esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 3 Proposed Method
### Clustering Algorithm for Applying the Path-based Clustering Model
#### 3.3.1 Path Distance Algorithm  
This protocol uses the Levenshtein distance to find the difference in routes between vehicles. Assuming that the total path of the vehicle node \(n\) is \(p_{n}\), the \(i\)-th road ID on \(p_{n}\) is \(r_{ni}\) and the length of \(p_{n}\) is \(l_{n}\), the algorithm for calculating the distance of the path is shown in Algorithm 1. Also, Algorithm 1 has a time complexity of \(O\left(n\right)\) because it computes the path distance using the finite \(n\) road information that each neighbor has.  
Figure 4: Urban Scenarios with High Potential for Path Deviations.  
```
1. Set \(i=1,j=1,d=0\)
2. For each \(r_{mi}\) and \(r_{nj}\) of \(p_{m}\), \(p_{n}\)
3. If \(r_{mi}\) is the same as \(r_{nj}\), then
4. Add 1 to \(i\) and \(j\)
5. Else then
6. Add 1 to \(i\) and \(d\)
7. Add \((l_{m}-j)\) to \(d\)
8. Set \(d\) as \(d\) over \(\max(l_{m},l_{n})\)
```  
**Algorithm 1** Path distance algorithm  
The calculated path distance is used as an important factor for determining priority in the clustering process. Clustering can be performed as in Algorithm 2 below using the threshold \(th\), where \(0<th<1\). and having a time complexity of \(O\left(n\right)\). In addition, Algorithm 2 has a time complexity of \(O\left(n\right)\) since it calculates Path distance for finite \(n\) neighbor nodes.  
```
1. Set \(b=0,\beta g=\) false, parameter \(th\), assign \(p\)
2. If there is a cluster head among neighbors, then
3. While not found proper cluster head and \(\beta g\) not set
4. For each \(m\in\{neighbors\}\)
5. Calculate path distance \(d_{m}\)
6. If \(d_{m}<b\), then
7. Set \(b=d_{m}\)
8. Set \(p=m\)
9. If \(b<th\), then
10. Send cluster assignment request to \(p\)
11. Else then
12. Set timer \(t\)
13. Else then
14. Broadcast cluster head announcement
15. Sub procedure \(t\):
2. If there is no \(d_{m}\) lower than \(th\), then
3. Send cluster head announcement
```  
**Algorithm 2** Clustering algorithm with path distance  
In this case, \(th\) is derived as Equation (6).  
\[th=\frac{M}{N} \tag{6}\]  
In Equation (6), \(M\) is the number of neighboring neighbor clusters and \(N\) is the number of neighboring nodes. That is, as the number of neighboring clusters is smaller than the number of neighboring nodes in the periphery, the node selects the cluster to join with care. In other words, it means to create a cluster if possible. In contrast, if the number of neighboring clusters is larger than the number of neighboring nodes, there are many clusters in the vicinity.
## 2 Literature Review
### Trajectory Analysis Using Only Spatial-Temporal Information  
#### 2.2.1 Similarity-Based Methods  
DTW [31], a traditional similarity measurement method for sequential data, can measure the similarity between trajectories on the basis of spatiotemporal information in the context of trajectory classification. The similarity of trajectories can also be utilized for classifying user attributes associated with trajectories. Although a variety of extended DTWs have been proposed [32; 33], these methods have a high computational cost. By contrast, [PERSON] et al. proposed a highly accurate prediction method for the similarity oftrajectory patterns composed of only spatial-temporal information based on a graph-based approach [13]. [PERSON] et al. developed a noise-robust method for calculating the similarity of trajectories and solved data sparsity for the search space for the same problem setting [14]. However, these methods do not assume spatial-temporal sparsity of the trajectory based on mobile device location data. In contrast, location data from mobile devices are obtained almost entirely through irregular events, such as the user opening an application. In addition, with regard to anonymity, location data often do not account for microscopic movement patterns associated with users' residences. The trajectories consisting of such location data are very sparse.
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 5 Discussion
### Failure Case Analysis  
Our method exhibited suboptimal performance in several specific scenarios, particularly in the V102, V201, and Corridor5 sequences. In the V102 sequence, the RMSE of our approach was 0.038 m, significantly higher than that of ORB-SLAM3 (0.015 m) and DROID-SLAM (0.012 m). Similarly, in the V201 sequence, our method achieved an RMSE of 0.120 m, marking the largest performance gap across all tested sequences when compared with the baseline methods.  
The primary factors contributing to these failures are as follows:  
* **Scene complexity:** In the V102 and V201 sequences, rapid camera motion combined with complex lighting conditions led to failures in the Mamba historical observer's learning process, preventing it from effectively updating the observation model, as shown in Figure 8a,b.
* **Feature sparsity:** In the Corridor5 sequence, our method achieved an RMSE of 0.040 m, while DDPG-SLAM achieved 0.010 m and SL-SLAM 0.009 m. The corridor environment, characterized by repetitive textures and relatively flat walls, resulted in suboptimal feature extraction and tracking. Additionally, image distortion further exacerbated this issue, leading to the Mamba historical observer's failure to properly learn from historical experiences. These issues are illustrated in Figure 8c,d, where distortion is particularly evident during the feature extraction process.  
These failure cases highlight key directions for future research. Firstly, there is a need to develop more robust mechanisms to distinguish between scenarios that require dynamic parameter adaptation and those where default parameters suffice, thereby improving the system's adaptability. Secondly, incorporating more advanced historical experience learning methods could enhance the Mamba model's ability to learn effectively in feature-scar and rapidly changing environments, thus improving decision-making accuracy and stability. These improvements would contribute to the overall performance enhancement of SLAM systems in complex scenarios.  
Figure 8: Partial datasets from V102 and Corridor5.
## 3 Results and Discussion  
### Performance of the PAN-NF HF Membrane  
Figure 2 shows the removal levels of NOM (HA and SA) by both the pristine and modified PAN-NF HF membranes. The removal of both organic materials by the modified PAN-NF HF membrane was slightly high: 93% for HA and 95% for SA; in contrast, the pristine membrane removed 65% of HA and 73% of SA. The main mechanisms for removing these contaminants in the PAN-NF HF membranes may be sieving and hydrophobic adsorption. Moreover, the mechanism in the PAN-NF HF membrane may be electrostatic repulsion between the NOM and negatively charged inner PAN membrane. In the observed charge differences (see Supplementary Materials for the zeta potential of the PAN-NF HF membrane, Figure S2), the modified membrane had a strong negative charge (\(-\)33.1 mv) at pH 7, whereas the pristine membrane showed a weak negative charge (\(-\)14.3 mv); this led to reduced electrostatic repulsion between the NOM and modified membrane, resulting in a higher removal efficiency than that observed in the pristine HF membrane [33; 34; 35]. In addition, the contact angle values in Figure S3 confirmed that the modified PAN-NF HF membrane exhibited a slightly higher hydrophilicity than the pristine HF membrane. This indicates that the PAN-NF HF membrane has a strong fouling resistance, which leads to reduced membrane fouling [36; 37].  
Figure 3 illustrates the permeate flux of pure water and fouling development time. The average pure water flux of the PAN-NF HF membrane was 4.75 LMH/bar prior to the test. Compared to the initial water flux of the membrane, the reductions in the flux in the modified PAN-NF HF membrane were 7.8% for HA and 11.8% for SA and those in the pristine membrane were 11.8% for HA and 16.3% for SA. Previous investigations found  
Figure 2: Removal of natural organic matter (NOM) (humic acid, HA, and sodium alginate, SA) by pristine and modified PAN-NF HF membranes. Operating conditions: HA and SA = 5 mg/L; pressure = 5 bar; stirring speed = 300 rpm; cross-flow velocity = 1 L/min; conductivity = 300 us/cm; and temperature = 25 \({}^{\circ}\)C.  
that the absence of membrane fouling can be attributed to electrostatic repulsion or/and hydrophobicity [36; 38]. The modified PAN-NF HF membrane had a stronger negative charge value than the pristine membrane, leading to increased electrostatic repulsion for NOM contaminants [34; 39]. Therefore, the flux decline in the modified PAN-NF HF membrane was less than that in the pristine membrane. The modified PAN-NF HF membrane exhibited lower hydrophobicity than the pristine membrane, leading to less hydrophobic attraction between the NOM and membrane surface or pores [37; 40]. This caused less membrane fouling with minimal flux decline.
## 2 Materials and Methods
### Scorecard Model
#### 2.3.5 Logistic Regression Model  
The logistic regression (LR) model is commonly used in the scorecard model, which is simple, stable and interpretable. The LR model is able to learn the linear relationship between the WOE of features and the logarithm of the unstable probability, and it gives the coefficient of each feature. It is similar to obtaining the scores for each item first and adding them up to obtain the final score.  
A logistic regression model is represented as \(P(y|x)\):  
\[P(y=1|x)=\frac{1}{1+e^{-w\cdot x}} \tag{16}\]  
\[P(y=0|x)=\frac{e^{-w\cdot x}}{1+e^{-w\cdot x}} \tag{17}\]  
where \(x\) is the processed input vector \(x=\Big{(}x^{(1)},x^{(2)},\ldots,x^{(n)},1\Big{)}^{T}\), i.e., the WOE of selected features in this occasion, \(y\) is the stability label with the value of 1 or 0, and \(w\) is the weight vector \(w=\Big{(}w^{(1)},w^{(2)},\ldots,w^{(n)},b\Big{)}^{T}\). The positive and negative probabilities are calculated, and the sample is classified into the class with a larger probability value. Odds are defined as the ratio of positive and negative probabilities and are introduced to the scoring model. The equations below show the odds and their logarithm (logit).  
\[odds=\frac{p}{1-p} \tag{18}\]  
\[logit(p)=log\frac{p}{1-p} \tag{19}\]  
where \(p\) is the positive probability. (20) can be obtained combining (16)-(19):  
\[log\frac{P(y=1|x)}{1-P(y=1|x)}=w\cdot x \tag{20}\]  
where the logit is expressed as a linear function of the input of \(x\).  
The process of logistic regression is to find suitable coefficients of w through supervised learning to make the probability value of the sample consistent with the stability label. The maximum likelihood method is used to estimate parameters. Assuming:  
\[P(y=1|x)=\pi(x),P(y=1|x)=1-\pi(x) \tag{21}\]The log likelihood function is as follows:  
\[L(w) =\sum_{i=1}^{N}[y_{i}log\pi(x_{i})+(1-y_{i})log(1-\pi(x_{i}))]\] \[=\sum_{i=1}^{N}[y_{i}log\frac{\pi(x_{i})}{1-\pi(x_{i})}+log(1-\pi( x_{i}))] \tag{22}\] \[=\sum_{i=1}^{N}[y_{i}(w_{i}\cdot x_{i})-log(1+e^{w\cdot x_{i}})]\]  
\(N\) is the number of samples and \(y_{i}\) is the label value of the \(i\)th sample \(x_{i}\). Estimates of coefficients \(w\) are obtained by iterative training of the gradient descent and the quasi-Newton method. Thus, the output probability \(p\) of the logistic model is obtained, i.e., the instability probability on the occasion of stability evaluation.
## 2 Study Area and Data Used
### SAR Data Used  
In this study, Sentinel-1 A/B ascending orbit data were collected from two tracks covering Svalbard from January to December 2021 for a total of seven maps, which could achieve full coverage of the land area of the archipelago. The spatial distribution of Sentinel-1 data is shown in Figure 1a, and its parameter information is shown in Table 1. The four maps of the 14 th orbit of the Sentinel-1 satellite used (left in Figure 1a) were all collected from the Sentinel-1 A satellite, and the shortest time baseline of the image pair was 12 days. The Sentinel-1 A and B satellite data exist simultaneously in the three maps of the 174 th orbit, so the time baseline can be shortened to 6 days, which can improve the frequency of glacier velocity monitoring. As the temporal baseline increases, ground-level changes become more pronounced, leading to a reduction in coherence. This decrease in coherence introduces additional phase noise, thereby reducing the reliability of the results. When ground features (such as glaciers) undergo significant melting or movement due to various factors (such as warm currents or atmospheric conditions), a shorter temporal baseline can better maintain high coherence between SAR images. This aspect is crucial for subsequent offset tracking.  
The external Digital Elevation Model (DEM) uses the ALOS \"ALOS World 3D-30m\" (AW3D30) global digital surface model (DSM) with relatively high resolution. Produced by a PRISM stereo camera onboard the land observing satellite ALOS [25], the horizontal resolution reaches 1 arc-second, and the nominal elevation accuracy is better than 5 m. In order to achieve complete coverage of Svalbard, 68 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) sheets of AW3D30 data were selected for Mosaic, which served for subsequent geocoding, terrain offset error elimination, and two-dimensional flow velocity calculation on the glacier surface. Since the data north of 80\({}^{\circ}\) N have twice the resolution in the latitude direction compared to those south of 80\({}^{\circ}\) N, they are oversampled by a factor of 2 before mosaicking to eliminate sinusoidal deviations caused by spectral aliasing. The obtained DSM of the study area is shown in Figure 1b.  
Figure 1: (**a**) Svalbard geographical location and Sentinel-1 image coverage used in research. (**b**) Study area DSM.
## 3 Results and Analysis
### Spatial Variation Characteristics of Soil Salinity
#### 3.3.1 Isotropic Analysis
Long-term continuous farming and management have weakened the influence of structural factors such as topography and groundwater level, resulting in an increase in salinity range(_a_) in the surface soil [31; 32; 33]. Meanwhile, salinity in soils of layers between 1 m and 2 m and between 2 m and 3 m are mainly influenced by structural factors such as topography and groundwater level, and the degree of this influence increases with depth, specifically, the range values (_a_) of soil salinity increased significantly from that of the soil layer between 1 m and 2 m [34].
# Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control +
## 2 Background  
The HDDM algorithm has shown sound performance IM [6; 13]. Figure 1 shows the general block diagram of the HDDM for IM. It uses a discrete Fourier transform (DFT)--or the more efficient FFT--to de-interleave a signal into multiple sub-bands. The sub-bands allow for time-frequency manipulation of the signals [12]. The signal manipulation could follow several goals, like restructuring the spectrum [14], equalizing the spectrum [12], or mitigating an interference signal [13]. The classic approach uses a simple PB to remove interference signals exceeding a power threshold [6; 13]. The PB provides temporal isolation and removal, and the DFT interleaving of the HDDM provides spectral isolation of an interference. Therefore, this approach is ideal for removing FMCW, which are sparse in the time-frequency domain. The HDDM improves temporal resolution compared to other Fourier-based techniques, e.g., FDAF [15; 16]. It is the reason for the superior performance against pulsed signals [13].  
A challenge in IM is to develop versatile algorithms that can mitigate a range of different interference types. Therefore, a common limitation of mitigation algorithms is the limitation to a single type of interference. For example, a PB is good at removing wide-band pulsed signals [17] but is ineffective against any continuous-wave (CW) signals. An opposing method is adaptive notch filtering (ANF), which excels at frequency-sparse FMCW [18; 19; 20; 21], but it is ineffective against multi-spectral signals [22] or pulsed signals.  
Multi-spectral methods, which use a DFT, discrete wavelet transform (DWT), or Karhunen-Loeve transform (KLT) to de-interleave the signal into different components, provide more versatility to mitigation [23], but often are not feasible for efficient HW implementation. Finally, a wide-band GNSS receiver requires algorithms with low latency to allow real-time processing at high rates, often requiring some form of parallelization to meet these requirements. Therefore, practical and versatile methods are desired, but few algorithms meet these requirements.  
Previous studies [13] used simple PB to remove large spectral components for IM. A PB is simple and requires low resources, making it ideal for HW implementations. However, with wide-band noise signals, the inherent interference-suppression properties of the code-division multiple access (CDMA) based signals may yield better results than removing large sections of the spectrum. Therefore, PB would cause more harm than benefit to the IM method. One approach uses non-linear functions [24] instead of completely blanking a signal. However, such non-linear functions are complex and impractical to implement in HW, often disregarding fixed-point limitations of high-speed digital systems. Using a simple AGC could provide a similar effect but significantly lower processing requirements.
# Fast Ferroelectric Liquid Crystal Based Optical Switch: Simulation and Experiments
###### Abstract  
The present work explains simulation and experimental investigation of the most significant performance parameters of a ferroelectric liquid crystal (FLC) optical switch. The measurements were carried out with commercially available FLC mixture (\(\theta=22.5^{\circ}\)), having a very fast response time within the range of 1-10 \(\upmu\)s. The best achieved cross talk was \(\sim\)19 dB, which is an exact match with the theoretical result.  
ferroelectric; liquid crystal; dielectric anisotropy; optical switching; crosstalk Article
## 5 Experiments
### Staff Line Detection
#### 5.2.1 Metrics  
To evaluate the staff line detection algorithm we use two different pixel-based metrics. A true positive (TP) pixel pair of GT and prediction is allowed to have a distance of maximal 3 px, any other pixel of a line is treated as false positive (FP) or false negative (FN) (compare Figure 11).  
Similar to an object detection task, we first measure the percentage of staff lines that are detected (recall) and if too many lines were found (precision), which then yields the line detection \(F_{1}^{\text{D}}\)-score. A staff line is marked as TP if the overlap of GT and prediction is greater than 0.5, that is more than 50% of the line must be hit, but the detected line may not be twice as long. This threshold is fairly harsh, since the detection of only a short section of a line causes both an FP and FN error.  
The second metrics computes how many pixels of detected staff line are hit in length which indicates whether the prediction was too long or too short (compare Figure 11). Thereto, we evaluate the precision, recall and \(F_{1}^{\text{LF}}\)-score of all TP-lines in the upper measure. Finally, multiplication of both individual metrics yields the total \(F_{1}\)-score.  
Figure 10: Scheme of the used cross fold training. Each color represents a part that is split into a five fold (smaller boxes). Depending on the experiment different sections are chosen for training and testing. Optionally, a pretrained model on remaining data is used. For the cross-part scheme \(N_{\text{train}}\) can be set to zero to only train on other parts.  
Furthermore, we evaluate the detection rate of a complete staff consisting of four staff lines (\(F_{1}^{\text{S}}\)). A staff is marked as found if at least two staff lines (=50%) are correctly detected. This arbitrary threshold is common for general object recognition tasks. For all detected staves we also count the relative amount of how many of the four lines are correctly hit (\(F_{1}^{\text{HL}}\)).
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 4 Results and Discussion  
### Experimental Data  
The experimental data in this research are the daily weather data of 2071 stations in the Chinese mainland for a total of 3531 days between 1 May 2010, and 31 December 2019. After the gray correlation analysis, eight attributes with correlations greater than 0.88 were selected, including average temperature, average humidity, sunshine duration, precipitation, average wind speed, maximum temperature, average pressure, and date. After data processing by the time sliding window, 3521 experimental data are retained for each site. The data sets are divided into the training set, validation set, and test set  
Figure 6: The workflow of the GCN-BiLSTM model.  
according to the ratio of 8:1:1. To analyze the performance of each model, the data sets will be re-divided according to different experiments, and the model will be retrained to perform prediction experiments.
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 3 CF of the Aggregated Interference in LOS  
In this section, the CF of the aggregate interference is derived in the presence of LOS links first for the general case of \(N_{c}\times N_{v}\) UcylA, and is then simplified for the UCA and single antenna. The focus of this paper is to analyze the behavior of the interference and coverage with respect to the height and arrangements of the antenna array. The mmW channel modeling is first LOS-only, and is then enriched with mixed LOS and NLOS links in Section 5.
## 5 Gaucher disease  
Gaucher disease is the most widespread lysosomal storage disease found worldwide, resulting from the absence of lysosomal enzyme glucocerebrosidase (GBA, acid beta-glucosidase). The enzyme functions on the substrate glucocerebroside (constituent of cell membranes). Under normal conditions within the lysosome, the protein saposin C facilitates the presentation of glucocerebroside to GBA, thereby activating the enzyme. This particular enzyme plays a crucial role in breaking down glucosylcramide through hydrolysis into glucose and ceramide. When this enzyme is deficient, it results in an accumulation of glucosylcramide and other glycolipids within the lysosomes of macrophages. This accumulation primarily occurs in various organs such as the liver, spleen, brain, bone marrow, osteoclasts, and occasionally in the skin, lungs, conjunctivae, kidneys, and heart [189].  
In the context of Gaucher disease, GSLs play a crucial role in shedding light on the underlying molecular changes associated with this disorder.  
Fujiwaki et al. [137; 161], using A-CHCA and DHB matrices on a DE-MALDI-TOF MS (Voyager Elite XL, PerSeptive Biosystem, Framingham, MA, USA) in positive ion mode, with the analyte prepared using Folch's partitioning, investigated the liver of a patient suffering from Gaucher disease, and showed that monohexosylcermide levels were elevated compared with the control samples, revealed by the peaks at \(m/z\) 722--monohexosylcermamide (d18:1/C16), as [\(\mathrm{M+Na}\)]\({}^{+}\), \(m/z\) 738--monohexosylcermamide (d18/C17), as [\(\mathrm{M+Na}\)]\({}^{+}\), and \(m/z\) 750--monohexosylcermamide (d18:1/C18), as [\(\mathrm{M+Na}\)]\({}^{+}\). The accumulation of monohexosylcermide was also revealed by the monohexosylcermide/sphyngomilein ratio, which was elevated. Taketomi et al. [135] identified alterations in the long-chain base composition within the tissue sample afflicted with Gaucher disease to be similar in composition with the tissue sample from the Niemann-Pick type C disease. This indicates that while these diseases may manifest differently, they share commonalities in the aberrations of sphingolipid metabolism.