esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Validity of Impedance Spectra
The first half-cycle is larger and is lengthened toward greater values of \(Re\{\underline{Z}\}\). For high charging currents (\(+6\)I\({}_{20}\) and \(+8\)I\({}_{20}\)), the first half-cycle is not even visible, as the spectrum consists of a straight line. While most points of the spectra with a superimposed discharge DC-current correspond to an LTI system, only the spectrum with \(+8\)I\({}_{20}\) of the spectra with charging DC-currents has a series of valid measurement points at high frequencies. For the other charging DC-currents with charging pre-history (Figure 8), not even the high-frequency part of the spectra fulfills the TI criterion. This could also be observed in single spectra with discharging DC-currents measured after recharging and to the end of the aging test. All spectra recorded during _soft AK3.4_ at 80% SoC have a sufficiently large time-invariant part at the beginning of the aging test, independently of the DC-current. With progressing aging, test spectra recorded with charging DC-currents or after recharging have fewer valid data points. Spectra with small discharge currents, recorded after the cycling phase, have enough valid points in the first half cycle of the negative electrode throughout the aging tests (provided as supplementary material). To summarize the observations, spectra with superimposed small discharge currents can be used for analysis, independently of the aging state. Spectra with superimposed charging currents can be used if they were recorded after cycling, with small DC-currents between \(+1\)I\({}_{20}\) and \(+1.5\)I\({}_{20}\) and at the beginning of the aging tests. Therefore, only the spectra with small discharge currents of \(-1\)I\({}_{20}\) to \(-3\)I\({}_{20}\) are further analyzed. Figure 6: Impedance spectra measured on _TC_1 of _hard AK3.4_ at the end of the first cycling phase (after 15 FCE) with charging DC-currents. Figure 8: _Cont._  
Figure 7: Impedance spectra with discharging DC-currents measured on _TC1_ of _hard AK3.4_ after 120 FCE at the end of cycling phase and after recharging. The impedance values, which pass the LTI criterion, are marked with squares.
# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 4 Discussion
### Model Lightweight Experiment  
As a common method used in model lightweighting, the advantage of depthwise separable convolution [28] is that the mapping of cross-channel correlation and spatial correlation in the feature map of the convolutional neural network can be completely decoupled. Replacing ordinary convolution with depthwise separable convolution can greatly reduce the number of parameters with comparable accuracy and can be easily defined and modified.  
In the model lightweight strategy, this paper also conducted related experiments on the use of depthwise separable convolutions. During the experiment, it was found that, due to the particularity of the expression recognition task, using a too-deep network easily causes overfitting, and the stepwise convolution strategy of depthwise separable convolution will increase the network depth to a certain extent. If overused in this task, network accuracy will be lost. Therefore, the layer4 of ResNet18 was replaced by a deep separable convolution layer in the experiment. This could be achieved with considerable accuracy, greatly reducing the number of parameters.  
Table 2 shows the comparison results of the parameters of each method obtained on the FER2013 dataset. It can be seen from Table 2 that, due to the use of depthwise separable convolution in the experiment 2 Xception architecture, when the multi-channel feature extraction network is also constructed, the amount of network parameters is 10.48 M, which is 0.69 M less than that of the Base network. Experiment 3 is the beginning of the  
Figure 10: Confusion matrix for expression recognition in the FER2013 dataset.  
construction of the multi-channel feature extraction network in this paper and does not use a lightweight module. Therefore, compared with the Base network, the accuracy is increased by 0.48%, but the number of parameters is increased by 3.33 M. In experiment 4, the network parameters decreased by 4.45 M and the accuracy decreased by only 0.02% after the rational use of the depthwise separable convolution. The result of experiment 5 is the multi-channel feature extraction network used in this paper. Compared with the initial structure, the number of parameters is reduced by 4.58 M, and the accuracy reaches 73.50%. Experiment 6 shows the complete multi-channel expression recognition network results based on channel weighting in this paper. The parameter quantity and accuracy reaches 73.81%.
## 2 Materials and Methods
### SHS Quantification  
After SHSs were detected, several metrics were quantified automatically. There were two types of metrics: (1) cumulative metrics generated over a longitudinal segment of the esophagus, and (2) intensity, shape and location metrics generated on a per-SHS basis. The three cumulative metrics are:  
1. The 2D number: quantified as mean/cross-section
2. Volume: total gland volume within the region of interest
3. The 3D number (Cluster): if the distance between adjacent 2D SHSs is within 2.5 mm circumferentially (this threshold is relatively large to account for motion artifacts) and within 3 images (0.15 mm) longitudinally, these SHSs were considered as one 3D cluster.  
The nine metrics generated on a per-SHS basis were either in Cartesian (C) (Figure 1A) or in Polar (P) (Figure 1B) coordinates, depending on the convenience of computation:
4. Size: area of each SHS in 2D cross-sectional images (C)
5. Perimeter: perimeter of each SHS in cross-sectional images (C)  
_Size_ and _perimeter_ are related but are not necessarily correlated. An SHS with a concave, irregular shape may have a large perimeter but a small size.
6. Depth: distance from the centroid of each SHS to the tissue surface (P)
7. Intensity: intensity of SHS normalized by the mean intensity of the axial lines traversing the SHS to account for intensity fluctuations between different angles, images and pullbacks
8. Standard deviation (STD): characterizing the intensity heterogeneity (P)
9. Orientation: absolute angle between the major axis of the SHS and tissue surface (P) after flattening the image to the tissue surface (Figure 2B)  
Figure 2: Illustration of Dice coefficient (**A**) for validation of SHS boundary segmentation and quantitative shape descriptors (**B–E**) used in this study. (**B**) Orientation is the angle between the flattened tissue surface and the major axis of the ellipse fit to the SHS. (**C**) Eccentricity is the ratio of the distance between the foci (f) of the ellipse and its major axis length. (**D**) Extent is the ratio of area between the SHS and its bounding box. (**E**) Solidity is the ratio of area between the SHS and its convex hull.  
Eccentricity: ratio between the foci of a fitted ellipse to the major axis length with a range of 0-1 where 0 indicates a circle and 1 indicates a line (C) (Figure 2C)
* Extent: ratio of SHS area to its bounding box (C), characterizing irregularity (Figure 2D)
* Solidity: ratio of SHS area to its convex area (C), characterizing irregularity (Figure 2E)  
_Extent_ characterizes how well a region fills in its \"bounding box\", whereas _solidity_ characterizes how convex a region is.
## 5 Energy-Efficient Resource Matching Algorithm for D2D Relay Communications
### Two-Dimensional Matching  
We start from the two-dimensional matching problem in the second hop D2D link, which involves a two-sided matching with \(M\) D2D TRs on one side and \(C\) RBs on the other side. We have the following definition:  
**Definition 1**.: _A matching \(\hat{\mathcal{X}}\) is a one-to-one correspondence from the set \(\mathcal{M}\cup\mathcal{C}\) onto itself, which is \(\hat{\mathcal{X}}:\mathcal{M}\cup\mathcal{C}\rightarrow\mathcal{M}\cup\mathcal{C}\), such that \(\hat{\mathcal{X}}(m)=c_{s}\) means that D2D TR \(m\) is matched with the RB \(c_{s}\) and \(\hat{\mathcal{X}}(m)=m\) means that \(m\) remains single. \(\hat{\mathcal{X}}(m)\) is regarded as the matching partner of \(m\)._  
We consider a matching \(\hat{\mathcal{X}}\), in which individuals \(m\) and \(c_{s}\) prefer each other to their matching partners, but are not matched with each other under \(\hat{\mathcal{X}}\), namely \(c_{s}\succ_{m}\hat{\mathcal{X}}(m)\) and \(m\succ_{c_{s}}\hat{\mathcal{X}}(c_{s})\). Thus, \(m\) and \(c_{s}\) form a blocking pair for the matching \(\hat{\mathcal{X}}\), that is, \((m,c_{s})\) blocks the matching. We say that matching \(\hat{\mathcal{X}}\) is not stable because both \(m\) and \(c_{s}\) have the desire to disrupt the matching \(\hat{\mathcal{X}}\) in order to be matched with each other.  
**Definition 2**.: _The matching \(\hat{\mathcal{X}}\) is stable when there exists no blocking pair._
# Effective Blind Frequency Offset Estimation Scheme for BST-OFDM Based HDTV Broadcast Systems
## 4 Proposed Estimation Scheme  
In this section, we present an effective joint LS estimation of CFO and SFO in the ISDB-T system without decoding TMCC signals. Since the same TMCC signals are transmitted by means of multiple carriers, such a repetition property can be incorporated for blind frequency-offset estimation. To show the effectiveness of the joint LS estimation scheme, the MSE performance is theoretically analyzed in the BST-OFDM context.
# Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks
## 3 Multi-Task Learning Recommendation Model for Directed Graph Convolutional Networks
### Knowledge Graph Embedding Layer  
Knowledge graph [47] is a knowledge base with a directed graph structure, and there are one-to-many, many-to-one, or many-to-many relationships between nodes. Use \(G=(E,R,K)\) to represent the complete knowledge graph, where \(E=\{e_{1},e_{2},...,e_{\mid E\mid I\mid}\}\) represents the entity set, \(R=\{r_{1},r_{2},...,r\mid R\mid R\mid\}\) denotes the relation set, \(K\) denotes the triplet set, \(\mid E\mid\) and \(\mid R\mid\) denote the number of entities and relations. In triples \(K=<\)\(i\),\(r\),\(a\)\(>\), \(i\)\(\in\)\(E\) denotes the head entity, \(a\)\(\in\)\(E\) represents the tail entity, and \(r\in R\) represents the relationship between \(i\) and \(a\).  
In order to effectively learn the many-to-one, one-to-many, or many-to-many relationships in the knowledge graph, this paper uses the TransH algorithm [47] to learn the knowledge graph embedding. TransH introduces two new definitions: hyperplane and relation vector. Given a triple \(<\)\(i\),\(r\),\(a\)\(>\) in the knowledge graph embedding, for each relation \(r\), the hyperplane \(w_{r}\) of relation \(r\) is defined, and relation vector \(d_{r}\) on the hyperplane \(w_{r}\) is defined. The embedding of \(i\) and the embedding of \(a\) are projected onto the hyperplane \(w_{r}\), respectively, denoted as \(i\)\(\perp\) and \(a\)\(\perp\). If \(<\)\(i\), \(r\), \(a\)\(>\) is defined correctly, and vector \(i\)\(\perp\) and \(a\)\(\perp\) can be connected in a low-error manner by the translation vector \(d_{r}\) on the hyperplane \(w_{r}\). A scoring function \(\big{|}\|i\)\(\perp\)\(+\)\(d_{r}-a\)\(\perp\)\(\big{|}_{2}^{2}\) to measure the correctness of triples is established. Suppose \(\mid\)\(\mid\)\(w_{r}\)\(\mid\)\(=\)\(1\), \(i\)\(\perp\) is expressed by \(w_{r}\) and \(i\), and is shown in Formula (15). \(a\)\(\perp\) is expressed by \(w_{r}\) and \(a\), and is shown in Formula (16).  
\[i\)\(\perp\)\(=\)\(i-w_{r}^{\intercal}iw_{r}\) (15)  
\[a\)\(\perp\)\(=\)\(a-w_{r}^{\intercal}aw_{r} \tag{16}\]The loss function formula of knowledge graph embedding is shown in Formula (17).  
\[\mathcal{L}_{K}=\sum\limits_{<i,r,a>}||\left(i-w_{r}^{\intercal}iw_{r}\right)+d_{ r}-\left(a-w_{r}^{\intercal}aw_{r}\right)||_{2}^{2} \tag{17}\]
## 2 Related Work  
In recent years, researches on the node placement of wireless sensor networks are also increasing. Node placement optimization problem can be either a single-objective optimization problem for coverage or a multi-objective optimization problem for multiple performance indicators (coverage, network lifetime, node movement, connectivity, etc.) according to application requirements. [PERSON] et al. [12] proposed an improved Shuffled Frog Leaping Algorithm (SFLA) to deploy the mobile nodes in WSN. It can solve the problems like non-uniform distribution and incomplete coverage of the nodes. However, the coverage is the only optimization objective considered by SFLA. [PERSON] et al. [13] regarded node placement problem as a linear programming problem and proposed a biogeography-based optimization (BBO) algorithm. BBO can find the optimal node placement for the WSN while meeting the coverage and connectivity requirements. In order to obtain an optimum sensor node placement for WSN, [PERSON] et al. [14] proposed a new biologic heuristic optimization algorithm called Territorial Predator Scent Marking Algorithm (TPSMA). TPSMA can simulate the behavior of territorial predators, and optimize the node placement to get an effective deployment that provides maximum coverage and minimum energy consumption without jeopardizing the connectivity. In this paper, the coverage, network lifetime and node movement are all considered.  
Because of its advantages of parallelism, self-organization and self-adaptation, evolutionary algorithms, especially genetic algorithms, are widely used to solve the node placement optimization problem. [PERSON] et al. [15] designed specific operators for WSNs' node placement optimization problem and proposed a constrained Pareto-based multi-objective evolutionary approach (CPMEA). CPMEA can find the Pareto optimal node placements that maximize the coverage and minimize the energy consumption while maintaining the full connectivity. [PERSON] et al. [16] improved a multi-objective genetic algorithm (MOGA) and applied it to the node placement optimization. The proposed algorithm has good performance and flexibility with the optimization objectives of coverage, network lifetime and the number of nodes. In the problems employing great numbers of nodes or large areas to be covered, normal genetic algorithms have poor performance. [PERSON] et al. [17] proposed a hybrid Genetic Programming (GP) and Genetic Algorithm (GA) for solving this problem. In their algorithm, GP optimizes the deployment structure and GA was used for actual node placement as per the GP optimized structure. The algorithm improved the total coverage area, energy utilization efficiency, network survival time and the number of nodes. In this paper, an improved ADENSGA is proposed to solve the node placement optimization problem.
## 4 Discussion
### Laboratory Findings  
In our study, we found some differences in the levels of inflammatory parameters depending on the etiology of erythroderma. The median level of erythrocyte sedimentation rate (ESR) was highest in patients with erythrodermic psoriasis, and ESR levels were significantly higher compared to patients with erythroderma caused by atopic dermatitis. C-reactive protein (CRP), on the other hand, was significantly higher in the subjects with drug-induced erythroderma than in those with primary cutaneous lymphomas. The median CRP level was highest in patients with drug-induced erythroderma. [PERSON] et al. described high CRP levels in patients with psoriatic erythroderma and generalized drug eruption [13]. On the other hand, [PERSON] et al. and [PERSON] et al. observed no correlation between etiology and CRP or ESR in their studies [3; 5].  
The median LDH level was the highest in patients with AD erythroderma; however, no statistical significance was observed. The available literature does not provide clear reports on this topic. [PERSON] and [PERSON] showed widespread elevation of LDH in patients with erythrodermic CTCL [2]. This is an important finding because, as is well known, elevated LDH levels are considered a poor prognostic factor. In contrast, the elevation of LDH has been reported far less frequently in patients with psoriasis. Yet, not every retrospective study on erythroderma includes an analysis of those inflammatory parameters. More research on this topic is needed.  
As mentioned above, we showed that IgE levels were significantly higher in patients with AD compared to other etiologies. We have discussed this issue in more detail earlier in this article. The second-highest median IgE levels occurred in patients with CTCL erythroderma. In this particular group, IgE levels were tested in 40.74% of the subjects. Interestingly, among those tested, as many as 72.73% showed IgE levels high enough to be second only to the aforementioned AD. Nevertheless, there was no statistical significance in this case. There are few reports in the literature on IgE levels in patients with CTCL erythroderma. [PERSON] and [PERSON] reported that total IgE levels were above the normal range in all CTCL patients, but values were not pronounced enough to be a significant finding, while [PERSON] et al. found that significantly increased IgE levels were present in 25% of patients with CTCL-induced erythroderma [2; 31].  
We found no statistically significant difference between the levels of other laboratory parameters in peripheral blood smear according to etiology. Yet, some correlations can be found in the literature. [PERSON] and [PERSON] found that the median leukocyte count and median lymphocyte count were higher in SS [2]. The median eosinophil count was higher in AD patients and idiopathic causes of erythroderma. [PERSON] et al. showed that eosinophil is correlated with malignancy-induced erythroderma [5].
## 4 Theoretical Results
### Quasi-Static Ground Response  
First, the effect of the axle pulses on a regular track support is examined. A train with speed of 160 km/h and three homogeneous soils with shear wave velocities of \(v_{S}\) = 150, 200 and 300 m/s are considered. The train passage is presented as one-third of the octave band spectra of the particle velocity for the distances \(x\) = 3, 5, and 10 m in Figure 6a-c. The moving static loads induce very low-frequency vibrations in the neighbourhood of the railway line. The main amplitudes of this quasi-static solution are below 12 Hz at 3 m, below 6 Hz at 5 m, and below 3 Hz at 10 m. The spectra strongly decrease with frequency and with distance. This means that the regular response to the static loads is limited to the low-frequency near field of the track (see also [36; 37; 38]), except for trans-Rayleigh-trains in very soft soils [18]. The quasi-static ground vibration amplitudes are also reduced in a stiffer soil (Figure 6c). The typical spectra of the different distances can be used to easily identify the quasi-static component in the following calculations. In measurements, there may be some additional low-frequency maxima and minima due to special train configurations. Note that the low-frequency quasi-static solution was calculated from axle impulses of higher frequency content. The result is very similar to a calculation based on the static transfer function of the soil [37]. Obviously, the higher impulse frequencies, which come from different track points, perfectly cancel each other.
# On Finding Optimal (Dynamic) Arborescences
## 2 Optimal Arborescences  
Both the Edmonds' and Tarjan algorithms proceed in two phases: a contraction phase followed by an expansion phase. The contraction phase then maintains a set of candidate edges for the optimal arborescence under construction. This set is empty in the beginning. As this phase proceeds, selected edges may form cycles, which are contracted to form super vertices. The contraction phase ends when no contraction is possible, and all the vertices have been processed. In the expansion phase, super vertices are expended in the reverse order of their contraction, and one edge is discarded per cycle to form the arborescence of the original graph. The main difference between both algorithms is with respect to the contraction phase.
# Adaptive Suppression Method of LiDAR Background Noise Based on Threshold Detection
## 3 Adaptive Suppression Method of Background Noise
### Critical Modules Implementation
#### 3.2.1 Noise Detection
\[V_{TN}=\sqrt{2V_{n}}erf^{-1}(1-\frac{2t_{\text{s1}}}{T_{1}}) \tag{12}\]  
The laser emission pulse period time is T\({}_{\text{L}}\), and the false alarm probability in time T\({}_{2}\) is P\({}_{\text{Z}}\); then, the proportional relationship between noise detection time T\({}_{1}\) and T\({}_{2}\) is the following:  
\[\Delta n=\frac{t_{\text{s1}}}{t_{\text{s2}}}=\frac{P_{\text{f1}}T_{1}}{P_{ \text{Z}}T_{2}}=\frac{P_{\text{f1}}T_{1}}{P_{\text{Z}}(T_{L}-T_{1})} \tag{13}\]  
From Formula (13), it can be known that under the same FAR, when the detection period is large and the noise detection time is short, namely T\({}_{2}>>\) T\({}_{1}\), the pulse duration t\({}_{\text{s1}}\) will be very small. Due to the limitation of the bandwidth of the detection system, t\({}_{\text{s1}}\) may not be detected, so t\({}_{\text{s1}}\) needs to satisfy the following:  
\[t_{\text{s1}}>t_{set}\approx 2t_{r}\approx\frac{0.7}{B} \tag{14}\]  
where t\({}_{\text{set}}\) is the settle time, t\({}_{\text{r}}\) is the pulse rising edge time, and B is the system bandwidth. Substituting this into Formula (12), the relationship between noise detection threshold V\({}_{\text{Tn}}\), time T\({}_{1}\) and system bandwidth B can be obtained. \[V_{Tn}<\sqrt{2V_{n}}erf^{-1}(1-\frac{1.4}{T_{1}B}) \tag{15}\]  
Since the time of noise detection is very short, the pulse width of random noise in the large-bandwidth receiver is quite small, so the dynamic comparator needs to operate at a high clock frequency in order to compare and quantify the noise as much as possible in the limited noise detection time. With dynamic comparators, the quantization resolution of the over-threshold noise duration is equivalent to the quantization number N\({}_{\text{ck}}\) of the comparator during the noise detection time, which depends on the clock cycle T\({}_{\text{ck}}\) of the comparator:  
\[N_{ck}=\frac{T_{1}}{T_{ck}}=T_{1}f_{ck} \tag{16}\]  
where f\({}_{\text{ck}}\) is the clock frequency of the comparator, provided by the signal processing unit. The quantization of noise pulse width can be based on Nyquist's theorem, and the clock frequency is generally more than 5 times the bandwidth of the detection system in practical application.
## 3 Case Study
### Custom Base Map  
Except for standard map and satellite images as a base layer on a locations map, any custom map from the tile server can be used. This is important because the user can have insight into the relation of the station location to some other important spatial properties. For example in Figure 11, the base layer is a map of the vulnerability index calculated in the frame of the HARmoNia project. At first sight, it is obvious that almost all monitoring stations are located inside vulnerable areas.