, ultimately contributing to the development of more resilient and safer structural designs.
# A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means
## 3 Proposed Model
### Digital Watermarking  
For embedding and extracting a watermark, we used DWT and IDWT. This process was used due to its robustness, better quality, and localization [21]. Robust digital watermarking is used to ensure good quality of the original image even after different attacks [22]. In DWT, a two-dimensional image is decomposed into four sub-bands. Low-Low (LL) is the top left sub-band that has the low frequency of the image, Low-High (LH) is the bottom right sub-band that has the vertical details of the image, High-Low (HL) is the top right sub-band that has the horizontal details of the image and High-High (HH) is the bottom right sub-band that has the high frequency of the image.  
In the proposed model, after computing the first level DWT we chose the LL1 sub-band because it contains the most information of the original image [4]. After taking the LL band, we computed the second level DWT in the LL1 and sub-band and choose LL2 for more robustness. Then, the watermark was embedded into the LL2 sub-band. The structure of the second level DWT is shown in Figure 6a and the second level DWT of the host image is presented in Figure 6b, where the watermark is implanted in low-frequency coefficient of the host image that contains most of the information. As a result, the image is less vulnerable against various attacks.
# Risk-Aware Wireless Positioning in
## 2 Preliminary: Various Wireless Positioning Schemes
### Hybrid Schemes  
As we explained, various parameters such as RSS, AoA and TDoA can be used for positioning and hybrid positioning schemes use more than two parameters for more accurate position information. The TDoA/AoA scheme roughly estimates the distance between BSs and the user with TDoA and then determines a more accurate user's position with AoA [24]. Also, the ToA/RSS schemes are studied in Reference [25; 26; 27], and the RSS/AoA scheme was studied in Reference [28]. Hybrid positioning schemes can obtain more accurate position information, so enables to provide a stable location-based service (LBS). However, they are hard to implement and requires more time for estimation.
## 2 Proposed Approach
### Input Features  
#### 2.1.1 Log-Mel Spectrogram  
A spectrogram is a representation of a frequency-specific value of a signal that changes with time. For analysis in the frequency domain, the time-domain signal is divided into sections by a certain number of data samples corresponding to the frame size. The short-time Fourier transform (STFT) is performed for the data in each section. In general, an appropriate segmentation length is 20-35 ms for the audio analysis. We set the frame and hop sizes for the STFT to 1024 and 600 samples, respectively.  
Humans are more sensitive to changes in a low-frequency band than in a high-frequency band. Therefore, it is effective to extract and use the feature vector by focusing on the low-frequency band rather than the entire frequency band. The mel-filter bank consists of filters with narrower bandwidths as the frequency decreases to analyze the change in a relatively low-frequency range in detail. A mel-spectrogram is obtained by analyzing data by a mel-filter bank. In addition, humans notice the intensity difference more sensitively at a lower power level than at a higher power level. To magnify the intensity difference as the power level is lower, a log-mel spectrogram is obtained by taking the logarithmic value on a mel-spectrogram. We set the number of filters to 256 for the mel-spectrogram.
# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
## 4 Experiments and Discussion  
This section analyzes the experiments we conducted to test and validate our system. Videos of every quantitative and qualitative experiment are provided in Supplementary Video S1.  
We test and compare the use of the complete integrated system with the S-DOVS planner on its own in different maps, our proposed waypoint generator with others of the state of the art and extensively evaluate the system in both simulation and the real world. Note that we don't compare the S-DOVS planner with other local reactive planners such as RL-DOVS [22], as developing a local planner is not the goal of the paper, and any other local reactive planner could be used in the system instead of S-DOVS. Common reactive planners, by themselves, are limited to handling only a few meters of navigation within a single room in the real world. As a proof of concept, our experiments demonstrated paths of up to 15 meters in length, both in simulations and the real world. However, it is not limited to this distance, as the waypoint generator enables us to generate paths of any desired length.
## 4 Results  
### Spectral Analysis without Actuation  
To identify the dominant frequencies present in the flow field, hot wire measurements were done along measurement lines (see Figure 5). For consistency, the points where the Power Spectral Density (PSD) was calculated were those where the peak RMS velocity was located, as shown in Figure 6.  
It was found that along the measurement lines of _L1_, _L2_ and _L3_ the frequencies representing the shear layer and the wake vortex were present. The high shear layer characteristic frequencies are seen in the bulk of the PSD, whereas the low wake vortex characteristic frequencies are obvious as a peak. When comparing the shear layer frequencies along _L1_, _L2_ and _L3_, it was observed that  
Figure 5: Location of measurement lines used to obtain the power spectra of flow instabilities without actuation.  
there was a reduction in the frequency of the bulk, which represents the characteristic frequency of the shear layer when moving downstream. The same wake vortex frequencies were also observed on \(W1\) and \(W2\). Based on the observed power spectra generated, the frequencies at which the actuation would be performed were finalised. Examples of the power spectra can be seen in Figures 7 and 8.  
According to the obtained power spectra, the most dominant and characteristic frequencies of the flow without plasma actuation were determined. The results of this analysis at a \(Re=100,000\) and \(\alpha=16^{\circ}\) indicated that the most dominant characteristic frequencies of the wake are \(f=35.0\,\mathrm{Hz}\) and \(44.2\,\mathrm{Hz}\), whereas for the shear layer the most dominant frequencies are \(500\) and \(1058\,\mathrm{Hz}\). At the same \(Re\) and \(\alpha=24^{\circ}\), the characteristic frequencies detected are \(f=34\), \(203\), \(400\) and \(700\,\mathrm{Hz}\).  
Figure 6: Example of the location where the PSD analysis took place for: the shear layer region close to the leading edge (lines L1, L2, and L3) **(left)**; and the region of wake (W1 and W2) as depicted in Figure 5 **(right)**.  
Figure 7: Power spectra along L1, \(Re=100,000\) and \(\alpha=24^{\circ}\).  
At a \(Re=200,000\) and \(\alpha=16^{\circ}\), the dominant frequencies detected in the power spectra were found to be \(f=33.6\), \(41.0\), and \(174.4\) Hz. At the same \(Re\) and \(\alpha=24^{\circ}\), the dominant values of frequencies were found to be \(f=69.0\), \(266.6\), \(470.0\) and \(1333.3\) Hz. In all cases, the most dominant characteristic frequencies detected were applied as the actuation frequency of the DBD actuator to determine the degree of control authority they possess in re-attaching separated flows at the respective angles of attack at which they were detected.
## 3 Experimental Setup and Results
### Results--Outlet Air Conditions and Water Production  
The test was carried out from 6 th to 27 th of November 2015. During this period, temperature and relative humidity values were somewhat different from historical statistics: in fact average temperature and relative humidity were 27.3 \({}^{\circ}\)C and 86%, respectively [12]. Calculated thermo-hygrometric parameters for these conditions are shown in Table 2, and indicated on the psychrometric chart (Figure 9 and 9 bis).  
\begin{table}
\begin{tabular}{c c} \hline
**Outdoor Parameters** & **Values** \\ \hline temperature & T\({}_{\text{e}}\) = 27.3 \({}^{\circ}\)C \\ relative humidity & RH\({}_{\text{e}}\) = 86\% \\ specific enthalpy & h\({}_{\text{e}}\) = 77.86 kJ/kg \\ moisture content & x\({}_{\text{e}}\) = 0.01975 kg\({}_{\text{yapour}}\)/kg\({}_{\text{dry air}}\) \\ dew point temperature & T\({}_{\text{dp,e}}\) = 24.7 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 2: Outdoor environment thermo-hygrometric parameter mean values (6–27 November 2015).  
Figure 8: Temperature probes NTC.  
Figure 7: Energy meterDuring the testing period, the integrated system ran continuously for 24 h/day, with average production of 1882 dm\({}^{3}\)/day of drinking water, which was entirely used for human consumption (drinking and kitchen uses). This production corresponds to an estimated saving of 158 dm\({}^{3}\)/day of bottled drinking water, corresponding to the number of guests during the test period (two half-liter bottles for each guest), and 1724 dm\({}^{3}\)/day of water from tanks, used for kitchen and restaurant uses. At the same time, domestic water was heated at an average temperature of 49.4 \({}^{\circ}\)C, by means of the heat flux coming from the integrated machine.  
In order to produce 1882 dm\({}^{3}\)/day of water, the system treated an environmental average airflow of 8500 m\({}^{3}\)/h, which was cooled and dried to 20.7 \({}^{\circ}\)C and 77% RH. The corresponding calculated thermo-hygrometric parameters are shown in Table 3. The wet air processes performed by means of the integrated system are shown in Figure 10, considering inlet air in outdoor conditions (Table 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Treated Air Parameters** & **Values** \\ \hline temperature & T\({}_{\text{out}}\) = 20.7 \({}^{\circ}\)C \\ relative humidity & RH\({}_{\text{out}}\) = 77\% \\ specific enthalpy & h\({}_{\text{out}}\) = 50.6 kJ/kg \\ moisture content & x\({}_{\text{out}}\) = 0.01175 kg\({}_{\text{vapour}}\)/kg\({}_{\text{dry air}}\) \\ dew point temperature & T\({}_{\text{dp,out}}\) = 16.5 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 3: Outdoor environment thermo-hygrometric parameter mean values of treated air (6–27 November 2015).  
Figure 9: Psychometric chart particular: outdoor conditions in November 2015.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 2 Background
### Managed Flooding  
Bluetooth Mesh specification defines a message delivery method named managed flooding. It is based on the flooding algorithm, which is no need for any complex routing policy, thus provide a resilient network due to its high redundancy and multi-path capabilities. The following techniques is introduced to limit flooding communication:  
* **Time-To-Live (TTL):** TTL restricts the number of hops a message can survive in the mesh network.
* **Message Caching:** This means mesh nodes can cache received packets to avoid unnecessary re-relaying of these packets.