esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Algorithm** & **TP** & **FP** & **TN** & **FN** & **Accuracy** & **PPV** & **NPV** & **TPR** & **TNR** \\ \hline \multicolumn{8}{c}{\(X_{\text{pdf}}\) was generated using the Gaussian kernel.} \\ SVM & 1168 & 660 & 5335 & 837 & 0.813 & 0.639 & 0.864 & 0.583 & 0.890 \\ Decision tree & 1335 & 691 & 5304 & 670 & 0.830 & 0.659 & 0.888 & 0.666 & 0.885 \\ NN (80, 10, 1) & 1126 & 421 & 5577 & 876 & 0.838 & 0.728 & 0.864 & 0.562 & 0.930 \\ NN (160, 40, 1) & 1356 & 777 & 5172 & 695 & 0.816 & 0.636 & 0.882 & 0.661 & 0.869 \\ NN (80, 20, 10, 1) & 1420 & 227 & 5706 & 647 & 0.891 & 0.862 & 0.898 & 0.687 & 0.962 \\ \hline \multicolumn{8}{c}{\(X_{\text{pdf}}\) was generated using the exponential kernel.} \\ SVM & 1188 & 668 & 5327 & 817 & 0.814 & 0.640 & 0.867 & 0.593 & 0.889 \\ Decision tree & 1315 & 710 & 5285 & 690 & 0.825 & 0.649 & 0.885 & 0.656 & 0.882 \\ NN (80, 10, 1) & 1560 & 394 & 5588 & 458 & 0.894 & 0.798 & 0.924 & 0.773 & 0.934 \\ NN (160, 40, 1) & 1265 & 591 & 5410 & 734 & 0.834 & 0.682 & 0.881 & 0.633 & 0.902 \\ NN (80, 20, 10, 1) & 1278 & 717 & 5325 & 680 & 0.825 & 0.641 & 0.887 & 0.653 & 0.881 \\ \hline \end{tabular}
\end{table} 
Table 6: The evaluation results of the test set for patients and controls with a matching ratio of 1:3, both X and X\({}_{\text{pdf}}\) were used as the input attributes. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Algorithm** & **TP** & **FP** & **TN** & **FN** & **Accuracy** & **PPV** & **NPV** & **TPR** & **TNR** \\ \hline SVM & 297 & 116 & 5879 & 1708 & 0.772 & 0.719 & 0.775 & 0.148 & 0.981 \\ Decision tree & 1276 & 752 & 5243 & 729 & 0.815 & 0.629 & 0.878 & 0.636 & 0.875 \\ NN (80, 10, 1) & 1196 & 739 & 5238 & 827 & 0.804 & 0.618 & 0.864 & 0.591 & 0.876 \\ NN (160, 40, 1) & 1184 & 725 & 5260 & 831 & 0.806 & 0.620 & 0.864 & 0.588 & 0.879 \\ NN (80, 20, 10, 1) & 1179 & 788 & 5215 & 818 & 0.799 & 0.599 & 0.864 & 0.590 & 0.869 \\ \hline \end{tabular}
\end{table} 
Table 7: The evaluation results of the test set for patients and controls with a matching ratio of 1:3, only X was used as the input attributes. For decision rules leading to a negative label of bipolar disorder, the most frequent branching attributes included hypertension, hyperlipidemia, type II diabetes mellitus, esophageal reflux, chest pain, depressive disorder, alcohol abuse, hypercholesterolemiaemia, atherosclerosis/coronary heart disease, atrial fibrillation, nicotine dependence, hypothyroidism, chest pain, headache, urinary tract infection, abdominal pain, and syncope and collapse.
## 2 Materials and Methods
### Unmanned Underwater Vehicle Model
#### 2.1.3 BlueROV2 Model  
The vehicle considered for the simulations in this work was the BlueROV2 from Blue Robotics(r). It has the six-thruster vectored configuration that can be seen in Figure 2.  
Matrices and vectors involved in the vehicle's hydrodynamics contain numerous unknown parameters, making it infeasible to estimate. Nevertheless, there are some assumptions and considerations that can be made because of the BlueROV2 geometry and operation, resulting in a considerable reduction of unknown parameters. These conditions are listed below:  
* Since BlueROV2 operates at relatively low speeds (i.e., less than 2 m/s), lift forces can be neglected.
* BlueROV2 is assumed to have port-starboard symmetry and fore-aft symmetry, and the center of gravity (CG) is in the symmetry planes.
* BlueROV2 is assumed to operate below the wave-affected zone. As a result, disturbances of waves \(\omega\) on the vehicle are negligible.
* The thruster allocation of BlueROV2 does not permit active control of the pitch orientation \(q\). However, the motion around this axis is considered self-regulated due to the vehicle buoyant restoring moments, resulting in the following reduced system  
\[\
u=(u,v,w,p,0,r)^{T}, \tag{6}\]  
Considering the reduced system detailed before, the physical and hydrodynamic parameters of BlueROV2 are summarized in Table 2 as identified in [31; 32; 33].  
Figure 2: BlueROV2 thruster configuration. (**a**) Right view. (**b**) Top view.  
The matrices that comprise the hydrodynamical model in Equation (4) are considered as defined in [30].  
Considering the thruster distribution in the BlueROV2, the thruster allocation matrix is assembled as  
\[B_{t}=\left[\begin{array}{cccccc}0.7071&0.7071&-0.7071&-0.7071&0&0\\ -0.7071&0.7071&-0.7071&0.7071&0&0\\ 0&0&0&0&-1&-1\\ 0&0&0&0&0.115&-0.115\\ 0&0&0&0&0&0\\ -0.1773&0.1773&-0.1773&0.1773&0&0\end{array}\right] \tag{7}\]
## 5 Evaluation and Discussion
### Evaluation Based on Features of Polystore System  
The features of the proposed system have been compared to the existing systems which are BigDAWG and CloudMdsQL. We present this evaluation in Table 6. As opposed to existing systems that use islands/shims or mediators/wrappers to manage heterogeneity and multiple data stores, the proposed system uses APIs to process queries. The proposed system does not employ wrappers and instead leverages native API calls such as BigDAWG or CloudMdsQL to achieve autonomy. No data repositories are specified in the proposed system because it aims for ultimate transparency. Unlike existing systems that require data stores or information islands to be specified, the proposed method will automatically change into table representation and hide it. The proposed system lacks schema flexibility,although schema can be updated manually and the workflow is defined by standard and QBE SQL. Active query transformation with user-defined data migration is proposed as a way to build upon BigDAWG's data transformation and active data migration capabilities. These functionalities are not supported by CloudMdsQL.
## 4 Evaluation
### Receiver Sensitivity, Link Margin, and Packet Receive Ratio  
The ability of the receiver to detect arriving packets is mainly influenced by the power level of the incoming signal. The receiver sensitivity, the lowest power where the receiver is able to successfully receive a packet, is influenced by many ranging parameters, (ranging channel, preamble, PRF, etc.). To determine the receiver sensitivity for the used set of ranging parameters, a UWB link is set up between an anchor module and a tag module, with a programmable attenuator, the quadAtten from octoBox [29], in between them. The test setup is given in Figure 6. The dynamic range from the attenuator is 0 to 55 dB. An extra attenuation of 30 dB is added to the test setup so path attenuations from 30 to 85 dB could be measured.  
For the Wi-PoShardware, the reported receive power, packet receive ratio, and range success ratio are measured for different attenuation levels and given in Figure 7. Ranging fails when one out of the three packets in the asynchronous two-way ranging scheme is lost, and the ranging starts to fail whenever the received power is less than \(-100\) dB m (path attenuation of 85 dB), but at \(-106\) dB m still 50% of the ranges succeed. As expected, the ranging capabilities degrades faster than the packet reception as ranging requires all three of the packets to be received. The lowest receive power reported where packets are detected (>1% of the packets are received) is at \(-109\) dB for both boards.  
Figure 5: Asynchronous two-way ranging scheme of the UWB packets for a three-anchor system. Only one poll is sent from the tag and shared over the different anchor nodes.
# Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**KSU1**} & \multicolumn{3}{c}{**KSU2**} \\ \hline
**Objects** & **\# Occ** & **AVG** & **Objects** & **\# Occ** & **AVG** \\ \hline pillar & 7 & 93.75 & board & 38 & 79.10 \\ fire extinguisher/hose & 65 & 92.44 & fire extinguisher/hose & 28 & 92.70 \\ trash can & 20 & 94.41 & trash cans & 22 & 91.73 \\ chairs & 83 & 90.12 & chairs & 37 & 85.22 \\ external door & 21 & 92.89 & external door & 18 & 95.29 \\ hallway & 44 & 93.86 & didactic service machine & 7 & 97.97 \\ self-service & 17 & 94.37 & self-service & 17 & 90.45 \\ reception & 5 & 99.77 & reception & 8 & 98.11 \\ didactic service machine & 10 & 92.2 & cafeteria & 5 & 97.74 \\ display screen & 11 & 99.08 & display screen & 14 & 98.61 \\ board & 24 & 92.92 & pillar & 4 & 99.12 \\ stairs & 16 & 96.7 & stairs & 7 & 97.90 \\ elevator & 9 & 99.59 & elevator & 6 & 98.29 \\ laboratory & 5 & 100.00 & prayer room & 5 & 81.06 \\ internal door & 53 & 84.59 & internal door & 32 & 78.38 \\ - & SEN & 94.14 & - & SEN & 94.51 \\ - & SPE & 96.54 & - & SPE & 93.79 \\ - & AVG & 95.34 & - & AVG & 94.15 \\ \hline \multicolumn{3}{c}{**UTrento1**} & \multicolumn{3}{c}{**UTrento2**} \\ \hline
**Objects** & **\# Occ** & **AVG** & **Objects** & **\# Occ** & **AVG** \\ \hline external window & 9 & 81.49 & stairs & 14 & 98.26 \\ board & 28 & 85.90 & heater & 12 & 94.79 \\ table & 4 & 98.09 & corridor & 33 & 95.54 \\ external door & 14 & 91.03 & board & 45 & 79.96 \\ stair door & 14 & 90.70 & laboratories & 15 & 86.70 \\ access control reader & 5 & 79.17 & bins & 15 & 84.32 \\ office & 37 & 88.33 & office & 13 & 95.80 \\ pillar & 34 & 92.19 & people & 8 & 66.09 \\ display screen & 18 & 90.31 & pillar & 11 & 98.56 \\ people & 1 & 50.00 & elevator & 6 & 99.17 \\ ATM & 7 & 94.33 & reception & 10 & 95.63 \\ chairs & 28 & 84.97 & chairs & 4 & 99.18 \\ bins & 14 & 84.25 & self-service & 6 & 87.86 \\ internal door & 35 & 76.57 & external door & 6 & 95.71 \\ elevator & 5 & 91.25 & display screen & 8 & 95.37 \\ - & SEN & 88.14 & - & SEN & 94.91 \\ - & SPE & 88.49 & - & SPE & 89.97 \\ - & AVG & 88.31 & - & AVG & 92.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detailed results per object using Model 3 with the CONV2D layer size = 256 and \(T_{p}=0.3\). Figure 14: Distribution of objects with respect to AVG score versus the number of occurrences using Model 3: (**a**) KSU1 dataset, (**b**) KSU2 dataset, (**c**) UTrento1, and (**d**) UTrento2.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 4 Background on MAC Protocols
### Evaluation Metrics
#### 4.1.2 Normalized Throughput (S)  
The normalized throughput is the quotient between the data received by the satellite in a given time and all the data that could be sent continuously at the transmission rate of the link. It can be interpreted as how effective is the use of the channel. It is always true that \(S\leq C\). The normalized throughput is calculated according to the following equation:  
\[S=\frac{D_{r}}{T_{x}\cdot t}, \tag{2}\]  
where \(D_{r}\) is the amount of data received by the satellite, \(T_{x}\) is the link transmission rate, and \(t\) is an arbitrary time.
## 6 Flow Shop Scheduling--A Case Study
### Assumption and Notations
#### 6.1.2 Notations  
The notations presented in this section were adopted from [50], where a pure genetic algorithm was used to solve the problem with lot streaming. The notation related to lot streaming was removed as the considered FJSP has no lot streaming. Lot streaming was removed since only ordered permutations were optimized. These notations are explained as follows:  
**Indexes and Input Data**  
\begin{tabular}{l l} \(I\) & Total number of stages where the stages are indexed by \(i\) or \(l=1,2,\ldots,I\); \\ \(M_{i}\) & Total number of machines in stage \(i\) where the machines are denoted by \(m\) or \(k=1,2,\ldots,M_{i}\); \\ \(N\) & Total number of jobs where the jobs are represented by \(n\) or \(p=1,2,\ldots,N\); \\ \(P_{n}\) & A set of pairs of stages \((l,i)\) for job \(n\), i.e., the processing of job \(n\) in stage \(l\) is followed by its processing in stage \(i\); \\ \(T_{n,m,i}\) & Total processing time for one unit of job \(n\) on machine \(m\) in stage \(i\); \\ \(Q_{n}\) & Batch size of job \(n\); \\ \(R_{m,i}\) & Number of maximum production runs of machine \(m\) in stage \(i\) where the production runs are indexed by \(r\) or \(u=1,2,\ldots,R_{m,i}\); \\ \(S_{m,i,n,p}\) & Setup time on machine \(m\) in stage \(i\) for processing job \(n\) following the processing of job \(p\) on this machine; if \(n=p\), the setup may be called minor setup; \\ \(A_{n,i}\) & Binary data equal 1 if setup of job \(n\) in stage \(i\) is attached (non-anticipatory), or 0 if \\  & this setup is detached setup (anticipatory); \\ \(B_{n,i}\) & Binary data equal 1 if job \(n\) needs processing in stage \(i\); otherwise, 0; \\ \(D_{n,m,i}\) & Binary data equal 1 if job \(n\) can be processed on machine \(m\) in stage \(i\), otherwise 0; \\  & \(D_{n,m,i}\leq B_{n,i}\); \\ \(F_{m,i}\) & The release date of machine \(m\) in stage \(i\); \\ \(\Omega\) & Large positive number. \\
**Variables** & \\ Continuous Variables: \\ \(c_{n,i}\) & Completion time of the job \(n\) from stage \(i\); \\ \(\widehat{c}_{r,m,i}\) & Completion time of the \(r\)th run of machine \(m\) in stage \(i\). \\ Binary Variables: \\ \(x_{r,m,i,n}\) & Binary variable that takes the value 1 if the \(r\)th run on machine \(m\) in stage \(i\) is for the job \(n\), 0 otherwise; \\ \(z_{r,m,i}\) & A binary variable that equals 1 if the \(r\)th potential run of machine \(m\) in stage \(i\) has been assigned a job to process, 0 otherwise; \\ \(c_{max}\) & Makespan of the schedule. \\ \end{tabular}  
Given the above notations, a mixed-integer linear programming can be used to formalize the scheduling problem addressed in this paper. However, we used a solution representation of a genetic algorithm and the accompanying decoding procedure as a way to formalize the problem. The following section provides details of the genetic algorithm that is commonly used to solve flow shop scheduling. This algorithm is used as the main component of the ANN-Guided GA framework proposed in Section 5.
## 2 Uplink Channel Model  
In the downlink channel, radio waves emitted from BS antennas are reflected and diffracted by surrounding objects, such as buildings or trees, and they construct secondary wave sources, that is, scatterers, around the MIMO terminal. Furthermore, radio waves radiated from the secondary wave sources are combined to form a single coherent wave in the antennas at the MIMO terminal via different routes. Then, the incoming radio waves around the MIMO terminal generate a uniform distribution, and the correlation coefficient between the signals received at the MIMO terminal antennas is low, regardless of the distance between them [27].  
One of the most common approaches to analyze a MIMO antenna system is to use Monte Carlo simulation, where many scatterers are arranged on a circle, known as [PERSON]'s model [28], to simulate a multipath fading channel in a mobile propagation environment; this results in the evaluation of the MIMO terminal performance [24]. A multipath propagation environment is simulated by the amplitude and phase of each wave radiated from the scatterers, which is an aggregation of all the information in the radio waves emitted by the BS antennas. A spatial fading emulator based on [PERSON]'s model is implemented using Monte Carlo simulation; thus, controlling the amplitude and phase of the scatterers is essential to create a realistic propagation environment.  
However, for the uplink channel, the BS correlation is high regardless of the BS antenna separation because the angular spread of the incoming wave for the BS antennas is very narrow [29]. The BS correlation is known to be relatively high, even in the case of array spacings of several wavelengths in a small cell environment [25]. Moreover, the BS correlation is expected to be higher when the direction of the incident wave is parallel to the BS antenna array. Hence, when the performance of the uplink channel from the MIMO terminal is examined, the channel model must implement a high BS correlation that is different from that of the downlink channel assumed to be independent and identically distributed (i.i.d.).  
Figure 1 shows the propagation model for the uplink channel, wherein the radio waves emitted from the MIMO terminal are reflected and diffracted over the full azimuth by surrounding objects, such as buildings or trees, which are the relay points, that is, probes, around the MIMO terminal. Furthermore, the radio waves radiating from the probes combine to generate a single coherent wave at the BS antennas via different routes.  
The BS correlation for the uplink channel generally involves the characteristics of radio wave propagation, which is represented by multipath waves and the BS antenna characteristics. In this paper, the sum of the two above-mentioned effects is attributed to the characteristics of the multipath waves, which are represented by the amplitude and phase of each wave radiating from the probes. Consequently, the area around the BS was not modeled in Figure 1.
## 3 Test and Result Analysis of Three-Dimensional Bending Test
### Perception and Collection of Data  
In the three-dimensional bending test of the scraper based on FBG, the wavelength signal of the sensing fiber was demodulated by using the sm125 demodulation instrument produced by Micron Optics. The channel number of the demodulator is 4. The demodulation wavelength ranges from 1510 nm to 1590 nm.  
The measured data of the three-dimensional curved shape were collected by a standard tape measure, and the X-direction, Y-direction and Z-direction coordinate offsets of each grating point relative to the starting point of the fixed end were obtained and then translated into corresponding coordinate values (\(X_{0i}\), \(Y_{0i}\), \(Z_{0i}\)).  
The wavelength drift at each grating point in the orthogonal direction was demodulated by the sm125 FBG demodulation instrument, and 3D bending shape sensing data was collected. Then, converted into the corresponding curvature according to Equation (4). Finally, the coordinate values of each grating point (\(X_{1i}\), \(Y_{1i}\), \(Z_{1i}\)) were obtained according to the 3D curve reconstruction method based on curvature information of discrete points described in this paper.  
However, when the bending direction changes, especially when the bending direction is opposite, FBG can still sense the bending information of the flexible substrate. Similarly, the wavelength drift at each grating point is obtained by demodulation with the sm125 FBG demodulator instrument. First, according to Equation (4), the corresponding curvature is converted. Then, according to the three-dimensional curve reconstruction method based on the curvature information of discrete points described in this paper, the coordinate values of each grating point (\(X_{2i}\), \(Y_{2i}\), \(Z_{2i}\)) were obtained.
# Automatic Method for Bone Segmentation in Cone Beam Computed Tomography Data Set
## 2 Materials and Methods
### Evaluation of Method Accuracy  
The results of the proposed algorithm were compared with the results of segmentation performed by an experienced surgeon. In order to evaluate the reliability of surgeon's segmentation, the segmentations were done twice with a 2 weeks interval. For the quantitative evaluation of the reliability, Intraclass Correlation Coefficient (ICC) by using a two-way mixed model, unit: single rater and the type of relationship: absolute agreement method [22] was calculated by:  
\[ICC=\frac{MS_{R}-MS_{E}}{MS_{R}+(k-1)MS_{E}+\frac{k}{n}(MS_{C}-MS_{E})}, \tag{12}\]  
where \(MS_{R}\) = mean square for rows, \(k\) = number of raters/measurements; \(MS_{E}\) = mean square for error; \(MS_{C}\) = mean square for columns; \(n\) = number of subjects.  
ICC was calculated separately for preoperative and postoperative cases and segmentation thresholds selected by the surgeon were used for this.  
The evaluation of segmentation accuracy by using proposed method three basic metrics were used:  
1. Root mean square (RMS) of the intersurface distance used to evaluate reconstructed surface mismatch: \[RMS=\frac{1}{\sqrt{n}}\cdot\sqrt{\sum_{x,y,z=1}^{n}(a_{x,y,z}-b_{x,y,z})^{2}},\] (13) where \(a_{x,y,z}\) represents the coordinates of reference surface point, \(b_{x,y,z}\)--the coordinates of surface point created with the proposed method;
2. Dice similarity coefficient (DSC), used to evaluate volume discrepancy: \[DSC=\frac{2|A\cap B|}{(|A|+|B|)},\] (14) where \(A\) represents the volume of the reference model, \(B\)--volume of automatically segmented bone;
3. Average intersurface distance error (ADE) was calculated by: \[ADE=\frac{1}{N}\cdot\sum_{i=1}^{n}(a_{x,y,z}-b_{x,y,z}),\] (15) where \(a_{x,y,z}\) represents the coordinates of 3D point of the reference model, \(b_{x,y,z}\)--the coordinates of 3D point of the automatically segmented model; Positive and negative ADE values were calculated also.  
Additional metric to evaluate the rapidity of segmentation the computation time of automatic segmentation was calculated. In this study, personal computer with parameters--processor--Intel(R) Core(TM) i7-4790 CPU @ 3.60 GHz, RAM--16 GB, system type--64-bit Windows 10 Operating System was used. The automatic method was implemented by using Matlab software.
## 5 Simulation Results and Discussion  
In this section, we evaluate the performance of the proposed RC-MIM-QSMS decoder with respect to the frame error rate, the convergence speed, and the memory requirement via Monte-Carlo simulations. Moreover, we also include the performance of the floating-point SBP decoder [23], the conventional QSMS decoder [27], the rate-compatible FAID (RC-FAID) decoder with flooding schedule [11], the rate-compatible MIM quantized minimum (RC-MIM-QMS) decoder [14]R2, and the RD-MIM-QSMS decoder [15] for comparison. We denote the bit width settings of the exchanged messages (\(q_{m}\)) and the a posteriori message (\(q_{p}\)) of different LDPC decoders by (\(q_{m}\), \(q_{v}\)), and the floating-point precision is represented by \"\(\infty\)\".  
We adopted a BPSK modulation and assumed the LDPC codewords were transmitted over the AWGN channels. We considered two types of LDPC codes which have moderate and short block lengths, respectively. One was the length-1296 LDPC codes adopted in the IEEE 802.11n standard [30] with code rates 2/3, 3/4, and 5/6, respectively. Another was the 5G LDPC codes constructed from a base graph one with lifting size 26 with code rates 3/4, 5/6, and 8/9 after rate matching [31]. Tables 1 and 2 show the degree distributions of the simulated codes and the design noise standard deviation (\(\sigma_{d}\)) for the associated MIM-QSMS decoder, respectively. Note that we designed the RC-FAID decoder based on DP [21] rather than the information bottleneck method [11] because DP proved to be optimal for maximizing MI. At least 300 error frames were collected at each simulated SNR. In addition, we set \(\alpha=0.8\) for the conventional QSMS decoder and \(T_{\text{max}}=15\) for all decoders.