esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Quality Assessment of COSMIC-2 GPS Observations
### GPS Satellite Visibility  
The visibility of the GPS satellites of six COSMIC-2 satellites on DOY 138 is shown in Figure 2. The statistical information on the percentage of visible satellites of DOY 138-142 is listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\leq\)**3** & **4–6** & **7–10** & \(\geq\)**11** & **Mean** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(Number)** \\ \hline Sat–1 & 0.10 & 18.50 & 81.17 & 0.23 & 7.56 \\ Sat–2 & 0.10 & 20.44 & 79.24 & 0.22 & 7.54 \\ Sat–3 & 0.20 & 15.57 & 83.80 & 0.42 & 7.67 \\ Sat–4 & 0.36 & 18.56 & 81.04 & 0.03 & 7.55 \\ Sat–5 & 0.30 & 12.90 & 86.12 & 0.68 & 7.71 \\ Sat–6 & 0.05 & 16.42 & 83.16 & 0.38 & 7.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage statistics of the visible number of GPS satellites for the six COSMIC-2 satellites, DOY 138–142, 2021.  
Figure 1: Satellite Structure of COSMIC-2.  
As seen in Figure 2 and Table 1, the number of GPS satellites tracked by the space-borne GPS receiver is mostly in the range of 7-10, and there are few epochs with the number of the tacked GPS satellite below 3. The average number of visible satellites for all six COSMIC-2 satellites is larger than 7, which indicates that the space-borne GPS receiver has a good observation performance.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 3 Connectivity Requirements for Drones
### Identification and Control of Aircraft  
Due to the high volume of drones, the use of automatic dependent surveillance broadcast (ADS-B) for recognizing commercial aircraft may overload its frequencies in the future. As a result, a new identification technique is required. The remote identification data can be used in conjunction with 6G, and act as license plates in the same way as license plates in vehicles. Radio waves are used to transmit the remote identification.  
Aircraft registration, identification, tracking, and regulation all necessitate reliable cellular network connectivity. Drones' traffic conditions can be detected and measured by actively monitoring drone locations and route details, and early recognition of geo-fencing and potential attacks can be identified accordingly. The UTM ecosystem provides Low Altitude Authorization And Notification Capability (LAANC) for drones, allowing drone operators to access controlled airspace near airports via real-time verification of airspace authorization below authorized altitudes and management of dynamic geofencing [57].
# A Survey on Memory Subsystems for Deep Neural Network Accelerators
## 7 Near-Memory Processing  
In an attempt to reduce the memory wall in the training processes of neural networks, in [19] the near-data processing enabled eXpander (NDPX) is developed as a nonintrusive method to handle memory-bound operations to increase the allocated GPU memory storage. Since NDPX minimally modifies the GPU architecture, it can be easily implemented with GPUs from various vendors.  
Memory-bound operations can consume a significant portion of memory capacity and considerably hinder the training process by lengthening train time and limiting training throughput. Batch normalization is one such process which optimizes all the activations in every channel for the same batch and normalizes them. In addition to this, the average in addition to the variance is also realized in the batch normalization training process. This stabilizes the distributions of activations in the batch. The pooling layer is another memory-bound operation that downsamples 4 \(\times\) 4 activations in input tensors to a 2 \(\times\) 2 output. Additionally, to update the model parameters, element-wise operations are commonplace in order to integrate the respective layers' output feature maps. These operations are examples of processes the NDPX will accelerate, so as to free memory in the GPU to speed up training. The NDPX has a direct connection through a high-speed memory semantic connection to the GPU. This consists of an NDPX controller as well as DRAM devices. Figure 34 illustrates the NDPX architecture and its interaction with the GPU.  
Each memory controller has a dedicated NDP unit and packet filter per memory channel, and is capable of simultaneously executing multiple requests. When a response packet via the memory controller or a request by the GPU containing physical address corresponds to a filter entry, an NDP kernel function is executed. This is done to replace a GPU kernel in order to offload the memory-bound operations to the NDPX. As a result, the GPU possess additional free memory to handle other operations to continue training. The NDPX architecture has been shown to speed up training for VGG-16 by 51%.
## 3 Materials and Methods
### Methodology for Skin Lesion Detection Using the Decision Fusion Classifier System
This simplicity is particularly valuable in medical applications where quick and easy interpretation of results is crucial;
* **Clinical Decision Support:** In a clinical setting, where decisions need to be made rapidly, binary outcomes can serve as valuable decision-support tools. For example, a clear positive (1) or negative (0) result can guide medical professionals in determining the next steps for patient care;
* **Standardization:** Binary decisions enable standardized reporting and communication of results. A consistent binary output format simplifies data sharing, communication between different medical institutions, and comparisons across different studies or systems;
* **Regulatory and Legal Considerations:** In medical applications, regulatory agencies often require clear, easily interpretable outcomes. Binary decisions can align with regulatory standards and facilitate compliance;
* **Reduced Ambiguity:** Binary outcomes reduce ambiguity in scenarios where nuanced classifications might lead to confusion or differing interpretations. By providing a direct answer, binary decisions can mitigate potential misunderstandings;
* **Threshold Adaptation:** Binary decisions allow for straightforward threshold adaptation. If specific sensitivity or specificity requirements need to be met, adjusting the threshold for positive predictions can be easily performed to achieve the desired balance;
* **Efficient Workflow Integration:** In automated systems, binary outputs can integrate seamlessly into an HER and other medical record systems, enabling quick and efficient data management and retrieval.
###### Abstract  
The over-extraction of groundwater from thin fresh groundwater lenses is a threat to the livelihood of farmers in the Lower Indus Basin (LIB). It is essential to monitor and regulate this pumping to sustain fresh groundwater lenses. In this study, we applied a modelling approach in combination with geostatistical analysis to identify the critical locations to monitor the groundwater levels for sustaining fresh groundwater in the LIB. Our approach included four steps: (i) simulating temporal heads using a calibrated hydrogeological model; (ii) sampling monitoring locations using a hexagonal pattern of sampling; (iii) applying principal component analysis (PCA) of the temporal head observations, and selecting high scoring locations from the PCA; and (iv) minimizing the observation points to represent the water level contours. The calibrated model was able to replicate the hydro-dynamic behavior of the study area, with a root mean square of 0.95 and an absolute residual mean of 0.74 m. The hexagonal pattern of spatial sampling resulted in a 195 point network, but PCA reduced this network to 135 points and contour classification reduced it even further to 59 points. The 195, 135, and 59 point networks represented the water levels with average standard errors of 0.098, 0.318, and 0.610 m, respectively. Long-term simulations with increased pumping showed that the water levels would best be assessed by 195 monitoring points, although 135 and 59 points would represent the depleting area but would not capture the water logging area.  
groundwater monitoring; principal component analysis; hexagonal pattern of sampling; interpolation; flow simulation; freshwater lens; sustainable water use +
Footnote †: journal: Applied Soft. 2020, 10, 5200; doi:10.3390/app10155200
# Development Pattern of Toppling Deformation Slopes in Western China
## 4 Geographic Position Susceptibility Zoning
### Susceptibility Result Analysis  
According to the development features (lithology, slope angle, slope height, elevation, horizontal depth and vertical depth) of toppling deformation slopes, the connection between the features and the susceptibility grade of the area can be studied.  
The lithology of slopes is classified into hard rock (H), soft rock (S) and hard + soft rock (S + H). Based on the statistical data, slope angles of toppling slopes are generally more than 35\({}^{\circ}\), and slope angles are classified into five sections (0-35\({}^{\circ}\), 35-40\({}^{\circ}\), 40-45\({}^{\circ}\), 45-50\({}^{\circ}\), 50-55\({}^{\circ}\); no toppling slope with a slope angle over 55\({}^{\circ}\) has been discovered). Most of the toppling slopes are higher than 100 m; therefore, slope heights are classified into five grades (0-100 m, 100-200 m, 200-300 m, 300-400 m, >400 m). According to the three-gradient terrain of China, elevations are classified into 7 sections (0-500 m, 500-1000 m, 1000-1500 m, 1500-2000 m, 2000-2500 m, 2500-3000 m and 3000-3500 m). Based on the results of research on toppling deformation slopes, horizontal depths are often deeper than 50 m and vertical depths are often deeper than 30 m; thus, horizontal depths are classified into five grades (0-50 m, 50-100 m, 100-150 m, 150-200 m and >200 m) and vertical depths are classified into four grades (0-30 m, 30-60 m, 60-90 m and >90 m).  
Analyzed results are shown in Figure 9. Only areas with very low (1 in Figure 9) or high susceptibility (3 in Figure 9) and toppling deformation slopes in those areas are analyzed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**River**} & \multicolumn{3}{c}{**Information Amount**} \\ \cline{2-4}  & **Upper Reaches** & **Middle Reaches** & **Lower Reaches** \\ \hline Yalong & \(-\infty\) & 1.7918 & \(-\infty\) \\ Lancang & 0.2824 & — & \(-\)0.9067 \\ Yellow & 0.3464 & \(-\)0.5910 & \(-\infty\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Information amount at upper, middle and lower reaches of three typical rivers. (There are only upper reaches and lower reaches in the Lancang River.).  
Figure 9: Distribution of toppling deformations with different (**a**) lithology; (**b**) slope angle; (**c**) slope height; (**d**) elevation; (**e**) horizontal depth and (**f**) vertical depth.  
Regarding lithology, there are four hard + soft rock slopes in grade 2 and one hard rock slope in grade 4. For slope angle, there are four toppling slopes (23\({}^{\circ}\), 44\({}^{\circ}\), 45\({}^{\circ}\) and 50\({}^{\circ}\)) in grade 2 and one (41\({}^{\circ}\)) in grade 4. With regard to slope height, there are four (150 m, 240 m, 340 m and 400 m) in grade 2 and one (546 m) in grade 4. For elevation, there are four (2077 m, 2160 m, 2190 m and 2230 m) in grade 2 and one (1970 m) in grade 4. Regarding horizontal depth, there are two (65 m, 120 m) in grade 2 and one (50 m) in grade 4, and for vertical depth, there is only one in grade 2 as well as in grade 4. Both of them are 35 m in depth.
## 2 Related Works
### C-NOMA for Reliable Network Connectivity  
This section further elaborates on the advantages of integrating cooperative communication into the NOMA system. The authors of [7] addressed the challenge of ensuring secrecy fairness in downlink (DL) C-NOMA systems with untrusted receivers and iSIC. The study suggested joint PA and decoding order optimization to maximize the minimum secrecy rate among UE. The authors of [8] focused on an SUE that assists the WUE by relaying information while harvesting energy using a battery-assisted model. The authors derived closed-form expressions for outage probabilities and evaluated the impact of self-interference and energy harvesting (EH) protocols.  
The authors of [9] explored a game-theoretic framework to address the challenges of PA and subcarrier assignment, aiming to elevate the overall system performance. The authors presented algorithms enabling UE distributed decision-making, leading to improved SE and fairness. The authors of [24] investigated the bit error rate (BER) and ergodic SSE (E-SSE) of the suggested spatial modulation (SM)-C-NOMA system. The results demonstrate that the SM-C-NOMA system achieves superior BER and E-SSE performance.
## Conclusions & Future Work Directions
* Benchmarking of the operational products of ET/SSM at different spatial scales and under a range of environmental and climatic conditions against in-situ and modelled estimates and also detailed inter-comparisons between the available satellite products is a very important step that needs to be taken in order to establish the accuracy and uncertainty of the retrieved biophysical parameters, which is a requirement for its use in operational applications. In this paper, we have provided a comprehensive review on the use of EO estimates of ET and SSM for agricultural applications. The review of literature indicates that more research in the technical literature domain is required on appraisal of ET and SSM over agricultural areas. Also, further studies should focus on estimating and improving ET using the SSM information from the EO dataset, perhaps by using the multi sensor fusion techniques or assimilation of SSM in ET algorithms for e.g., in GLEAM (Global Land Evaporation Amsterdam Model--[[https://www.gleam.eu/](https://www.gleam.eu/)]([https://www.gleam.eu/](https://www.gleam.eu/))), which has provision for assimilation of SSM to correct random forcing errors. GPP's contribution has been supported by the FP7-People project ENViSIon-EO (project reference number 334533) and the author gratefully acknowledges the financial support provided by the European Commission. Both GPP and SP have been supported by a UK Research Council award (STFC ST/N006836/1). Thanks also to [PERSON] for his help with the preparation of Figure 1. All authors contributed to the writing of this review manuscript. The authors declare no conflict of interest.
# Accuracy Analysis of GNSS Hourly Ultra-Rapid Orbit and Clock Products from SHAO AC of iGMAS
## 3 Assessment and Analysis
### Computational Efficiency  
The total time consumption of the procedure of 6H ultra-rapid products was about 90 min, which may not satisfy the requirement of users. Thus, in order to improve the computational efficiency, the procedure of 1H products was improved by the optimized CSPT method for product generation. The quality control of observation and navigation  
Figure 3: Data processing flow chart of the combined serial and parallel threads (CSPT) method for hourly orbit and clock products.  
data was also improved for 1H products to reduce the iteration of the least square parameter estimation. The time consumption of the 6H and 1H product generation procedure for GNSS is shown in Figure 4. We can see the procedure mainly includes three steps: observation combination, parameters estimation, and products generation. The total time consumption of 1H is 41, 39, 36, and 45 minutes for GPS, GLONASS, Galileo, and BDS, which are less than 6H for three steps, respectively. The efficiency of 1H was improved by 40%, 48%, 52%, and 48% for GPS, GLONASS, Galileo, and BDS, with respect to 6H, respectively. Thus, the improved method of 1H shows a promising application for real-time or near-real-time applications.  
Figure 4 shows that the latency is no more than 2 h for 6H, and 1 hour for 1H. Moreover, the available prediction session of the orbit and clock products is 1 st-8 th hour for 6H and 1 st-2 nd hour for 1H. Thus, in this study, the accuracy and precision of the observed and 1 st-8 th and 1 st-2 nd prediction sessions for 6H and 1H products are analyzed and discussed in detail.
## 6 Discussion and Limitations
### Future Research Directions  
Building on the promising results of DICTION, we identify several key directions for future research that could significantly enhance the framework's applicability and robustness.  
**Large language models and transformer architectures**: Extending DICTION to transformer architectures and large language models like GPT and BERT represents a critical next step. This requires adapting the activation-based watermarking approach to attention mechanisms and token embeddings, which presents unique challenges given the fundamentally different computational structure of transformers compared to traditional neural networks. The growing importance of language models in practical applications makes this extension particularly valuable for ensuring model ownership protection in NLP domains.  
**Federated learning applications**: The adaptation of DICTION for federated learning environments presents another promising direction. This involves addressing the unique challenges of decentralized model ownership verification while preserving watermark integrity across multiple clients. In federated settings where data privacy is paramount, the watermarking mechanism must maintain its effectiveness without compromising the privacy guarantees that make federated learning attractive. This includes developing protocols for watermark verification that do not require centralized access to client models.  
**Advanced attack resistance**: Developing enhanced robustness against emerging attacks remains crucial for practical deployment. This includes defending against model extraction attacks, as studied by [PERSON] et al. [71], where adversaries attempt to steal model functionality through query access. Additionally, architectural modification attacks that add dummy neurons, as analyzed by [PERSON] et al. [72], pose unique challenges that require watermarking schemes to be invariant to such structural changes. Furthermore, gradient inversion and other privacy-focused attacks that attempt to extract training data or watermark information through gradient analysis must be considered in future defense mechanisms.  
**Real-world deployment and integration**: Practical deployment considerations warrant a thorough investigation to transition DICTION from research prototype to a production-ready solution. This encompasses integration with existing ML pipelines and frameworks such as TensorFlow, PyTorch, and MLflow, ensuring minimal overhead on training and inference processes. Performance optimization for large-scale deployment becomes critical when dealing with models serving millions of requests. Additionally, conducting comprehensive cost-benefit analyses for industrial applications will help organizations understand the trade-offs between watermarking overhead and intellectual property protection. Finally, working toward standardization and ensuring compatibility with existing watermarking protocols will facilitate broader adoption across the machine learning community.
###### Abstract  
A time division multiplexing differential modulation technique is proposed to address the interference problem caused by the fluctuation of laser light intensity in the single optical path detection system. Simultaneously, a multi-reflection chamber is designed and manufactured to further improve the system's precision with an optical path length of 80 m. A near-infrared C\({}_{2}\)H\({}_{2}\) detection system was developed. The absorption peak of the acetylene (C\({}_{2}\)H\({}_{2}\)) molecule near 1520 nm was selected as the absorption line. A laser driver is developed, and a lock-in amplifier is used to extract the second harmonic (2\(\ell\)) signal. A good linear relationship existed between C\({}_{2}\)H\({}_{2}\) concentration and the 2\(\ell\) signal, and the correlation coefficient was 0.9997. In the detection range of 10-100 ppmv, the minimum detection limit was 0.3 ppmv, and the precision was 2%. At 50 ppmv, C\({}_{2}\)H\({}_{2}\) and continuous detection for 10 h, the data average was 50.03 ppmv, and the fluctuation was less than \(\pm\)1.2%. The Allan variance method was adopted to evaluate the long-term characteristic of the system. At 1 s of integration time, the Allan deviation was 0.3 ppmv. When the integration time reached 362 s, the Allan deviation was 0.0018 ppmv, which indicates the good stability of the detection system.  
near-infrared; C\({}_{2}\)H\({}_{2}\) detection; TDLAS; time division multiplexing differential modulation; a multi-reflection chamber +
Footnote †: journal: Applied Sciences, 2019
## 2 Materials and Methods
### Risk Assessment and Data Processing
#### 2.3.4 The Migration Coefficient (MC) and Bioavailability Coefficient (BC)  
Among the seven forms of heavy metals, the water-soluble form and the ion exchange form are the most easily exchanged by other ions, and easy to be directly used by plants [28]. They have a strong migration ability and are sensitive to environmental changes. These two forms are usually used to calculate the mobility of heavy metals and are important indicators for evaluating soil heavy metal pollution. The carbonate-combined form can be extracted by mild acid, which is easily absorbed and utilized by organisms. Therefore, the bioavailability of heavy metals in soil is calculated by the sum of the water-soluble form, the ion exchange form, and the carbonate combined form [7]. The MC and BC are calculated as follows:  
\[\text{Migration coefficient (MC)}=\frac{(F1+F2)}{(F1+F2+F3+F4+F5+F6+F7)} \tag{4}\]  
\[\text{Bioavailability coefficient (BC)}=\frac{(F1+F2+F3)}{(F1+F2+F3+F4+F5+F6+F7)} \tag{5}\]  
where \(F1\) and \(F2\) represent the water-soluble form and the ion exchange form, \(F3\) and \(F4\) represent the carbonate combined form and the humic acid combined form, \(F5\) and \(F6\) represent the iron-manganese oxide combined form and strong organic combined form, and \(F7\) represents the residual form.
## 4 Results and Analysis
### Characteristics of DP  
#### 4.3.1 Characteristics of Annual DP  
According to Figure 7, the annual DPs of Qiemo and Ruoqiang were 124.21 and 66.18 VU, respectively. The RDP, RDD, and RDP/DP of Qiemo were 73.81 VU, 222.34\({}^{\circ}\), and 0.594, respectively, and those of Ruoqiang were 40.73 VU, 241.79\({}^{\circ}\), and 0.610, respectively. Both counties had medium directional variability. The annual DP of Qiemo presented a trend of dominance by a single wind direction, with a prevailing direction of NE, 62.82 VU. The prevailing direction of the annual DP of Ruoqiang was ENE, 34.85 VU. According to the division of wind energy environments, both counties had a low-wind-energy environment. The intensity of sand drift activities in Qiemo was much higher than that in Ruoqiang. Medium direction variability suggests that the two counties have relatively stable sand-moving wind directions on the annual scale, which are mainly concentrated in NE and ENE.
## 3 Mathematical Model for Optimal Trajectory Planning
### Constraint Function Design
#### 3.3.1 Aerial Constraint (AC)  
While generating a feasible path, monitoring drones should consider terrain areas as monitoring drones cannot go through the terrain area to avoid collisions with mountains. To avoid collisions, the flying altitude of monitoring drones should be higher than the terrain altitude. We represented terrain in aerial constraint which can be described as:  
\[AC=0,\;AC=\sum_{i=1}^{N_{w}}AC_{i} \tag{19}\]  
where,  
\[AC_{i}=\left\{\begin{array}{cc}P,&if\,z_{j}<Area(x_{i},y_{i})\\ 0,&otherwise\end{array}\right.\]