esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Ion exchange (IX) and chelating resins are vital for the technological progress of humanity, being a part of separation technologies, recognized as crucial for the development of human society [1]. They are widely used in various processes related to the communal services, as well as a part of modern and advanced technologies that our society attributes progress to. Industrial applications of IX resins include various processes, from preparation of foods (e.g., processing of milk, beverages, and ready-to-use water) to advanced separation technologies (e.g., removal of radioactive isotopes, wastewater treatment, and recovery of precious resources) [2; 3; 4; 5]. Recently, IX resins have drawn even more attraction, becoming a part of end-use industries and households. These emerging areas include softening, demineralization, and purification of drinking water, resulting in locally applied technologies. This covers small-scale plants processing groundwaters for local communities, as well as integrated, compact devices installed for softening of tap water, allowing for the protection of appliances from calcification, and simultaneously reducing a need for cleaning agents. For these reasons, the market of IX resins reveal tremendous potential for further growth, exceeding an estimated worth of 2.0 billion USD by 2027 [6].  
Topics related to the IX resins also draw much attention in the scientific communities. The literature provides a number of researches focused on the synthesis, as well as applications [7; 8; 9; 10] of polymers with IX and chelating properties. The research carried out so far provides a vast experimental and literature reviews on conventional and unconventional synthetic routes, modification procedures as well as the nature and properties of functionalities incorporated into IX resins [7; 8; 9; 10; 11].  
For these reasons, our research group has focused on the development of new polymeric materials with IX and chelating properties designed for the selective separation and recovery of precious metals such as Au, Pt, Pd, and Re. Within the scope of the carried-out research, we have also proposed alternative applications of IX resins, pushing them out of hydrometallurgy. It was done by exploiting the polymeric materials for the synthesis of unique polymeric nanocomposites (pNCs) suitable for multifunctional applications, linking hydrometallurgical purposes with environmental protection engineering and biomedicine.  
Hence, the aim of the present work is to summarize recent achievements in the synthesis and applications of IX and chelating resins for separation, catalysis, heat management, and biomedicine developed in our research group. The purpose of the present mini-review is to demonstrate that the polymeric materials with IX and chelating properties are not limited only to the hydrometallurgical and separation processes, but that they can be pushed further beyond. This includes unconventional synthetic routes, as well as applications.
## 6 Sensitivity Analyses for the Retrieval of GOME-2A SiF Using PCs
### PC Reference Area  
Finally, we have investigated the effect of the reference area from which spectra are selected for construction of the PCs. For the four test months in 2013, we have also used cloudy ocean and areas covered with snow or ice as reference areas. For selection of cloudy ocean pixels, we follow the approach by [PERSON] et al. (2011) and select pixels over ocean with a minimum cloud fraction of 0.9 but also with a minimum cloud pressure of 850 hPa to avoid shielding of lower parts of an atmospheric column by high clouds. Pixels are taken from two large predefined latitude-longitude boxes over the North and the South Atlantic Ocean. Pixels over snow- or ice-covered areas are selected from a predefined latitude-longitude box over Greenland and the Canadian Arctic Archipelago.  
The retrieval using PCs determined from spectra over snow/ice reference areas performs worst (Table 5). Global SiF maps show very large patches of negative SiF values, particularly in a large band around the equator. SiF maps using cloudy ocean or desert as PC reference areas, however, are largely consistent with each other: the overall correlation R is as high as 0.86. As mentioned before, the baseline retrieval shows slightly positive SiF values over the oceans, which indeed reduce when cloudy ocean is used as reference area. However, these positive values do not completely disappear, i.e. a positive bias is present for the cloudy ocean reference case as well. There is a tendency for SiF values using the cloudy ocean reference to be larger than those using the desert reference.
## 1 Background  
The use of catheters for neurogenic bladder management is a source of significant morbidity, so different alternatives such as neuroprosthetic devices using electrical stimulation were developed and put into clinical use [1; 2; 3; 4]. For complete suprasacral spinal cord lesions, the Brindley sacral anterior root stimulator is commercially available [5]. For complete sacral cord lesions resulting in a decentralized bladder and sphincter (\"second neuron damage\"), direct bladder wall stimulation (DBWS) is still in development, as post-ganglionic nerves located in the bladder wall (intramural nerve network) are viable. Starting in 1958, several study teams demonstrated the feasibility of DBWS, with promising results [6; 7; 8; 9; 10; 11].  
More recent studies were published by [PERSON] (1975) and by [PERSON] and [PERSON] (1978), using the Mentor(tm) bladder stimulator in patients with \"urinary vesical hypotonia\", with a few good long-term results [12; 13]. The most recent clinical studies were conducted by [PERSON] and [PERSON] (1986) [14]. They concluded that the best location for electrode implantation was ventral to the ureters and trigone area, which was in line with prior results [14].  
With the increasing use of intermittent catheterization for the management of neurogenic bladder, DBWS was considered less interesting for clinicians [3]. However, experimental studies in cats and dogs have continued [5; 15; 16; 17]. In one chronic study, a suture electrode composed of multistranded stainless steel wire electrodes with a needle attached to the tip was sutured into the bladder wall, and effective bladder contractions were induced with voiding; however, electrode migration was a problem. The most recent DBWS study (2012) used Permaloc(r) stainless steel wire electrodes that had two stimulating surfaces on a single lead [18]. An anesthetized swine model was used, and only limited detrusor contraction was produced, with no voiding.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 3 Methodology
### Multitask Encoder for Style Disentanglement
#### 3.2.1 Encoder Architecture  
We employ a convolutional backbone with four layers (each with a kernel size of three and ReLU activations), followed by a flattening layer. The feature dimension after flattening is 512. We then apply an eight-headed attention mechanism (for local patches only) to refine style embeddings. One branch encodes the _entire glyph_ to produce a global style vector \(s_{g}\). Another branch splits the same glyph into 25 overlapping patches (\(5\times 5\) grid, each patch size = 32 \(\times\) 32). This local stream produces patch-level embeddings \(\{s_{l_{1}},s_{l_{2}},\ldots,s_{l_{25}}\}\).
## 3 Results and Discussion
### Impact-Oriented Analysis of Lightweight Aggregates Added with Wastes  
Table 4 shows the data obtained for aggregates incorporating 2.5 wt % AHS, PS, CP and CG. From these results, we can see how the addition of waste has a greater impact on the manufacturing and extraction category than on the raw materials and transport categories.  
For extraction, the mixes incorporating 2.5% AHS (Scenario 1) present an impact reduction of up to 60% when PR is added to the clay in the most benefited stages, namely, Abiotic Depletion, Acidification, Eutrophication, Global Warning Potential and One-Layer Depletion, as can be seen in Figure 4a. This improvement reaches 40% for these same categories in the mixes with SY and PY. The improvement for the rest of the categories decreases between 15 and 20 points with respect to the data discussed above.  
In Scenario 4, illustrated in Figure 4d, the overall improvement is in the same categories mentioned in the previous paragraph for the three clays analyzed and would be 40%. In Scenarios 2 and 3, it is more negligible, being in the order of 4% for the extraction stage of Scenario 2 and 12% in Scenario 3. The second stage that presents a greater reduction in environmental impact is the manufacturing stage (Figure 5), as discussed above and in other industry work [55; 57]. Specifically, for Scenario 1 and PR clay, the improvement is 35%, this being the mixture that presents the best results. However, the rest of the scenarios also show improvements in the order of 5-8% in Scenario 4 for the three types of clay, 0.5-3% for Scenario 3 and 1% for Scenario 2. For the rest of the stages, raw materials and transport, the results are of little relevance, with improvements between 0% and 2.5%.  
If we were to evaluate the data globally and compare them with each other, we would obtain a visual as shown in Figure 6. From it, we can see how the mix of Scenario 1, specifically PR-2.5 AHS (Figure 6c), represents a reduction in environmental impact of about 35% with respect to traditional aggregate. This figure is an average value of the impact reduction of all the categories analyzed. It would be followed by the PY-2.5 AHS (Figure 6b) mix in the same scenario, with a reduction of 13.5%. The PR-2.5 CG, SY-2.5 CG and PY-2.5 CG mixes in Scenario 4 would have reductions of 10.5%, 8.2% and 8.1%, respectively. Except for the SY-2.5 AHS (Figure 6a) combination of Scenario 1 with a 7.6% reduction, the rest of the mixes are hardly appreciable improvements over the traditional aggregates evaluated.
## 4 Results
### Comparative Evaluation
#### 4.2.4 Scalability Analysis (Varying Job Loads)  
To evaluate how well the proposed framework scales with increasing workloads, we tested each model under higher job arrival volumes while keeping the number of AMRs fixed at 4, 5, and 6. Specifically, the number of jobs per episode increased from 80 to 100 and 120. The goal was to observe how each method maintains scheduling performance under growing system pressure.  
Improvement gain  
\[Improvement(\%)=\left(\frac{baseline-Compared\ model}{baseline}\ \right)\times 100 \tag{10}\]  
Figure 11: Priority inversion count (mean \(\pm\) SD) for five schedulers across six workload configurations: (**a**) 4 AMRs, 80 jobs; (**b**) 5 AMRs, 120 jobs; (**c**) 5 AMRs, 100 jobs; (**d**) 6 AMRs, 120 jobs; (**e**) 6 AMRs, 100 jobs; (**f**) 4 AMRs, 100 jobs.  
\[r_{t}=\begin{cases}5,&\text{if job priority}=2\\ 2,&\text{otherwise}\end{cases}-0.1w_{t}-0.2b_{t}-10I_{\text{\emph{ inversion}}}+20I_{\text{\emph{fast high prio}}} \tag{11}\]  
Simulation results show that the hybrid method reduces the overall makespan and high-priority task delay while virtually eliminating priority inversions compared to PCP-only, DRL-only, and Priority Inheritance baselines, providing clear evidence and deployment guidelines for smart-factory engineers. The percentage improvements of DRLCC over each baseline scheduler (PCP, DRL, DRL + Inheritance, PI, EDF, and EDF_SRP) are summarized in Table 7.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 3 GNSS Error Analysis
### Error Sources
#### 3.3.2 Satellite Orbit and Clock Errors  
Satellite orbit and clock errors manifest in the modeled satellite position \(\hat{r}_{\mathds{S}_{i}}\) and the modeled satellite clock bias \(\delta t_{\mathds{S}_{i}}\). The International GNSS Service (IGS) provides orbit and clock models for GNSS satellites. The predicted _ultra rapid_ orbits and satellite clocks have an accuracy of \(\sim\)5 cm and \(\sim\)3 ns, respectively [40]. These may add up to \(\sim\)1 m of combined pseudorange model error for a given satellite. The 17-h retroactively-available _rapid_ orbits and satellite clock models are accurate to \(\sim\)2.5 cm and \(\sim\)75 psRMS errors, respectively [40], adding up to less than 5 cm of RMS error in the modeled pseudorange for a given signal. Since the orbit and clock parameters are fit to measurements made at IGS analysis centers, the errors in the estimated parameters must be asymptotically zero-mean by design of the estimator. For post-processing applications such as mapping, it is reasonable to assume the availability of _rapid_ orbit and satellite clock products, and thus the asymptotic average position errors due to errors in modeled satellite position and clock bias can be reduced to a sub-5-cm level.
## 3 Data Analysis Approach
### Determination of the Effectiveness of Prediction Models  
The effectiveness of the regression models and selected neural networks were evaluated based on root mean square error (RMSE), determination coefficient (R), and mean absolute percentage error (MAPE). These values are determined by  
\[\text{RMSE}=\left(\left(\frac{1}{N}\right)\left(\sum_{i=1}^{N}\left|t_{i}-y_{ i}\right|^{2}\right)\right)^{1/2} \tag{8}\]  
\[\text{R}^{2}=1-\left(\frac{\sum\limits_{i=1}^{N}\left(t_{i}-y_{i}\right)^{2}} {\sum\limits_{1}^{N}\left(y_{i}\right)^{2}}\right) \tag{9}\]  
\[\text{MAPE}=\frac{\sum\limits_{i=1}^{N}\left(\left(t_{i}-y_{i}\right)/t_{i} \right)\times 100)}{N} \tag{10}\]  
where \(t\) is the target value, \(y\) is the predicted value, and \(N\) is the number of samples in analysis.
## 2 Problem Formulation and Model
### Target Dynamical Model  
Due to the ground target move with much lower speeds than UAVs, we use a constant-velocity target model in this paper. This constant-velocity target model regards the target acceleration as a zero-mean Gaussian acceleration noise. Therefore, we define \(\mathbf{x}_{\mathrm{t}}(k)=[x_{\mathrm{t}}(k),\,v_{\mathrm{t}}{}^{x}(k),\,y_{ \mathrm{t}}(k),\,v_{\mathrm{t}}{}^{y}\,\,(k)]^{\mathrm{T}}\), with \(\mathbf{p}_{\mathrm{t}}(k)=[x_{\mathrm{t}}(k),\,y_{\mathrm{t}}(k)]^{\mathrm{T}}\) and \(v_{\mathrm{t}}\) (\(k)=[v_{\mathrm{t}}{}^{x}(k),\,v_{\mathrm{t}}{}^{y}\,\,(k)]^{\mathrm{T}}\) to denote the target position and velocity at time \(k\), respectively. The discrete model of the moving target is thus expressed by:  
\[\mathbf{x}_{\mathrm{t}}(k+1)=\mathbf{f}_{\mathrm{t}}(\mathbf{x}_{\mathrm{t}}(k),\mathbf{w}(k) )=\mathbf{F}(k)\mathbf{x}_{\mathrm{t}}(k)+\mathbf{G}(k)\mathbf{w}(k) \tag{4}\]  
where \(\mathbf{w}(k)\)-\(N(\mathbf{0},\,Q(k))\) represents the dynamic acceleration noise. The covariance matrix of the process noise \(\mathbf{w}(k)\) is \(\mathbf{Q}(k)=\mathrm{diag}(\delta_{x}{}^{2}(k),\,\delta_{y}{}^{2}(k))\). \(\delta_{x}(k)\) and \(\delta_{y}(k)\) are the standard deviations related to target acceleration toward \(x\) and \(y\) axes. The state transition matrix \(\mathbf{F}(k)\) and the process noise input matrix \(\mathbf{G}(k)\) are expressed as:  
\[\mathbf{F}(k)=\left[\begin{array}{cccc}1&T_{s}&0&0\\ 0&1&0&0\\ 0&0&1&T_{s}\\ 0&0&0&1\end{array}\right]\mathbf{G}(k)=\left[\begin{array}{cccc}\frac{T_{s}^{2}} {2}&T_{s}&0&0\\ 0&0&\frac{T_{s}^{2}}{2}&T_{s}\end{array}\right]^{T} \tag{5}\]
# LTDA-MAC v2.0: Topology-Aware Unsynchronized Scheduling in Linear Multi-Hop UWA Networks +
###### Abstract  
This paper investigates the use of underwater acoustic sensor networks (UASNs) for subsea asset monitoring. In particular, we focus on the use cases involving the deployment of networks with line topologies, e.g., for monitoring oil and gas pipelines. The Linear Transmit Delay Allocation MAC (LTDA-MAC) protocol facilitates efficient packet scheduling in linear UASNs without clock synchronization at the sensor nodes. It is based on the real-time optimization of a packet schedule for a given network deployment. In this paper, we present a novel greedy algorithm for real-time optimization of LTDA-MAC schedules. It produces collision-free schedules with significantly shorter frame duration, and is 2-3 orders of magnitude more computationally efficient than our previously proposed solution. Simulations of a subsea pipeline monitoring scenario show that, despite no clock synchronization, LTDA-MAC equipped with the proposed schedule optimization algorithm significantly outperforms Spatial TDMA.  
linear TDA-MAC; Medium Access Control; underwater acoustic network Article
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 2 Enabling Technologies
### 5G-Cellular Technology
Taking this contextual information into consideration can help in envisioning new services like triggering promotions as part of loyalty programs based on the location or adapting the connectivity aspects based on the availability of bandwidth or other QoS parameters. Examples of this adaptation may include establishing an ad-hoc V2V communication relay in case of losing the communication with the infrastructure or if the infrastructure we want to reach is outside the communication range of the vehicle. In-vehicle infotainment services, such as delivery of high-definition videos/maps and real-time interactive games, exemplify the new means of convenience that can be provided by the new cellular-V2X technology [45]. To reach a reliable, established and secured seamless connectivity for V2X applications/services, it is crucial to provide feasible connectivity solutions that can be integrated with the built-in V2X products installed in CVs [28; 45]. In this context, a step forward towards 5G connectivity is the latest Qualcomm Snapdragon 5G platform [46] that provides various functionalities to empower CVs. The integration of C-V2X direct-based communication to enhance road safety and 5G dual SIM dual active (DSDA) technology that gives the chance for drivers and vehicles to select and operate on different network operators independently are examples of the various features provided by Qualcomm Snapdragon 5G platform [47]. Other companies, such as Pirelli, have developed smart tires that utilize the 5G cellular networks to relay hazardous road-surface data to the driver and nearby vehicles. In 2019, a field test was conducted to validate the efficiency of these smart 5G-connected tires in detecting the presence of aquaplaning hazard and alerting nearby vehicles [48]. Table 1 provides a focused comparison between DSRC and 5G-cellular technology, in terms of latency, communication range, benefits, and challenges.
# Multi-Layer QoE Learning System Implemented by Fiberhost
## 3 Multi-Layer QoE Metric Nature
### Telephony Service Layer  
Telecommunications operators need to constantly monitor the voice quality to detect things such as cracklings, distortion, echo, dropped calls, and unidirectional sound. For this purpose, Mean Opinion Score (MOS) is a parameter commonly used by telecommunications operators as it represents the subjective audio quality parameter calculated after compression, decompression or transmission. As a consequence, we used it in our multi-tier QoE learning system to evaluate the VoIP quality. Since we used the G.711 codec [39], the expected MOS value for the codec was around 4.3. However, since Fiberhost uses optical fiber as the transmission medium, the experienced delays are usually very low. Therefore, most of the recorded MOS values were around 4.5, as shown in Figure 7.  
Figure 6: Percentage of stalls.
## 3 Experiment Results and Analysis
### Security Analysis
Both the correlation coefficients and the figures show that the proposed RDHEI method effectively decorrelates the neighboring pixels of the plain images. 5. _The information entropy test_ The information entropy is initially defined by [PERSON] [43]. When an image is encrypted, its entropy should ideally be equal to 8 bpp. If the entropy of the encrypted image is less than 8 bits/pixel, there exists a certain degree of predictability and the encryption algorithm will be threatened in its security. The entropies of the encrypted and marked-encrypted images for Lena and Jetplane are shown in Table 1. While the encrypted and marked-encrypted Lena images have entropies of 7.9994 bpp and 7.9995 bpp, respectively, the encrypted and marked-encrypted Jetplane images have entropies of 7.9993 bpp and 7.9994 bpp, respectively. These values are very close to the theoretical value of approximately 8 bpp, indicating that the proposed encryption scheme is secure against entropy attacks. Figure 17: The histogram analysis of the encrypted and the marked-encrypted images for Lena and Jetplane. (**a**) Histogram of the Lena encrypted image. (**b**) Histogram of the marked and encrypted Lena image. (**c**) Histogram of the encrypted Jetplane image. (**d**) Histogram of the marked and encrypted Jetplane image.
## 3 Results
### Associations between Current Dixxin Exposure (Indicated by Blood Dixins) and Brain Volume
#### 3.3.1 Global Brain Volume Analyses  
Total gray matter volume was significantly higher in men supposed to have been subject to perinatal dioxin exposure than in men without perinatal exposure (\(p\) = 0.005; effect size (ES) = 0.252; Table 4). However, no significant differences in white matter volume and total cerebrospinal fluid were observed between men with and without perinatal dioxin exposure. Total brain volume and total intracranial volume were significantly higher in men with perinatal exposure than in men without exposure (\(p\) = 0.020, ES = 0.178 for total brain volume; \(p\) = 0.034, ES = 0.151 for total intracranial volume; Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Perinatal Dixxin Exposure** & \multicolumn{4}{c}{**Without (\(n\) = 20)**} & \multicolumn{4}{c}{**With (\(n\) = 12)**} \\ \hline \multirow{2}{*}{**Global Volumes**} & \multirow{2}{*}{**Adj Mean**} & \multicolumn{3}{c}{**95\%CI**} & \multicolumn{3}{c}{**95\%CI**} & \multirow{2}{*}{\(p\)**-Value**} & \multirow{2}{*}{**ES**} \\ \cline{3-3} \cline{5-8}  & & **Lower** & & & & **Lower** & & **Upper** & \\ \hline Gray matter (GM) (cm\({}^{3}\)) & 615 & 601 & 629 & 651 & 632 & 670 & 0.005 & 0.252 \\ White matter (WM) (cm\({}^{3}\)) & 527 & 508 & 545 & 551 & 527 & 576 & 0.118 & 0.085 \\ Cerebrospinal fluid (CSF) (cm\({}^{3}\)) & 300 & 283 & 317 & 306 & 284 & 329 & 0.651 & 0.007 \\ Total brain volume (TBV) (cm\({}^{3}\)) & 1142 & 1111 & 1172 & 1202 & 1163 & 1242 & 0.020 & 0.178 \\ Total intracranial volume (TIV) (cm\({}^{3}\)) & 1443 & 1401 & 1484 & 1517 & 1463 & 1570 & 0.034 & 0.151 \\ \hline \hline \end{tabular}  
* \(n\): number of subjects; adj mean: adjusted mean; CI: confidence interval; ES: effect size. Covariates: age (years) and height (cm).  
\end{table}
Table 4: Adjusted comparisons of global brain volumes between men with and without perinatal dioxin exposure estimated according to their mothers’ residency in Bien Hoa during pregnancy.  
Figure 3: Brain regions positively correlated with 1,2,3,4,7,8-HexaCDD (FDR-corrected at \(p\) < 0.05) are indicated by yellow color and arrows on axial MRI. (**A**) shows the correlations in the left cerebellum lobule VII (arrows) and (**B**) shows those in the right middle frontal gyrus (arrows). L and R indicate the left and right sides of the hemispheres, respectively. Each value below each brain slice indicates each value in MNI z-coordinates.  
3.2. VBM Analysis; Brain Regions in Which Gray Matter Volume Differed between Men with and without Estimated Perinatal Dioxin Exposure  
The gray matter volume of the left inferior frontal gyrus (IFG) pars orbitalis was significantly lower in men with perinatal dioxin exposure than in with men without perinatal exposure after adjusting for total intracranial volume and age (\(p<0.05\), FDR-corrected; Table 5, Figure 4). However, no area showed a higher volume in men with perinatal exposure in comparison with men without exposure (Table 5).