esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
An ARISE control architecture was developed, and the effectiveness of the controller was compared with a traditional general SM controller. The ARISE controller comprises a novel auxiliary error system that is designed to compensate for uncertain terms that are upper-bounded by a constant. The chattering effect caused by SM terms is mitigated by the introduction of the novel auxiliary error system. A traditional SM controller has a discontinuous SM term directly in the controller, whereas the proposed ARISE controller implements an integral of the SM term in the controller. Unlike SM, the integral of a signum function enables a continuous controller, eliminating chattering and the requirement for infinite control frequency. Unlike RISE control, the developed ARISE controller can be applied to switched and nonsmooth systems. The performance of the proposed ARISE controller was evaluated and compared against a traditional SM controller via simulations. Results from the numerical simulations prove that ARISE control can outperform SM control. Future work will investigate mathematical approaches to eliminate the ultimate bound in the stability analysis. Furthermore, experiments will be performed to experimentally evaluate the performance of the developed ARISE control structure in real systems.  
[PERSON]'s contribution towards the project include methodology, software, formal analysis, investigation, resources, data curation, visualization, project administration, and writing--original draft preparation. [PERSON] contributed towards writing--review and editing, validation, and methodology. [PERSON] contributed to validation and writing--review and editing. [PERSON] contributed to conceptualization, validation, supervision, review and editing, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by NSF award number 2230971. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsoring agency.  
Not applicable.  
All data supporting the results has already been presented in the manuscript.
# Audlet Filter Banks: A Versatile Analysis/Synthesis Framework Using Auditory Frequency Scales
## 4 Implementation
### Code  
We provide code for performing an Audlet analysis/synthesis as part of the Matlab/Octave \"large time-frequency analysis toolbox (LTFAT)\" toolbox [61; 62] available at [[http://lftat.github.io/](http://lftat.github.io/)]([http://lftat.github.io/](http://lftat.github.io/)). The analysis filters are generated by the function audfilters. The function allows to construct at will uniform or non-uniform Audlet FBs with integer or rational downsampling factors, thus offering flexibility in FB design. Rational downsampling factors can be achieved in the time domain by properly combining upsamplers and downsamplers (e.g., [19]). In LTFAT the sampling rate changes are directly performed in the frequency domain by periodizing and folding the \(Y_{k}(z)\)'s, then performing an inverse DFT [63]. This technique allows to achieve rational downsampling factors at low computational costs. The desired number of channels in the frequency range \([f_{\min},f_{\max}]\) can be set by specifying either \(K\) or \(V\). The function audfilters also accepts parameters Scale, \(\beta\), \(w\), and \(R_{\text{t}}\). Currently, three scales (ERB--the default--as well as Bark and Mel) are available. Possible choices of \(w\) include (but are not limited to) Hann (default), Blackman, Nuttall, gammatone, or Gaussian. If \(R_{\text{t}}\) is specified, \(c_{bp}\) is inferred from \(R_{\text{t}}\) according to (15)-(18). Otherwise \(c_{bp}=1\). The analysis of a signal is performed by filterbank. The synthesis is performed by ifilterbankiter that implements Algorithm 1. In the painless case, the more computationally efficient synthesis can be achieved by first computing the _exact_ synthesis FB with filterbankdual and then synthesizing the signal with ifilterbank. The function filterbankdual can also be used to check whether a given analysis FB qualifies for the painless case.  
Example scripts to perform Audlet analyses/syntheses in various FB settings are provided as Supplementary Material (see Archive S1). The supplementary material also demonstrates the realization of iterative reconstruction.  
Note that for real-time implementations using macro blocks like in [46], the overall redundancy depends also on the overlap between the blocks. For analysis or processing purposes, the sub-bands can be combined in an overlap-add manner closely approximating the true non-blocked sub-bands. The perfect reconstruction property within the blocks is preserved.
###### Abstract  
Contemporary wireless networks dramatically enhance data rates and latency to become a key enabler of massive communication among various low-cost devices of limited computational power, standardized by the Long-Term Evolution (LTE) downscaled derivations LTE-M or narrowband Internet of Things (NB IoT), in particular. Specifically, assessment of the physical-layer transmission performance is important for higher-layer protocols determining the extent of the potential error recovery escalation upwards the protocol stack. Thereby, it is needed that the end-points of low processing capacity most efficiently estimate the residual bit error rate (BER) solely determined by the main orthogonal frequency-division multiplexing (OFDM) impairment-carrier frequency offset (CFO), specifically in small cells, where the signal-to-noise ratio is large enough, as well as the OFDM symbol cyclic prefix, preventing inter-symbol interference. However, in contrast to earlier analytical models with computationally demanding estimation of BER from the phase deviation caused by CFO, in this paper, after identifying the optimal sample instant in a power delay profile, we abstract the CFO by equivalent time dispersion (i.e., by additional spreading of the power delay profile that would produce the same BER degradation as the CFO). The proposed BER estimation is verified by means of the industry-standard LTE software simulator.  
LTE; IoT; end-point; BER estimation; time-sampling; BER abstraction +
Footnote †: journal:
# Reversible Data Hiding in Encrypted Images Based on the Mixed Multi-Bit Layer Embedding Strategy
## 3 Proposed Scheme
### Data Extraction and Image Recovery  
* Case 1: Receiver only has hiding key \(Kd\)  
Firstly, extract parameters \(m\) and \(n\) at the position of the second pixel. In the embedding strategy, regardless of whether the first block belongs to AHB or UHB, the data of \(m\) and \(n\) are in the second pixel, and its length is fixed. Next, according to \(Kd\), extract all data and classify them. The received image is segmented into blocks with a size of \(m\times n\), and the MSB of the second pixel in each block (except the first block) is extracted. If MSB is 1, AHBs(\(BT_{1}\)) can be acquired. Otherwise, segment the block for the second round, and extract the MSB in the same way to obtain AHBs (\(BT_{2}\)) and UHBs (\(BT_{3}\)). Finally, all data are extracted from all AHBs. The extracted data includes secret data and auxiliary data.  
For AHBs belonging to \(BT_{1}\), the auxiliary data \(T\) is extracted from the first pixel based on the AMLE strategy structure, and the number of error bits is fixed. For AHBs belonging to \(BT_{2}\), the \(\max(D)\) costs fixed bits in the AMLE strategy structure. Based on \(\max(D)\), each block recalculates a different \(T\) and gives a different number of error bits. \(LE_{1}\), \(LE_{2}\), \(L_{5_{1}}\), and \(L_{5_{2}}\) can be calculated by \(m\), \(n\), the number of error bits and the number of pixels in an AHB.  
By using the above parameters and the mixed embedding strategy, separate auxiliary data \(S_{1}\), \(E_{1}\), \(S_{2}\), \(E_{2}\), and secret data can be obtained.  
Case 2: Receiver only has encryption key \(Ke\)  
Firstly, extract parameters \(m\), \(n\) like Case 1. Then, directly decrypt the received image with \(Ke\) to obtain the first decrypted image, \(De_{1}\). \(De_{1}\) is segmented with \(m\) and \(n\) to improve the decrypted quality. The LSB of \(x_{1}\) is extracted in each block. If the LSB mark is 0, second-round segmentation and mark information extraction are performed. After processing, the blocks marked 0 are classified as UHBs, and the remaining blocks are classified as AHBs. Finally, the first pixel in each block is used to restore other pixels in the AHBs, and the pixels are unchanged in the UHBs. Here, the final decrypted image \(De_{2}\) can be obtained.  
Case 3: Receiver has encryption key \(Ke\) and hiding key \(Kd\)  
Perform subsequent processing according to the results in Case 1 and Case 2. Take the decrypted image \(De_{2}\) in Case 2 as the image object to be processed. Then, recover the LSBs of the reference pixels in AHBs and UHBs with \(S_{1}\) and \(S_{2}\), and calculate the other pixel values by \(E_{1}\) and \(E_{2}\) in the AHBs. The calculation of pixel values is reversible according to the principle in Section 3.1. So far, a completely lossless recovered image can be obtained.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 4 Line-of-Sight Analysis
### Line-of-Sight from Pit Surface to Pit Floor  
With the communication and power transfer architecture defined in Figure 5b, the SphereX robot on the surface of the pit needs to maintain a direct line-of-sight connection link with the SphereX robot on the floor of the pit entrance. To maintain a direct line-of-sight connection, link the SphereX robot on the surface near the pit entrance at a distance \(a\) deploys a boom of height \(h\) vertically upwards that consists of an antenna at its tip. The antenna has a half-power beamwidth \(B\) and is oriented at an angle \(\phi\) with respect to the boom, as shown in Figure 11. The distance \(d\) on the floor of the pit within which a direct line-of-sight communication link is possible with the SphereX on the surface is calculated by projecting the antenna's beamwidth on the floor. The dimensions of the pit entrance are approximately equal to that of MTH, as shown in Figure 11.  
Figure 12 shows the variation of distance \(d\) on the floor of the pit within which a direct line-of-sight communication link is possible with respect to the deployed boom height \(h\) in meters and orientation of the antenna \(\phi\) in degrees. The parameters used for the results are Figure 12a: Beamwidth \(B=30^{\circ}\) and \(a=5\) m, Figure 12b: Beamwidth \(B=60^{\circ}\) and \(a=5\) m, Figure 12c: Beamwidth \(B=30^{\circ}\) and \(a=10\) m, and Figure 12d: Beamwidth \(B=60^{\circ}\) and \(a=10\) m. It can be seen that when the surface SphereX is deployed within 5 m of the entrance, a boom of 3 m will be sufficient to maintain a direct line-of-sight communication link with another SphereX on the floor of the pit. In comparison, when it is deployed at a distance of 10 m, the minimum required boom height increases to 5.7 m.  
Figure 11: Schematic of the boom deployed by the SphereX on the surface near a pit entrance with an antenna at its tip. The dimensions are not scaled.  
Figure 13 shows the feasibility of a direct line-of-sight communication link with respect to the deployed boom height \(h\) in meters and orientation of the antenna \(\phi\) in degrees for an optical transmitter where the angle \(B\)\(\rightarrow\)0. The parameters used for the results are Figure 13a: \(a=5\) m and Figure 13b: \(a=10\) m. It can be seen that when the surface SphereX is deployed within 5 m of the entrance, a boom of 3 m will be sufficient to maintain a direct line-of-sight communication link with another SphereX on the floor of the pit, while when it is deployed at a distance of 10 m, the minimum required boom height increases to 5.7 m.
## 5 Routing over the Hierarchical Virtual Topology
### Path Recovery after Node Failures
#### Recovery of an \(N\)-th-Tier Virtual Long-Distance Link  
For recovery of an \(N\)-th-tier virtual long-distance link, wide flooding may be necessary because a physical path assigned to an \(N\)-th-tier virtual long-distance link may traverse many first-tier modules. Therefore, we adopt a patching method for the recovery of an \(N\)-th-tier virtual long-distance link as a way to reduce overhead. The obtained path may not be the shortest path, because we do not find an end-to-end path; however, the recovery time and the overhead can be reduced by not insisting on the optimal path.  
In recovering the \(N\)-th-tier virtual long-distance link \((s,d)\) assigned to path \([s\ (=p_{0}),p_{1},p_{2},\cdots,p_{l-2},d\ (=p_{l-1})]\) from the failure of node \(p_{i}\ (0<i<l-1)\), each node \(p_{j}\ (0\leq j<i-1)\) floods a route request packet and searches a path to each node \(p_{k}\) where \((i+1\leq k\leq l-1)\). Here, the range of flooding is restricted by the time-to-live (TTL) of the route request packets.
# Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
## 2 Proposed Methodology
### Feature Encoding
#### 2.3.4 Proposed Residual-VLAD (R-VLAD)  
This section describes the proposed encoding method inspired by VQ and VLAD encoding schemes. The goal is to find a simple yet meaningful representation for given spatio-temporal descriptors of a video. The first step is to find the nearest codeword \(c\) for each \(f_{i}\) in \(F=[f_{1},f_{2},\ldots f_{N}]\in\mathbb{R}^{D\times N}\) of a video. These nearest codeword assignments are similar to VQbut instead of hard assignment into a histogram, a residual vector similar to VLAD is obtained by finding the difference between the descriptor \(f_{i}\) and the mean of the assigned codeword. This helps in describing the distribution of feature vectors with respect to the center. The mean of each assigned codeword is a unique value which is an important statistical information. The final encoded vector is given by:  
\[V_{i,j}=f_{j}-mean(c) \tag{4}\]  
where \(c\) represents the nearest codeword and \(f_{j}\) represents the \(j\)th component of the feature descriptor \(F\). This difference vector brings complementary information related to the assigned codewords, which proves to be beneficial for the classifier. The final dimension of the encoded vector is the same as that of the feature descriptor represented by D (features dimensionality). Due to this reduced size we call it the Residual VLAD or R-VLAD feature vector. In comparison, VLAD has a dimension of \(M\times D\) (number of clusters \(\times\) the features dimensionality).
## 1 Introduction  
Obstructive sleep apnea syndrome (OSAS) is a prevalent sleep disorder characterized by repeated upper airway collapse. It can cause various problems, including daytime sleepiness, neurocognitive disorders, cardiovascular and metabolic disorders, and even traffic accidents [1, 2]. Sleep bruxism (SB) is a masticatory muscle activity during sleep that is characterized as rhythmic (phasic) or nonrhythmic (tonic) and is not a movement disorder or a sleep disorder in otherwise healthy individuals [3]. SB is sometimes seen in patients with OSAS. Although continuous positive airway pressure (CPAP) therapy is highly effective for treating patients with OSAS, there are several confounding issues of CPAP therapy, such as problems with adaptation to devices and compliance [4]. An oral appliance is one superior alternative approach to CPAP for patients suffering from OSAS [5, 6]. SB in a patient with OSAS might affect the treatment using an oral appliance instead of CPAP therapy. Considering that oral appliances have better preference and compliance than CPAP [7, 8] and that well-designed oral appliances can control both SB and OSAS simultaneously [9, 10], these might be considered more useful than CPAP when the patient has SB during OSAS, especially for mild and moderate OSAS. However, the relationship between OSAS and SB is unclear. The criteria used to investigate the relationship between SB and OSAS have varied between studies and the results are controversial [11; 12; 13; 14; 15; 16]. Therefore, further studies on SB in patients with OSAS are necessary.  
In-laboratory polysomnography (PSG) is the gold standard for diagnosing OSAS [17] and detecting SB episodes [18]. However, this technique is time-consuming and expensive and has long waiting periods. Few studies have examined the association between SB and OSAS using in-laboratory PSG. Also, there has been insufficient research on the association between SB episodes identified by in-laboratory PSG and self-reported SB. Here we aimed to investigate patients' characteristics in cases of OSAS with SB episodes and related PSG indices using in-laboratory PSG. For this, we compared subjects with and without SB episodes among patients with OSAS rather than healthy controls because we wished to consider the influences between factors that might affect the combination of OSAS and SB. We evaluated whether a single overnight in-laboratory PSG session would be suitable for assessing SB episodes in patients with OSAS.
## 3 Performance Analysis of UOWC Links under Air Bubble-Induced Fading
### Bit Error Rate Analysis  
As a case study, we analyze the BER performance of a SISO UOWC system when adopting On-Off Keying (OOK) signaling for the case of perfectly known channel state information at the receiver. As stated in [29], the conditional average BER at the receiver for the case of equally likely transmitted symbols is given by  
\[P_{b}(E|h)=Q\Big{(}\sqrt{2\gamma}\cdot L\cdot h_{b}\Big{)}, \tag{4}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Antacid [mg/L]** & \(\mathbf{(a,d,p)}\) & \(\mathbf{L_{T}}\) **[dB]** & \(\mathbf{c}\) **[m\({}^{-1}\)]** & \(\mathbf{\sigma^{2}}\) & \(\mathbf{R^{2}}\) \\ \hline
0 & (1.73, 1.43, 7.95) & 6.8 & 0.16 & 0.22 & 0.99 \\
3.6 & (1.72, 1.44, 6.28) & 11.7 & 0.54 & 0.23 & 0.99 \\
7.3 & (1.59, 1.68, 4) & 14.7 & 0.77 & 0.19 & 0.98 \\
11 & (1.19, 2.75, 3.05) & 17.4 & 0.98 & 0.14 & 0.99 \\
14.5 & (0.79, 5.09, 2.41) & 20.9 & 1.24 & 0.09 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experiment results of fitting parameters for generalized Gamma, total losses, the attenuation coefficient, and the scintillation index under small air bubble scenario for different turbidity levels. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Antacid [mg/L]** & \(\mathbf{(a_{1},d_{1},p_{1})}\) & \(\mathbf{(a_{2},d_{2},p_{2})}\) & \(\mathbf{w}\) & \(\mathbf{L_{T}}\) **[dB]** & \(\mathbf{c}\) **[m\({}^{-1}\)]** & \(\mathbf{\sigma^{2}}\) & \(\mathbf{R^{2}}\) \\ \hline
0 & (0.028, 2.12) & (1.38, 29.38, 22.7) & 0.2 & 5.0 & 0.16 & 0.32 & 0.95 \\
3.6 & (0.05, 1.110, 0.85) & (1.38, 13.95, 12.8) & 0.23 & 9.9 & 0.54 & 0.30 & 0.98 \\
7.3 & (0.06, 1.320, 0.85) & (1.39, 11.93, 11.7) & 0.23 & 12.9 & 0.77 & 0.29 & 0.98 \\
11 & (0.047, 3.77, 1.060) & (1.36, 10.87, 10.55) & 0.26 & 15.6 & 0.98 & 0.27 & 0.98 \\
14.5 & (0.037, 7.28, 0.98) & (1.33, 10.87, 10.2) & 0.25 & 19.1 & 1.24 & 0.21 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experiment results of fitting parameters for generalized Gamma, total losses, the attenuation coefficient, and the scintillation index under large air bubble scenario for different turbidity levels. where \(Q(\cdot)\) represents the Gaussian-\(Q\) function. Hence, \(P_{b}\) in the small air bubble scenario is obtained by averaging over the PDF of small air bubbles defined in Equation (2) as follows:  
\[P_{b_{\text{small}}}=\int_{0}^{\infty}Q\Big{(}\sqrt{2\gamma}\cdot L\cdot h_{b} \Big{)}\cdot f_{h_{\text{small}}}(h_{b})\,dh_{b}. \tag{5}\]  
By substituting Equation (2) into Equation (5), and making use of ([30], Equation (07.34.21.0012.01)), the exact closed-form expression for the average BER in the presence of small air bubbles can be obtained as follows  
\[P_{b_{\text{small}}}(L,a,d,p)=\frac{p}{4\sqrt{\pi}(L\,a)^{d}\Gamma\Big{(}\frac {d}{p}\Big{)}L^{d}}\gamma^{-\frac{d}{2}}H_{2,2}^{1,2}\bigg{[}\frac{1}{(L\,a)^ {p}}\gamma^{-\frac{p}{2}}\bigg{|} \begin{array}{c}(1-\frac{d}{2},\frac{p}{2} ),(\frac{1-d}{2},\frac{p}{2})\\ (0,1),(-\frac{d}{2},\frac{p}{2}),\end{array} \bigg{]}, \tag{6}\]  
where \(H_{m,n}^{p,q}(\cdot)\) is the H-Fox function ([31], Equation (1.1.1)).
## 7 Simulation Results and Discussion
### Performance Indices  
To further validate the superior performance of the proposed MPPT strategy to the FTSMC-, CSMC-, FBLC- and PID-based MPPT strategies, the dynamic performance of each MPPT candidate is also evaluated through four different performance indices, given as follows [11; 28; 29]:  
\[ISE=\int_{0}^{t_{sim}}\left[e\left(t\right)\right]^{2}dt \tag{28}\] \[ITSE=\int_{0}^{t_{sim}}t\left[e\left(t\right)\right]^{2}dt\] \[IAE=\int_{0}^{t_{sim}}\left[e\left(t\right)\right]dt\] \[ITAE=\int_{0}^{t_{sim}}t|e\left(t\right)|dt\]  
where \(t_{sim}\) indicates the total simulation time.  
The performances indices, ISE (integral squared error), ITSE (integral of time squared error), IAE (integral absolute error) and ITAE (integral of time absolute error) have been computed for PMSG-WECS using \(e(t)=\Omega_{h}-\Omega_{ref}\) for each MPPT strategy. It is evident from Figures 15-18 that as the time advances, the accumulative error of each MPPT controller also increases. However, the proposed FDTSMC renders the flattest profile and the smallest error in each case, thus guaranteeing  
Figure 14: Mechanical torque versus the low-speed shaft rotational speed.  
Figure 13: Electromagnetic torque versus the tip speed ratio performance.  
its superior performance. Moreover, the FTSMC is the second best MPPT candidate, while the PID is the worst.  
Figure 16: Integral of time squared error.  
Figure 17: Integral absolute error.  
Figure 15: Integral squared error.