esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Medieval Music Recognition--A Complete Pipeline for Historic Chants
## 8 Discussion
### Text Recognition  
As previously established in the evaluation, text recognition without post-processing performs poorly. In the paper, a post-processing method was introduced and evaluated, demonstrating its usefulness. This section will delve further into text recognition, with an additional experiment created to provide an extended evaluation of the post-processing method and shed light on other relevant aspects.  
To address these questions, several experiments were conducted: (1) Is additional fine-tuning on text recognition and post-processing necessary? (2) How many errors are produced by splitting chants into lines based on inaccurate OCR output? For this, 10 extra pages from the Mul 2 book (not included in the Mul 2 dataset) were used for fine-tuning and then compared with the model without fine-tuning. Furthermore, during evaluation, chant-based post-processing was optionally enabled or disabled.  
Figure 7: Example of actions needed in the OMMR4 all editor to correct errors in the transcription marked by a blue arrow. Symbol drag: the marked symbol needs to be adjusted closer to the staff line so that it is recognized by the system as being on the staff (yellow square). Symbol insert: insert a missing symbol. Symbol delete: the custody at the end of line must be removed as it is not part of the melody. Syllable assignment: the syllable was incorrectly assigned to a note and needs to be corrected by moving it to a new note via drag and drop.  
Table 7 presents the results of this evaluation. For each experiment, three values were calculated: Line CER, Chant CER, and WER. These metrics measure the number of actions (Insert, Delete, Replace) required to convert predicted transcriptions into the ground truth sequence. The three metrics differ only in their pre-processing of the sequence. In the case of Line CER, predicted lines are compared with ground truth lines; for Chant CER, the entire predicted lyrics of a chant are compared with the ground truth lyrics of that chant; and for WER, individual words are treated as tokens instead of individual characters.  
The results show that, as expected, fine-tuning leads to improvements. However, the difference between fine-tuning and not fine-tuning is not substantial. Errors occur in both cases, and already without fine-tuning, reasonable results can be achieved.  
It is also worth noting that while the error rate decreases with post-processing, the errors also shift. Without post-processing, approximately 40-60% of all words were incorrect, but this number falls to 9-12% with post-processing. This is significant for manual correction as it is easier to correct individual words than multiple single letters.  
Additionally, approximately 1% of errors are due to suboptimal segmentation. Nevertheless, chant-based post-processing is crucial for the quality of automatic transcription.
## 2 Scientific Instrument FXE for Chemical Dynamics Studies
### The Pump Laser System and Synchronization  
Chemical reactions in solution phase are triggered by femtosecond laser radiation provided by a specially developed burst-mode femtosecond laser system, the Pump-Probe Laser (PPL) [39]. The system consists of an amplified slab laser producing 800 fs long pulses with 1030 nm wavelength and a non-collinear optical parametric amplifier (NOPA) to generate broadband laser pulses centered at 800 nm, which are routinely compressed down to 15 fs pulse duration. Consequently, a very robust and dispersion controlled beam propagation setup is installed for making the shortest possible pulses available at the experiment interaction point. The 800 nm radiation can be subsequently frequency-converted to the second (400 nm) or third (267 nm) harmonics. Further tunability of the excitation wavelength will be provided via using a TOPAS system currently under installation and commissioning. The PPL system is synchronized to the XFEL machine using both the RF electronic reference signal and an optical fiber-based synchronization scheme, and the resulting arrival time jitter was measured at the sample position using an interferometric timing-tool scheme [34]. As a result,  
Figure 2: Layout of the scientific instrument FXE in the experimental hall of European XFEL. The SASE X-ray beam enters the experimental hutch from the right and first passes the optics and diagnostics branch (O) inside the experimental hutch (EH) [34]. The beam is focused in the sample interaction region (S) and can optionally be transported through the LPD central hole to the post-diagnostics table (P) hosting beam imaging, spectral diagnostics and intensity-position monitor units. The optical laser radiation, the excitation source for the pump-probe experiments, is conditioned and characterized in a separate instrument laser hutch (ILH), the entire experiment is remotely conducted from the control hutch (CH).  
the RF synchronization scheme delivers a coarse timing accuracy with a remaining 250-300 fs FWHM jitter. This accuracy is significantly increased using the optical synchronization scheme, yielding a residual timing jitter of about 70 fs FWHM.
## 4 Simulation Results
### Retrieval Using Rectangular Basis Functions
We propose the following piecewise linear (PL) function to process the retrieved \(\{\hat{w}_{n}\}_{n=1}^{N}\) after the first least-squares computing:  
\[w_{n}^{\prime}=\frac{\hat{w}_{n}}{B(\hat{w}_{n})}, \tag{35}\]  
where  
\[B(x)=\left\{ \begin{array}{ll}2.8&\text{for }x<0.5\\ 1.9&\text{for }0.5\leq x<1\\ 1.8&\text{for }1\leq x<1.5\\ 1.6&\text{for }1.5\leq x<2\\ 1.5&\text{for }x\geq 2\end{array} \right.. \tag{36}\]  
The value of \(B(x)\) for each interval is chosen empirically from multiple simulations to achieve the best retrieval outcome. The retrieved attenuation field by applying \(\{w_{n}^{\prime}\}_{n=1}^{N}\) on Equation (15) is shown in Figure 3d, which is very similar to the retrieved field by averaging the third and fourth iterations. In reality, the basis functions can only be an approximation of the actual field. Different types of basis functions will introduce different levels of errors into the retrieval model. To investigate the effects of larger rectangle basis functions, we consider the retrieved attenuation field represented by \(160\times 25\) rectangular basis functions for the second set of simulations. The width of each function (\(u\)) is 500 m, i.e., each unit square is now four times as big as before in area. We use the same PL function to  
Figure 4: The plots of \(F_{n}\) (red line), \(A_{I}\) (blue dashed line) and \(T_{sky}\) (black line) over the 400 samples during the overpass of the satellite. Panel (**a**) and panel (**b**) are for the receivers located at 40 km and 60 km, respectively. process the retrieved \(\hat{w}_{n}\). The final retrieved attenuation field is shown in Figure 3e. It can be seen that similar results are achieved but with poor spatial resolutions. As a global index of the performance for the retrieval, the root-mean-square error (RMSE) between the retrieved attenuation field and the reference field has been considered, which is defined by  
\[\text{RMSE}=\sqrt{\frac{1}{W}\sum_{i=1}^{W}\bigl{(}R_{i}-R_{i}^{\prime}\bigr{)} ^{2}}, \tag{37}\]  
where \(W\) denotes the total number of comparison points, \(R_{i}\) and \(R_{i}^{\prime}\) are the specific attenuations at a particular comparison point for the reference field and the retrieved field, respectively. For the following results, the comparison points are assigned to the centers of the top \(320\times 30\)-unit squares in the reference field. The RMSE for the retrieved attenuation field after the first iteration (Figure 3b) is 0.323 dB/km. Averaging the third and four iterations (results shown in Figure 3c) reduces the RMSE to 0.101 dB/km and the PL function (results shown in Figure 3d) also brings the RMSE down to 0.107 dB/km. The RMSE for the retrieved attenuation field with enlarged rectangular basis functions (Figure 3e) is 0.215 dB/km.
# Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP
## 5 Conclusions  
In this paper, we propose an effective PIH scheme based on LLRR and rotation invariant uniform LBP. LLRR is first employed to obtain a principal feature matrix and a salient feature matrix. Following this, rotation invariant uniform LBP is used to extract robust features for perceptual hash generation. The ability of LLRR to extract salient features, along with the effective texture feature extraction ability of LBP, are both helpful to robustness and discriminability. Experiments show that our proposed perceptual hashing scheme is robust to content-preserving attacks such as JEPG compression, low-pass filter, noise addition, slight rotation and scaling, and that it has better robustness and  
Figure 5: The normalized Hamming distances between hash pairs with the correct secret key and 1000 wrong secret keys.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Collision Probability**} \\ \cline{2-5}
**Threshold \(\mathbf{\tau}\)** & **Liu et al.’s Scheme**[13] & **Davazzani et al.’s Scheme**[24] & **Qin et al.’s Scheme**[26] & **Proposed Scheme**[27] \\ \hline
0.26 & 0.3694 & \(2.1125\times 10^{-6}\) & \(1.5412\times 10^{-5}\) & \(4.0388\times 10^{-6}\) \\
0.24 & 0.1351 & \(1.1043\times 10^{-6}\) & \(2.2805\times 10^{-6}\) & \(3.7881\times 10^{-8}\) \\
0.22 & 0.0306 & \(5.6748\times 10^{-7}\) & \(2.2853\times 10^{-7}\) & \(2.9354\times 10^{-9}\) \\
0.20 & 0.0041 & \(2.8665\times 10^{-7}\) & \(3.0222\times 10^{-8}\) & \(1.8778\times 10^{-10}\) \\
0.18 & \(3.2451\times 10^{-4}\) & \(1.4233\times 10^{-7}\) & \(2.7002\times 10^{-9}\) & \(9.9118\times 10^{-12}\) \\
0.16 & \(1.4608\times 10^{-5}\) & \(6.9462\times 10^{-8}\) & \(2.0399\times 10^{-10}\) & \(4.3144\times 10^{-13}\) \\
0.14 & \(3.7352\times 10^{-7}\) & \(3.3320\times 10^{-8}\) & \(1.2995\times 10^{-11}\) & \(1.5481\times 10^{-14}\) \\
0.12 & \(5.3909\times 10^{-9}\) & \(1.5710\times 10^{-8}\) & \(6.9822\times 10^{-13}\) & \(4.5771\times 10^{-16}\) \\
0.10 & \(4.3729\times 10^{-11}\) & \(7.2801\times 10^{-8}\) & \(3.1634\times 10^{-14}\) & \(1.1149\times 10^{-17}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: A comparison of collision probability of different schemes under various thresholds.  
discriminability performance than existing hashing schemes. In addition, the hashing scheme has high key-dependent security.  
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61672528 and No. 61073191, the Social Science Fund of Hunan Province under Grant No. 16 YBA102, the National Social Science Fund of China under Grant No. 17 BTQ084, and the Research Fund of Hunan Provincial Key Laboratory of informationization technology for basic education under Grant No. 2015 TP1017.  
[PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] wrote the paper. All authors have read and approved the final version of the paper.  
The authors declare that there are no conflicts of interest.
###### Abstract  
In this paper, a multi-task learning U-shaped neural network (MTU-Net) is proposed and applied to single-channel speech enhancement (SE). The proposed MTU-based SE method estimates an ideal binary mask (IBM) or an ideal ratio mask (IRM) by extending the decoding network of a conventional U-Net to simultaneously model the speech and noise spectra as the target. The effectiveness of the proposed SE method was evaluated under both matched and mismatched noise conditions between training and testing by measuring the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI). Consequently, the proposed SE method with IRM achieved a substantial improvement with higher average PESQ scores by 0.17, 0.52, and 0.40 than other state-of-the-art deep-learning-based methods, such as the deep recurrent neural network (DRNN), SE generative adversarial network (SEGAN), and conventional U-Net, respectively. In addition, the STOI scores of the proposed SE method are 0.07, 0.05, and 0.05 higher than those of the DRNN, SEGAN, and U-Net, respectively. Next, voice activity detection (VAD) is also proposed by using the IRM estimated by the proposed MTU-Net-based SE method, which is fundamentally an unsupervised method without any model training. Then, the performance of the proposed VAD method was compared with the performance of supervised learning-based methods using a deep neural network (DNN), a boosted DNN, and a long short-term memory (LSTM) network. Consequently, the proposed VAD methods show a slightly better performance than the three neural network-based methods under mismatched noise conditions.  
Keywords:speech enhancement; deep neural network; U-shaped network; ideal ratio mask; multi-task learning; voice activity detection +
Footnote †: journal: Applied Physics Letters
# A Comprehensive Failure Risk Assessment Method of
## 2 Comprehensive Risk Assessment of the Machining Center Component Based on Topological Analysis
### Calculation of Independent Failure Risk of Machining Center Component Based on ANP  
The independent failure risk of a machining center component refers to the risk generated by itself, which is mainly related to the frequency ratio of each failure mode of the component, the failure rate of the component and the influence of the failure mode. Therefore, the equation for calculating the independent failure risk probability of the component is as follows:  
\[R_{l_{i}}(t)=\sum_{j=1}^{n}\alpha_{ij}(t)\lambda_{i}(t)\beta_{ij} \tag{1}\]  
where \(R_{l_{i}}(t)\) represents the independent failure risk of component \(i\) at time \(t\). \(n\) is the number of failure mode types for the component \(i\). \(\alpha_{ij}(t)\) represents the frequency ratio of failure mode caused by failure mode \(j\) of component \(i\) at time \(t\). \(\lambda_{i}(t)\) is the failure rate of component \(i\) at time \(t\). \(\beta_{ij}\) represents the influence degree of the \(j\)-th failure mode of component \(i\).  
Therefore, in order to realize the independent failure risk calculation of machining center component, it is necessary to obtain component failure mode frequency ratio, failure rate and the influence degree of failure mode. The following will focus on the solution of these three parts.
## 1 Introduction  
Because of its widespread application and significant research value, the traveling salesman problem (TSP) has probably become the most classical, famous and extensively studied problem in the field of combinatorial optimization [1; 2; 3]. It can be simply described as to find out the shortest tour that starts from one city from the set of given cities, visits every given city once, and returns to the original finally. As a typical NP-hard combinatorial optimization problem, it is extremely difficult to solve [4]. Compared with the exact algorithms for solving TSP, the approximate algorithms are simpler. Although they cannot guarantee to find the optimal solution, often they can obtain a satisfactory solution. They are more suitable to be used to solve larger-scale TSP [5]. Many approximate algorithms have been applied to solve the TSP [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26].  
Imperialist competitive algorithm (ICA) is a new socio-politically motivated meta-heuristic algorithm proposed by [PERSON] and [PERSON] in 2007, inspired by the colonial phenomenon in human society and history [27]. Although it has been successfully applied to many different optimization tasks and has shown great performance in both the convergence rate and the global optimal achievement [28, 29, 30, 31, 32, 33], its application on solving TSP is still very limited. The literature [34] gives some results obtained by ICA, but the description about how to use ICA to solve TSP is ambiguous. The literature [35] submits a new approach, but in our test experiments, the approach cannot produce the results given in the literature. [PERSON] _et al._[36] proposed a hybrid algorithm based on ICA and tabu search, using ICA to solve TSP at first and using a tabu search to improve the solution, however, the results obtained by the hybrid algorithm are not yet good enough.  
Seeking to explore the potential of ICA and to find a novel and efficient way for solving TSP, we present a novel discrete ICA in this paper.  
The rest of this paper is organized as follows. In Section 2, a brief introduction about the basic ICA is given. In Section 3, the proposed algorithm is set out in detail. In Section 4, the numerical experiments, results and related discussion are given. In Section 5, we conclude the paper and put forward the future works.
## 5 Use Cases of Attack Detection with Matana
### Use Case: Prime + Probe Cache Attack Detection
#### 5.2.2 Determination of a Suitable Attack Benchmark  
The validation phase (i.e., step 4 in our methodology) requires a suitable attack benchmark parallel to more common benchmarks. In addition to a set of schoolbook implementations of Prime + Probe, we would like to assess that our detection algorithm is able to secure the execution of a sensible application by detecting Prime + Probe attack at runtime. In our case, we focused on the Prime + Probe attack of the last round of AES described in [2] and implemented the attack on our target system with the help of an adapted version of the Mastik tool [35]. This attack is applied to the C implementation of AES in OpenSSL \(1.1.1\)k, which is based on the well-known T-Table optimization. This does not reduce the generality of the approach since the Prime + Probe gadget remains but is integrated into a more sophisticated attack.  
To test different attack scenarios, we implemented two versions of this attack: (1) a _SingleProcess_ version where the attack code and the victim code run in the same process; (2) a _SharedMemory_ version where the attack code and the victim code run in separate processes and exchange plaintexts and ciphertexts via a shared memory. In both versions, the attack works as follows. First, the attacker performs a Prime on the cache. Second, the attacker provides a random plaintext to the victim and waits until the victim provides the encrypted plaintext. Third, the attacker checks the cache with a Probe. Notice that with our setup, the full key recovery is possible with 3000 different random plaintexts for the _SingleProcess_ version.  
For the schoolbook Prime + Probe benchmark, we implemented the standard cache line profiling, i.e., loop of a Prime--an access to a specific address and a Probe (which also acts as a Prime operation). There are three versions of this program, one that probes only set 0, one that probes set 1, and one that probes all 64 sets.
# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 3 Material and Methods  
In this study, different datasets were designed to evaluate the potential application of Sentinel-2 data and determine the optimal datasets for lithological mapping. Firstly, after pre-processing (radiation correction and atmospheric corrections), georeferencing and co-registering, three multispectral data (Landsat-8 OLI, Sentinel-2 and ASTER) were employed for lithological mapping in order to compare the capability of Sentinel-2 data for lithological classification with other multispectral data. Secondly, image enhancement techniques (PCA, MNF and ICA) and texture analysis techniques (GLCMs) were applied to evaluate the best method for improving the data quality for lithological mapping. Thirdly, with the goal of improving the classification results, five datasets (the Sentinel-2, PCA, MNF, ICA and GLCM datasets) were combined as an input to the RF classifier. Finally, dataset optimisation was performed by the Relief-RF model to solve the data redundancy problem (Figure 3).  
\begin{table}
\begin{tabular}{c c c} \hline
**Category Number** & **Stratigraphic Chronology** & **Lithology** \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{E\({}_{36}\)} & Yellowish green block bedded rock, lithtic \\  & & feldspathic quartz sandstone intercalated \\  & & with purplish red siltstone and light grey \\  & & medium thick mari \\  & & Reddish- and yellowish-brown block layered \\
6 & E\({}_{1-21}\) & gravels with purple calcareous \\  & & feldspar sandstone \\  & & Purplish red thick gravel with pebbly \\
7 & K\({}_{\text{qn}}\) & sandstone or greyish feldspathic \\  & & quartz sandstone \\
8 & \(Z^{2}_{\text{dk}}\) & Interbedding of biotite plagioclase gneiss \\  & & and marble \\
9 & \(Z^{\text{lb}}_{\text{dk}}\) & Interbedding of plagioclase amphibe and \\  & & migmatised biotite plagioclase gneiss \\
10 & \(Z^{\text{la}}_{\text{dk}}\) & Migmatisation, biotite plagioclase gneiss \\  & & with garnet sillimanite mica schist \\
11 & J\({}_{\text{3h}}\) & Reddish thick fine sandstone and clay rock \\  & & interbedded with blue-grey siltstone belt \\  & & Interbedding of greyish white gravelly \\
12 & J\({}_{1-2}\) & sandstone and black shale intercalated with \\  & & coal seam \\  & & Yellowish grey dolomite and purple \\
13 & C\({}^{\text{b}}_{\text{3 zh}}\) & sandstone interbedded, with boulder at \\  & & the bottom \\  & & Light grey thick-banded crystalline limestone \\
14 & C\({}^{\text{c}}_{\text{3 zh}}\) & intercalated with phyllite and \\  & & calcareous slate \\ \hline \end{tabular}
\end{table}
Table 1: Cont.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine
#### 8.1.8 Network Coverage, Capacity and Mobility  
Telemedicine applications require very good network coverage for smooth operation. As such, network providers must work to improve and sustain the transmission of medical information from local areas where patients require telemedical services to areas where doctors are based. This need is even more stringent if the medical services need to be provided in real time. As highlighted by [136], the issue of network coverage and mobility is crucial if telemedical services are to be guaranteed. Research on the improvement of network availability for more efficient telemedicine service delivery is an important area to be considered. Data transmission rates and capacity are not currently adequate for telemedicine operations in most deployment areas, and services such as telemedicine video conferencing require reliable 5G deployment.