esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning for Computational Mode Decomposition in Optical Fibers
###### Abstract  
Multimode fibers are regarded as the key technology for the steady increase in data rates in optical communication. However, light propagation in multimode fibers is complex and can lead to distortions in the transmission of information. Therefore, strategies to control the propagation of light should be developed. These strategies include the measurement of the amplitude and phase of the light field after propagation through the fiber. This is usually done with holographic approaches. In this paper, we discuss the use of a deep neural network to determine the amplitude and phase information from simple intensity-only camera images. A new type of training was developed, which is much more robust and precise than conventional training data designs. We show that the performance of the deep neural network is comparable to digital holography, but requires significantly smaller efforts. The fast characterization of multimode fibers is particularly suitable for high-performance applications like cyberphysical systems in the internet of things.  
deep learning; digital holography; few-mode fiber; mode decomposition Article
# Matheuristics and Column Generation for a Basic Technician Routing Problem
## 4 Matheuristics Based on Compact MILP Formulations
### VND Local Search Matheuristic
#### 4.5.1 General Algorithm  
We present here the general local search algorithm described in Algorithm 4, similarly to [4]. The neighborhoods are defined here using MILP definitions. MILP neighborhoods allow exploring large neighborhoods, using recent progresses of MILP solvers, having fewer and better local optimums than small neighborhoods approaches. Many MILP neighborhoods can be defined; Algorithm 4 changes systematically the choice of the neighborhood within the local search, similarly with multi neighborhood search approaches. It induces that a local optimum for the entire local search is a local optimum for all the neighborhoods considered in Algorithm 4. It ensures having less and better local optimums than classical local search approaches. The stopping criterion could be a maximal time limit or a maximal number of iterations or being in a local extremum for all neighborhoods.  
```
Input: an initial solutions, a set and order of neighborhoods to explore Initialization: currentSol = initSolution, \(\mathcal{N}~{}=~{}\text{initial neighborhood}\). while the stopping criterion is not met  define the MILP with incumbent currentSol and the neighborhood \(\mathcal{N}\)  define currentSol as warmstart  currentSol = solveMILP(MILP,timeLimit(\(\mathcal{N}\))) \(\mathcal{N}=\text{next}\text{Neighborhood}(\mathcal{N})\) endwhile returnCurrentSolution
```  
**Algorithm 4** VND with MILP neighborhoods  
Usually, MILP neighborhoods are defined for small sub-problems where exact B&B converges quickly to optimality. MILP neighborhoods are defined with three characteristics, for an efficient MILP neighborhood search includes small time limits without the optimality guarantee. Parameterization is empirical for a good trade-off between solution quality and time spent in MILP solving. The current solution is the primal solution given by the last B&B resolution, and it is also defined as warmstart for the next B&B resolution to improve the efficiency of B&B primal heuristics, enabling RINS or Local Branching heuristics from the beginning. This ensures that the solution given by the MILP resolution is at least as good as the current solution at each iteration. This algorithm is, thus, a steepest descent algorithm.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 3 Proposed Method
### Quantum Circuit for GIFT Block Cipher
#### 3.2.3 AddRoundkey of GIFT Block Cipher  
In the AddRoundkey operation, the round key \(RK\) (\(n/2\)-bit) is XORed to the block \(B\) (\(n/2\)-bit). The XOR operation can be done with the CNOT gate. At this time, the result of qubit should be \(B\). The AddRoundkey of GIFT-64/128 and GIFT-128/128 block ciphers are similar, only the number of bits is different. In the GIFT-128/128 block cipher, double the CNOT gates are used compared to the GIFT-64/128 block cipher. Quantum circuits for Addroundkey of GIFT-64/128 and GIFT-128/128 block ciphers are shown in Algorithm 7 and Algorithm 8, respectively.  
```
0: 64-qubit block \(B(b_{63},...,b_{0})\), 32-qubit round key \(RK(rk_{31},...,rk_{0})\).
0: 64-qubit block \(B(b_{63},...,b_{0})\) after AddRoundKey.
1:for\(i=0\) to 15 do
2:\(b_{4i}\leftarrow\) CNOT \((rk_{i},b_{4i})\)
3:\(b_{4i+1}\leftarrow\) CNOT \((rk_{i+16},b_{4i+1})\)
4:endfor
5:return\(B(b_{63},...,b_{0})\)
```  
**Algorithm 7** Quantum circuit for AddRoundkey of GIFT-64/128 block cipher  
Figure 10: Quantum circuit for Sbox of GIFT block cipher.  
```
128-qubit block \(B(b_{127},...,b_{0})\), 64-qubit round key \(RK(rk_{63},...,rk_{0})\).
2128-qubit block \(B(b_{127},...,b_{0})\) after AddRoundKey.
3
4:for\(i=0\) to 31 do
5:\(b_{i+1}\leftarrow\) CNOT \((rk_{i},b_{4i+1})\)
6:\(b_{4i+2}\leftarrow\) CNOT \((rk_{i+32},b_{4i+2})\)
7:endfor
8:return\(B(b_{127},...,b_{0})\)
```  
**Algorithm 8** Quantum circuits for AddRoundKey of GIFT-128/128 block cipher
## 2 Materials and Methods
### Bioinformatic Analysis  
The original image data obtained by high-throughput sequencing were converted to sequence data by CASAVA base calling. Each sample generated more than 8 gigabytes of data. For further analysis, the reads were filtered from raw sequencing data by removing adaptors and trimming low-quality (<Q30) reads. The processed reads were then mapped to the reference _Brassica napus_ genome ([[http://brassicadb.org/brad/datasets/pub/Genomes/Brassica_napus/](http://brassicadb.org/brad/datasets/pub/Genomes/Brassica_napus/)]([http://brassicadb.org/brad/datasets/pub/Genomes/Brassica_napus/](http://brassicadb.org/brad/datasets/pub/Genomes/Brassica_napus/))) [19], and transcript levels of unigenes were identified by HISAT2 (v2.0.5) [20; 21]. The mapped reads for each sample were assembled using StringTie (v1.3.3) [20; 22] with a reference-based approach. This method employs spliced reads to determine exon connectivity.  
Cuffdiff (v1.3.0) [23] was utilized to calculate the fragments per kilobase of exon per million fragments mapped (FPKMs) for coding genes in each sample. Gene FPKMs were computed by summing the FPKMs of the transcripts in each gene group, which were calculated based on the length of the fragments and the read count mapped to each fragment. Cuffdiff (v2.2.1) [23] provides statistical methods for determining differential expression in digital transcript or gene expression data sets using a model based on a negative binomial distribution. Significantly differentially expressed genes (DEGs) were identified with the following criteria: corrected \(p\)-value \(<0.05\) (false discovery rate (_FDR_) \(<0.05\)) and \(\mathrm{log_{2}(fold\;change)}\)\(\geq 1\).  
Gene functional annotations were based on _B. napus_ ([[http://www.genoscope.cns.fr/brassicanapus/](http://www.genoscope.cns.fr/brassicanapus/)]([http://www.genoscope.cns.fr/brassicanapus/](http://www.genoscope.cns.fr/brassicanapus/))) and _Arabidopsis_ ([[https://www.arabidopsis.org/index.jsp](https://www.arabidopsis.org/index.jsp)]([https://www.arabidopsis.org/index.jsp](https://www.arabidopsis.org/index.jsp))) genome databases and matched to GO terms ([[http://www.geneontology.org/](http://www.geneontology.org/)]([http://www.geneontology.org/](http://www.geneontology.org/))). GO enrichment analysis was carried out using PerlModule (GO: TermFinder) [24]. GO terms with a corrected \(p\)-value \(<0.05\) were considered to be significantly enriched among the DEGs. R functions (phyper and qvalue) were applied to test for statistical enrichment of DEGs among Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. KEGG pathways with a corrected \(p\)-value \(<0.05\) were considered to be significantly enriched among the DEGs.
## 4 Results
### Ablation Analysis  
Table 6 presents the results of the Ablation Analysis, comparing the proposed original model (O) against the four variants where specific modeling features are removed. The  
Figure 8: Distribution of the suggested sampling rate over different technologies.  
Figure 7: (**a**) Percentage of adoption of the different inspection technologies; (**b**) percentage of installation of inspection technologies over the assembly stations.  
comparison focuses on two metrics: the change in the objective function value (delta in percentage) and the change in computational time (also in percentage).  
The Ablation Analysis demonstrates that the most significant impact is observed when the model is restricted from using machine vision inspection (NO-MVI), resulting in a 42.6% increase in the objective function value compared to the proposed model (P). This substantial degradation highlights machine vision's critical role in achieving cost-effective quality control strategies. Interestingly, this variant also reduced the computational time by 26.6%, suggesting that removing this option simplifies the search space, albeit at the expense of optimality. Excluding assisted human inspection (NO-HAI) and human inspection (NO-HI) results in much smaller increases in objective value--1.5% and 0.7%, respectively--while yielding noticeable improvements in computational time in the 24-27% range. These findings indicate that while both forms of human-based inspection contribute marginally to solution quality, they increase the model complexity and solution effort. Fixing the sampling rate (MCN) yields the lowest change in the objective function but leads to an 8% increase in the computational time. This suggests that while the model can adapt inspection volumes with little gain in performance for this case study, enforcing fixed control levels introduces additional constraints that may slightly hinder the solver efficiency.
## 3 Preliminaries
### Communication Model  
In this paper, we employ a probabilistic underwater acoustic communication model as described in [25; 26]. This model takes account of the frequency and distance-dependent signal attenuation in water, as well as the noise disturbances in the underwater environment, such as turbulence, shipping activity, thermal noise, and wind.  
Due to the absorption of the medium itself, the extension of wavefront during acoustic propagation, and scattering phenomenon caused by components such as magnesium sulfate and magnesium borate in the seawater, high speed communications over underwater acoustic channels are complicated but can be characterized by path losses. Based on a given distance \(d\) and frequency \(f\) (in kHz), the path loss can be calculated by an empirical formula given in [27]:  
\[A(d,f)=A_{0}d^{k}a(f)^{d} \tag{1}\]  
where \(A_{0}\) is a normalizing constant, and \(k\) is the spreading loss with typically used values of \(k=1\) for cylindrical spreading, \(k=1.5\) for practical spreading, and \(k=2\) for spherical spreading. Additionally, \(a(f)\) represents the absorption coefficient (in dB re km), which can be described by the [PERSON]'s formula empirically as [28]:  
\[10\log a(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75\times 10^ {-4}f^{2}+0.003 \tag{2}\]  
The ambient noise is influenced by many factors: turbulence, thermal noise, shipping activity, and wind-driven waves. The noise can be finally modeled as [27]:  
\[10\log N_{t}(f) =17-30\log f \tag{3}\] \[10\log N_{th}(f) =-15+20\log f \tag{4}\]\[10\log N_{\text{s}}(f)=40+20(s-0.5)+26\log f-60\log(f+0.03) \tag{5}\]  
\[10\log N_{w}(f)=50+7.5w^{1/2}+20\log f-40\log(f+0.4) \tag{6}\]  
where \(s\) ranging from 0 to 1 represents the surface shipping activity for low or high activity, and \(w\) is the speed of wind (in m re sec). The final noise level is the summation of these four factors:  
\[N(f)=N_{t}(f)+N_{\text{s}}(f)+N_{w}(f)+N_{th}(f) \tag{7}\]  
Next, for moderate transmitted power \(P\) and signaling bandwidths \(B\), the signal-to-noise ratio (SNR) at the receiver at a frequency \(f\) and distance \(d\) can be expressed as:  
\[SNR(d,f)=\frac{P/A(d,f)}{N(f)B} \tag{8}\]  
Using the underwater acoustic communication model described above, a symbol error rate based on a SNR can be approximated as [25]:  
\[P_{e}=\frac{1}{4 SNR} \tag{9}\]  
For a packet with \(N\) symbols, the error probability of transmission is then:  
\[P_{packet}=1-(1-P_{e})^{N} \tag{10}\]  
Ultimately, for a packet with \(N\) symbols transmitted \(m\) rounds, the successful transmission probability can then be computed as:  
\[P=1-P_{packet}^{m} \tag{11}\]  
Above all, the successful transmission probability is eventually a function of the transmission frequency, channel bandwidth, transmitted power, turbulence, shipping activity, thermal noise, wind speed, and transmission rounds.
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 2 PPP Mathematical Model and Satellite Selection Algorithms
### The Fast-Rotating Partition Satellite Selection Algorithm  
The fast-rotating partition satellite selection algorithm meets the GDOP requirements of positioning and can greatly reduce the computation time when compared with the traditional optimal DOP satellite selection algorithm. The computation time was almost unaffected by the number of satellites selected, and slightly affected by the number of satellites in the field of view [19]. However, the description of the original algorithm is complex and difficult to implement in programs. In this study, we provide a concise mathematical function model of this algorithm and have made a few adjustments in the steps. The algorithm consists of the following four steps.  
Step 1: According to the elevation mask angle and SV Health, select the healthy satellites.  
Step 2: As shown in Figure 1, divide the satellite sky for \(n\) partitions according to the number (\(n\)) of visible satellites.  
Step 3: Select the satellite that is closest to the partition midline in each partition. Calculate the absolute value of the azimuth difference between the satellite and partition midline. Calculate the average of absolute values of all partitions. The scheme is one of optional schemes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Average Number of Visible Satellites at JFNG**} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Selected Satellites** \\ \end{tabular} } & **G** & **GC** & **GR** & **GR** \\ \cline{2-5}  & **9** & **15** & **19** & **25** \\ \hline
4 & 126 & 1365 & 3876 & 12,650 \\
5 & 126 & 3003 & 11,628 & 53,130 \\
6 & 84 & 5005 & 27,132 & 177,100 \\
7 & & 6435 & 50,388 & 780,700 \\
8 & & 6435 & 75,582 & 1,081,575 \\
9 & & 5005 & 92,378 & 2,042,975 \\
10 & & 3003 & 92,378 & 3,268,760 \\
11 & & & 75,582 & 4,457,400 \\
12 & & & 50,388 & 5,200,300 \\ \hline \hline \end{tabular}  
* G, R, and C denote GPS, BDS, and GLONASS, respectively.  
\end{table}
Table 1: The number of all possible combinations, \(C_{N}^{n}\).  
Step 4: Rotate the midline of each partition at the same time according to a certain angle (\(j\)) to form a new dividing situation of the sky. Repeat steps 1-3. The scheme with the minimum absolute value is chosen as the final satellite selection scheme. The mathematical equation is as follows.  
\[\begin{array}{l}Sum=\min\sum\limits_{i=1}^{n}\Big{\{}\alpha_{s_{i}}-\Big{[} \frac{180}{n}+\frac{360}{n}(i-1)+j\Big{]}\Big{\}},\quad 1\leq j\leq[\frac{360}{n}]\\ \alpha_{s_{i}}=\left\{\begin{array}{l}\alpha_{s_{i}},\quad\alpha_{s_{i}}>j \text{ and }\alpha_{s_{i}}\
eq 0\\ \alpha_{s_{i}}+360,\quad 0\leq\alpha_{s_{i}}<j\end{array}\right.,\end{array} \tag{8}\]  
where \(n\) is the number of visible satellites; \(i\) is the number of partitions; \(\alpha_{s_{i}}\) is the azimuth angle of the satellite \(S_{i}\); \(S_{i}\) is the nearest satellite to the midline in partition \(i\); \(j\) denotes the rotation angle.
## 3 Results and Discussion
### HS-SPME Method Optimization
#### 3.1.2 The Effect of Dilution  
Sample dilution is an approach that modifies the sample matrix and influences the HS-SPME efficiency [55]. We hypothesized that undiluted samples would yield higher total peak area and total peak numbers, which is confirmed by our results (Figure 2). Specifically, the non-diluted samples obtained an average of 2.69 \(\times\) 10\({}^{7}\) total peak area, whereas diluted samples obtained 2.17 \(\times\) 10\({}^{7}\), showing statistically significantly different (\(p\) value = 0.0167). In addition, the non-diluted samples produced a mean of 290 peaks, which is statistically significantly higher than the 237 peaks obtained from the diluted samples.  
It has been reported that for samples with complex and high-concentration matrices, adding diluent may increase the extraction recovery by reducing the interference from matrix components and promoting the shift of analytes to the free form [3]. However, excessively diluting the sample may decrease sensitivity due to a reduced concentration. In two HS-SPME optimization studies [56; 57], the authors observed that adding diluent initially increased the extraction performance, but an excessive dilution led to a decrease in extraction efficiency. Another study on plasma samples reported that adding diluent reduced the amount of extracted volatile compounds except for diazepam, which tends to bind strongly with the proteins in plasma [58]. BALF is a saline solution primarily composed of alveolar macrophages and some lymphocytes [59]. We suspect that the BALF is not a complex matrix; hence, the addition of diluent led to a decrease in sensitivity.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Benefits of Using IoT in Healthcare  
The IoT is expected to have a significant impact on how healthcare is provided. We have entered a completely new era in terms of the interaction between applications, technologies, and people providing healthcare solutions. The ability to create an integrated healthcare network with the help of the IoT has dramatically improved, giving new insights and tools. Healthcare processes that formerly needed a lot of time and were error-prone due to human involvement can now be automated thanks to IoT and AI. For instance, a lot of hospitals now employ networked equipment to regulate temperature and ventilation in operating rooms. There are several ways that IoT may advance healthcare, but the following are some of the more significant advantages:  
* Reduced probability of human errors,
* Resolution of restrictions regarding distance and physical visits,
* Less paperwork and record-keeping,
* Early detection of chronic disorders,
* Improved handling of medication,
* Immediate medical attention,
* Improved results of therapy.
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 3 Landing Environment
### Deck Motion  
The 6 DOF motion of the aircraft carrier under the impacts of wind and waves can be approximated as general rigid motion. The linear motions along the three axes are surge motion \(\Delta x_{c}\), sway motion \(\Delta y_{c}\) and heave motion \(\Delta z_{c}\), while the angular motions around the three axes are pitch motion \(\theta_{s}\), roll motion \(\phi_{c}\) and yaw motion \(\psi_{c}\). To simulate the aircraft carrier motion-triggered perturbation action, the theory of stationary random processes in reference [41] is cited here, and transfer functions are used to describe the deck motion, with the transfer functions of the aforementioned linear and angular motions expressed as  
\[\left\{\begin{aligned} & G_{T}(s)=\frac{b_{3}s^{2}+b_{3}s+b_{1}}{s^{4} +a_{4}s^{3}+a_{5}s^{2}+a_{2}s+a_{1}}\\ & G_{A}(s)=\frac{d_{3}s^{2}+d_{3}s+d_{1}}{s^{5}+c_{4}s^{3}+c_{3}s^{ 2}+c_{2}s+c_{1}}\end{aligned}\right. \tag{10}\]  
The ideal touchdown point of the CBA is located between the second and third arresting cables in the middle landing runway on the carrier deck. Figure 1 shows the relationship between the ideal touchdown point and the carrier's centre of gravity.  
The causes of motion at the ideal touchdown point are divided into two parts, which are described in detail below:  
\[\begin{cases}\Delta x_{1}=\Delta x_{c}\cos\psi_{c}-\Delta y_{c}\sin\psi_{c}\\ \Delta y_{1}=\Delta x_{c}\sin\psi_{c}+\Delta y_{c}\cos\psi_{c}\\ \Delta z_{1}=\Delta z_{c}\end{cases} \tag{11}\]  
\[\begin{cases}\Delta x_{2}=-L_{TD}\cos\psi_{c}+L_{TD}-Y_{TD}\sin\psi_{c}-Z_{TD} \sin\theta_{c}\cos\psi_{c}\\ \Delta y_{2}=-L_{TD}\sin\psi_{c}-Y_{TD}\times Y_{TD}\cos\psi_{c}+Z_{TD}\sin \phi_{c}\cos\psi_{c}\\ \Delta z_{2}=L_{TD}\sin\theta_{s}+Y_{TD}\sin\phi_{c}-Z_{TD}\sin\phi_{c}\cos \theta_{c}+Z_{TD}\end{cases} \tag{12}\]  
where \([\Delta x_{1},\Delta y_{1},\Delta z_{1}]\) and \([\Delta x_{2},\Delta y_{2},\Delta z_{2}]\) are formed by the aforementioned linear and angular motions along the carrier deck, respectively, and \([L_{TD},Y_{TD},Z_{TD}]\) is the distance vector between the ideal touchdown point and the centre of gravity of the deck. Therefore, the ideal touchdown point can be expressed as  
\[\begin{cases}x_{p}=V_{c}t\cos(\psi_{c}+\psi_{0})+\Delta x_{1}+\Delta x_{2}\\ y_{p}=V_{c}t\sin(\psi_{c}+\psi_{0})+\Delta y_{1}+\Delta y_{2}\\ z_{p}=\Delta z_{1}+\Delta z_{2}\end{cases} \tag{13}\]  
where \(V_{c}\) is the cruising speed of the aircraft carrier, \(\psi_{c}\) (=0 deg) is its heading angle and \(\psi_{0}\) is the angle between the carrier's cruising direction and deck centre line.