esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Applications
### Comparative Study on the Accuracy in the Reliability Assessment and Sensitivity Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Failure Probability**} & \multicolumn{1}{c}{**FORM**} & \multicolumn{1}{c}{**SORM**} & \multicolumn{1}{c}{**SORM-i**} & \multicolumn{1}{c}{**MCS**} \\ \hline \(P_{f}\) & 0.1427 & 0.1332 & 0.1291 & 0.1287 \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Sensitivity** \\ \(\partial P_{f}/\partial d_{1}\) \\ \(\partial P_{f}/\partial d_{2}\) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CS–SORM** \\ 0.6818 \\ \(-1.1932\) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **FDM–SORM-i** \\ 0.6775 \\ \(-1.1859\) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CS–SORM-i** \\ 0.6553 \\ \(-1.1468\) \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **FDM–SORM-i** \\ 0.6493 \\ \(-1.1366\) \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **MCS** \\ 0.6511 \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the assessment of the failure probability and sensitivity analysis by the FORM, CS–SORM, CS–SORM-i, and MCS at a point \(\mathbf{d}=(0.7,0.8)\).
## 3 Results and Discussion
### Effect on Post-Treatment Storage
#### 3.5.3 Tvbn  
TVBN measures the total amount of amines and ammonia produced upon the decomposition of meat, and thus, acts as an indicator of meat freshness [51]. TVBN of CBCs during storage following ADCP treatment is shown (Table 6). The reactive species of ADCP and amino acids lead to interactions resulting in the loss of \(\alpha\)-helix and \(\beta\)-sheet, causing protein denaturation [52; 53]. However, the TVBN of CBCs following ADCP treatment did not show significant differences (\(p>0.05\)), indicating that the reactive species generated during ADCP treatment caused no significant protein denaturation in CBCs. These results may be due to reactive species generated being insufficient to induce protein denaturation in CBCs or because the WPI coating on CBCs acts as a barrier to prevent the reactive species from directly influencing chicken breasts, as described above.  
Across all storage temperatures, TVBN displayed an increasing trend in ADCP-untreated and ADCP-treated CBCs, and this increase in TVBN during storage may be attributed to protein decomposition, due to microorganisms [54]. During storage at 24 \({}^{\circ}\)C, an increase in TVBN was exhibited from 2 d onwards, which may be due to the rapid increase in indigenous mesophilic bacteria in CBCs at identical storage temperatures, facilitating protein decomposition (Figure 1A).
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### AlOHA  
The ALOHA Protocol is a form of random-access protocol that capacitates numerous communication stations in UWSNs, to send data packets that may also be in the form of data frames, over the communication channels of same features and properties at the same time. The aforementioned protocol is an uncomplicated way of communication in which each network communicating stations are given equal priority, but functions independently [43]. The main differences between carrier sense multiple access (CSMA) and ALOHA Protocols are shown in Table 7.
# Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot
###### Abstract
It is important to point out that to fit PeTra's CNN, it is necessary to provide pairs of images. The first image shows a first occupancy map built from LIDAR readings (raw data), see Figure 1c. The second image shows a second occupancy map of the same location but built from the LIDAR readings only belonging to people's legs (labelled data), see Figure 1d. To obtain labelled data, a beacon-based Real Time Location System (RTLS) was used in the first version of PeTra [9]. The people in the scene carried a mobile transceiver that gathers the beacons' signal to estimate their location. The main drawback of using a RTLS to label data is related to the measurement error of the device. However, KIO has a \(\pm\)30 cm average error, which is related to the identification problems of PeTra on specific locations. This paper poses a new data labelling method for refitting PeTra's CNN by bootstrapping. The CNN has been refitted with data labelled by PeTra itself. As a result, the CNN achieves higher accuracy. The remainder of the paper is organized as follows: Section 2 describes the materials and evaluation methods used to carry out the research; results are presented and discussed in Sections 3 and 4, respectively; Finally, conclusions and future works are proposed in Section 5.
## 2 Target Region Extraction of Foreign Object Images  
Since the performance of the detection model relies on the training result of classifiers, the complexity and diversity of the background in foreign object images obtained by UAV aerial images and video surveillance may affect the feature extraction effect. In order to improve the representativeness of the extracted features, the Otsu adaptive threshold segmentation algorithm [24] and morphological processing were adopted to extract the target region of the foreign object images, so as to remove the complex background and retain the important region with the foreign object. The implementation details and steps are described as follows.  
1. Gray the input foreign object images and divide the gray level into \(N\), i.e., (0, 1, 2,..., \(n-1\)). \(G_{i}\) represents the number of pixels with gray level \(i\) and the total of gray levels in the image are \(G_{sum}=G_{0}+G_{1}+\ldots+G_{n-1}\);
2. Set the initial threshold \(T\), define the pixels in the \([0,T]\) gray level interval as the target, and define the pixels in the \([T+1,N-1]\) gray level interval as the background.
3. Calculate the class variance \(g\) between the target and background according to Equation (1) and maximize the class variance by changing the value of \(T\), that is the best threshold \(T\) for Otsu adaptive threshold segmentation. \[g=\omega_{0}\omega_{1}(\mu_{0}-\mu_{1})^{2}\] (1) where \(\mu_{0}\) and \(\mu_{1}\) represent the average gray level of target class and background class, respectively; \(\omega_{0}\) and \(\omega_{1}\) represent the proportion of target and background pixels in the whole image and the values can be calculated according to Equation (2). \[\left\{\begin{array}{l}\omega_{0}=\sum\limits_{i=0}^{T}\frac{G_{i}}{G_{ \text{sum}}},\ \ \omega_{1}=\sum\limits_{i=T+1}^{N-1}\frac{G_{i}}{G_{\text{sum}}}\\ \mu_{0}=\sum\limits_{i=0}^{T}\frac{i\times G_{i}}{\omega_{0}\times G_{\text{sum}}},\ \ \mu_{1}=\sum\limits_{i=T+1}^{N-1}\frac{i\times G_{i}}{\omega_{0}\times G_{\text {sum}}}\end{array}\right.\] (2)
4. Morphological open operation is used to process the segmented binary image, which corrodes the image for eliminating the noise and then expands the image to obtain the connected region and colors the different regions.
5. According to the connected region of interest, the centroid and bounding rectangle are obtained, so as to mark the target region. Finally, the target region in the image is cropped and extracted.  
Taking a foreign object image of the nest as an example to show the process and effect of target region extraction based on the Otsu adaptive threshold segmentation algorithm, as shown in Figure 1.
# Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments
## 6 Towards More Efficient Parallel Stream Join on Multicore Processors with TEEs
### Optimal Partitioning Configuration  
The key observation in Equation (4) motivates the use of an MPI distributed computational model for efficient stream processing on TEE. Yet, the cautious avoidance of the computationally expensive EPC page swaps operations is essential to achieve efficiency. Hence, we adopt the Join-Matrix partitioning scheme as it naturally lends itself to our distributed model.  
Configuring the matrix dimensions of the partitioning scheme is approached pragmatically. With the assumption that streams \(R\) and \(S\) are arriving at the same rate (i.e., have an equal throughput), a buffering process is established to achieve the join-matrix dimensions calculated in Equation (5). These dimensions will ensure the efficient use of enclave memory without triggering extra EPC page swaps. Note that the tuple sizes are expressed in bytes.  
\[M_{R}=M_{S}=|\lfloor-(|R|+|S|)/2|J|\pm\frac{\sqrt{(|R|+|S|)^{2}-4\cdot Mem_{ Encl}\cdot|J|}}{2\cdot|J|}|\rfloor \tag{5}\]  
However, when the throughputs of \(R\) and \(S\) are vastly different, the adoption of a square matrix will entail large waiting times, negatively impacting the overall performance of the system. Hence \(M_{R}\) and \(M_{S}\) need to satisfy the generalized Equation (6) that takes into account the rate of tuple arrivals, effectively optimising the waiting time during the batching process. For both cases, the number of batches processed independently by secure enclaves is expressed in Equation (7).  
\[\begin{cases}M_{R}=\lfloor\lfloor-\lambda_{R}\cdot|R|/2\cdot\lambda_{S}\cdot| J|-|S|/2\cdot|J|\cr\pm\frac{\lambda_{R}\cdot\sqrt{(|R|+|S|\cdot\lambda_{S}/M_{R})^{2} -4\cdot Mem_{Encl}\cdot|J|}}{2\cdot\lambda_{S}\cdot|J|}|\rfloor\cr M_{S}=\frac{ \lambda_{S}}{\lambda_{R}}\cdot M_{R}\end{cases} \tag{6}\]  
\[\#Batches=\lceil\frac{N_{R}}{M_{R}}\rceil\cdot\lceil\frac{N_{S}}{M_{S}}\rceil \tag{7}\]
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 1 Introduction
The proposed methodology quantitatively identifies the optimal dimensional proportion between transmitting and receiving coils by systematically investigating the impact of their size ratio on the mutual inductance variation under offset conditions, thereby achieving the optimized configuration for array-coupling mechanisms. 2. The spatial position of the receiving coil is precisely identified through the comparative analysis of reflection impedance characteristics between the target power transmission coil and its adjacent coils, thereby facilitating the optimized selection and activation of the most suitable power supply coil configuration.
## 8 Discussion of the Results
As per the overshoot evaluation method, GA, CSA and PSO outperform the ZN method through reporting significant reductions in the overshoot values as presented in Table 2.  
In accordance with the rise and settling time values, GA, CSA and PSO report rise and settling time values that are considerably lower than the rise and settling time values reported by the ZN method for all inputs of the quadcopter as presented in Table 2.  
As per the fitness function values for the inputs of the quadcopter, GA, CSA and PSO reach extremely low fitness costs for all inputs, indicating that GA, CSA and PSO are robust and effective in optimizing the PID parameters for the quadcopter flight as presented in Table 3.  
The convergence curves of the evolutionary processes of GA, CSA and PSO confirm the appropriateness of these methods and the robustness of the proposed multi-objective fitness function in controlling the flight of a quadcopter. For all evaluation criteria, the meta-heuristic algorithms presented in this paper, including GA, CSA and PSO, are able to control the PID controller of the quadcopter to control its movement in all orientations. The superior performance of these algorithms in such an optimization and control problem is attributed to the astonishing features of meta-heuristics and the fitness function, which suggested carrying out such work. The meta-heuristic optimization algorithms is presented in Appendix A.
## 3 Methods
### Phase Closure Loop Residual  
The interferometric phase will introduce new phase errors in the process of difference, unwrapping, etc., resulting in a non-zero phase closure of interferogram triplets. These phase un-closed areas usually appear in the jump of the unwrapping phase [60]. These residuals constitute an important index by which to judge the quality of unwrapped interferometric phases [61]. Assuming that the original phases of three SAR images are \(\mathcal{O}_{1}\), \(\mathcal{O}_{2}\), and \(\mathcal{O}_{3}\), the differential phases of their mutual interference are \(\mathcal{O}_{12}\), \(\mathcal{O}_{23}\), and \(\mathcal{O}_{13}\), and the corresponding unwrapped phases are \(\Phi_{1}\), \(\Phi_{2}\), and \(\Phi_{3}\). Then, the phase closure loop residual can be expressed as [62]:  
\[\Delta_{123}=\mathcal{O}_{12}+\mathcal{O}_{23}-\mathcal{O}_{13} \tag{8}\]  
Due to the influence of residuals such as DEM error, orbit error, and atmospheric error in data processing, there will be phase triangle closed residuals [62]. Therefore, these residuals are an important index by which to judge the quality of the unwrapped image. The RMSE (root mean square error) of the residual error of the unwrapped phase closed loop can reflect how much phase unwrapped error is included in the closed loop [51] so that the quality of the relevant interferogram can be deduced.
## 2 Materials and Methods
### Grating Fabrication Process
#### 2.2.1 Photoresist Pattern Formation  
The exposure of photoresist layers was performed using EVG mask aligner (EV Group, Sankt Florian am Inn, Austria) with 2.85 mW/cm\({}^{2}\) radiation intensity (0.15 mW/cm\({}^{2}\) deviation) using the filter for wavelengths shorter than 365 nm. The photomask for exposure was a 5-inch Cr-Quartz mask purchased from Compugraphics (Jena, Germany) with four regions of different periods and shapes, and the area of each region was 1 cm\({}^{2}\). The exposure was carried out in soft contact mode (no vacuum) in order to prevent the sticking of the photoresist layer to the mask. The exposure dose values are listed in Table 1. Theoretically, the optimal dose depends on the photoresist thickness, solvent, UV sensitivity, absorption in the exposure spectrum, power, and coherence of UV lamp, but for fabrication purposes it was empirically estimated using the sensitivity curve method. For thicknesses higher than 20 \(\upmu\)m, the optimal dose values were extrapolated from the sensitivity curve data.  
Post-exposure bake and development steps were the same as for the contrast curve sample set. Development times were adjusted according to the thickness of the photoresist layer. Optical microscope images of the gratings were obtained to perform a preliminary quality assessment of periodicity and homogeneity of the pattern. Sharp definition of the structures and a high degree of periodicity was observed for all periods for photoresist thickness up to 20 \(\upmu\)m. However, for a photoresist thickness of 30 \(\upmu\)m and thicker, structures with a period of 10 \(\upmu\)m undergo undesirable crosslinking caused by the diffraction effect, which made square structures appear round. This resulted in structure size loss, as the final structures were electroplated using a structured photoresist layer as a mask for the conductive wafer surface.  
Figure 2: Fabrication process steps of UV LIGA: substrate preparation consisting of cleaning of the surface and applying the photoresist; photoresist pattern formation with exposure and development; and gold pattern formation by electroplating and stripping of the photoresist.
## 3 Problem Descriptions and Optimization  
This paper focuses on designing a distributed RA policy for EH-STs in satellite communication networks. Following this way, all the EH-STs in the networks are considered to have their own local information, i.e., the energy level at the current time and the EH rate. Therefore, EH-ST \(u\) decides whether to transmit the packet in the current frame \(k\) or keep idle based only on \(E_{u,k}\), independent of the energy level of other EH-STs (\(E_{i,k},i\
eq u\)). For the given \(E_{u,k}\), the probability of EH-ST \(u\) transmitting its current packet (\(Q_{u,k}=1\)) with \(l_{u,k}\) replicas is \(\eta\left(E_{u,k}\right)\), which is the transmission policy of our design. Supposing there are \(M\) EH-STs transmitting packets in the same frame duration, given the initial state of energy levels \(E_{\mathbf{0}}=e_{\mathbf{0}}\in E^{U}\), the policy \(\mathbf{\omega}\), the number of packet replicas \(l\), and the number of time slots in a frame \(N\), the average long-term throughput contributed by the specific EH-ST \(u\) can be denoted as follows:  
\[T_{\mathbf{\omega}}^{\left(u\right)}\left(e_{\mathbf{0}}\right)=\lim_{K\to\infty}\frac {1}{K}\Bigg{[}\sum_{k=0}^{K-1}\big{(}\left(1-\mathit{PLR}\left(M,l,N\right) \right)\eta_{u,k}\cdot\binom{U}{M}\times\prod_{i\
eq u,j}\eta_{i,k}\prod_{j \
eq u,j}\left(1-\eta_{j,k}\right)\big{)}\ |\ e_{\mathbf{0}},\mathbf{\omega}\Bigg{]}, \tag{7}\]  
where \(i\) indicates the other co-transmitted EH-STs in addition to EH-ST \(u\), and \(j\) represents the rest of the EH-STs which keep idle. The term \(1-\mathit{PLR}\left(M,l,N\right)\) represents the throughput contributed by EH-ST \(u\), and the term \(\binom{U}{M}\prod_{i\
eq u,j}\eta_{i,k}\prod_{j\
eq u,i}\left(1-\eta_{j,k}\right)\) is a binomial operator which stands for the probability of \(M\) EH-STs transmitting packets. Specifically, the average probability of EH-ST \(u\) transmitting \(l_{u,k}\) replicas in energy level \(e\) is denoted as:  
\[\eta_{u}\left(e\right)=\Pr\left[Q_{u,k}=1\ |\ E_{u,k}=e\right]. \tag{8}\]  
Furthermore, the expected throughput contributed by EH-ST \(u\) in energy level \(e\) is denoted as \(g\left(\eta_{u}\left(E_{u,k}\right)\right)\), which is given by:  
\[g\left(\eta_{u}\left(E_{u,k}\right)\right)=\mathbb{E}\left[1-\mathit{PLR}(M,l,N)\ |\ E_{u,k}=e\right]. \tag{9}\]  
\(g\left(\eta_{u}\left(e\right)\right)\) is a concave function w.r.t the traffic load, which is related to the transmission probability \(\eta_{u}\left(e\right)\).
## 4 Proposed System
### Performance Metrics
#### 4.1.3 Latency  
It is genuinely challenging to gather the latency in the entire stream as it is a complex metric. Therefore, it is achieved through sample slicing off some of the records from the incoming stream, followed by an appropriate estimation. Records sampling is completed as well, to prevent any potential degradation of performance in the system, and specific records are marked from the source location, enabling the sink operator to identify them during calculation, as these records are the only ones required.  
Marking can be done randomly (through random selection algorithm), or at specified times depending on circumstance. This way, the sink operator know the exact records to use in latency calculations. The Job Manager (master node) calculate the latency through following Equation (2).  
\[\textit{Latency}=\ t_{finish}-\ t_{start} \tag{2}\]  
where \(t_{finish}\) is the finish time of the marked sample and \(t_{start}\) is the entering time of the sample record in the execution pipeline.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 4 Case Study
### Conceptual Model
#### 4.1.2 Safety Conditions  
Safety conditions refer to providing the necessary actions to face contingencies such as fires. To achieve this end, the conditions that determine a fire are precisely described, and the rooms are equipped with the necessary detection equipment that provides the required information on time. The conditions that are jointly fulfilled to determine the existence of fire are:  
* The most recent temperatures obtained by the sensor are VERY HIGH, that is, well above the usual limits of high temperatures in hot or desert climates;
* The presence of high concentrations of smoke, which is a widely known sign of fire;
* The presence of toxic gases, such as carbon monoxide, is additionally a venous gas.  
The safety conditions are established as conditions in the corresponding transitions in the service patterns as analyzed in the computational models exposed next.
## 3 Experimental Setup  
Figure 8a depicts the experimental setup for a 120 Gb/s PS-64 QAM coherent optical communication system. At the transmitter side, the pseudo-random binary sequence (PRBS) is generated by a Matlab program. A constant composition distribution matcher (CCDM) [24; 25] is used to generate non-uniform 64 QAM signals with a Maxwell-Boltzmann distribution. In this paper, we set \(v\) as 0.05. Then the non-uniform 64 QAM signals are mapped to a PS-64 QAM constellation. The analog signals are sent into a 25 GSa/s arbitrary waveform generator (AWG). The signal output from the AWG is amplified by two electric amplifiers (EAs) and fed into the I/Q modulator to generate modulated signals. An external cavity laser (ECL) is used to generate a continuous optical wave with a linewidth of 100 kHz and wavelength of 1550 nm. The polarization-division-multiplexing (PDM) module includes a polarization-maintaining optical coupler (PM-OC), a polarization controller (PC), optical delay line, and a polarization beam combiner (PBC). An erbium-doped fiber amplifier (EDFA) is set before the variable optical attenuator (VOA) to amplify the signals. The VOA is also used to adjust the signal power.  
The transmission link consists of five spans of G.652D single-mode fiber (SMF) with a length of 75 km. There is an EDFA following each span to compensate for the fiber loss. There are five spans in this system. At the receiver side, an ECL with a linewidth of 100 kHz is used as the local oscillator (LO) for coherent detection. Then, the optical signals are detected by the coherent receiver. A 100 GSa/s digital phosphor oscilloscope (DPO) is used to digitize the signals. The offline digital signal processing (DSP) flow chart is shown in Figure 8b. The offline DSP includes the low-pass filter, amplitude normalization, chromatic dispersion compensation (CDC), clock recovery, resampling, Gram-Schmidt orthogonalizing process (GSOP), constant modulus algorithm (CMA) equalization, frequency offset estimation (FOE), carrier phase recovery (CPR), CS M-ary SVM NLE, PS-64 QAM demapping and BER calculation.  
Figure 7: Classification strategy for (**a**) inner constellation points and (**b**) outer constellation points.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 3 The Greedy Compact Multicast Routing (GCMR) Scheme
### Basic Functionality
Moreover, GCMR is adaptive, meaning that the routing decisions may be modified once there is a change in the information that has led to that decision. This change can be (i) a (node/link) failure in some part of the MDT and GCMR needs to restore the MDT or (ii) a given sequence of join/leave events that lead to a sub-optimal multicast routing path and GCMR needs to optimise the MDT. GCMR treats both cases in the same way and causes the execution of the adaptability mechanism which is based on a modified two-stage search process explained above. Nonetheless, in this paper we only focus on the adaptation of the MDT in case of failure events. In this context, the adaptation is executed by the upstream node of the (node/link) failure in the MDT. The next section describes three examples to illustrate the behaviour of GCMR when (i) a node joins an MDT with a local search, (ii) a node joins an MDT with the global search (i.e., the local search fails), and (iii) a link failure in one of the edges of the MDT disconnects some leaf nodes.