esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### FGI-GSRx Multi-Frequency, Multi-Constellation Receiver  
FGI-GSRx is a MATLAB-based Software-Defined Receiver (SDR) developed at the Finnish Geospatial Research Institute (FGI). It is a MFMC software receiver used to test and validate novel receiver processing algorithms for resilient and accurate GNSS positioning performance. At present, FGI-GSRx can process GNSS signals from multiple constellations, including GPS, Galileo, BeiDou, GLONASS, and IRNSS. The FGI-GSRx software receiver was recently released as open source for the GNSS community under the General Public License (GNU) [18]. The software receiver is intended to process raw Intermediate Frequency (IF) signals in postprocessing. The processing chain of the software receiver consists of signal acquisition, code, and carrier tracking, decoding the navigation message, pseudo-range estimation, and Position, Velocity, and Timing (PVT) computation. The software architecture is built in such a way that a new algorithm can be developed and tested at any stage in the receiver processing chain without requiring significant changes to the original codes.  
The software receiver can also be utilized to develop innovative approaches for interference impact analysis, detection, and mitigation. In [19], authors implemented a Running Digital Sum (RDS)-based interference detection method in FGI-GSRx, where interference detection is conducted via a digital sum analysis of the digitized signal following the Analog-to-Digital (A/D) conversion at the IF. The RDS-based interference detection method was implemented to detect interference in GPS L1 C/A signal. Later, in [20], authors developed and tested a Narrowband Interference (NBI) detection and mitigation algorithm for GPS L1 C/A and Galileo E1 signals in FGI-GSRx. The results demonstrate that the method can identify unintentional NBI and effectively minimize the effect of NBI on GPS L1 C/A and Galileo E1 signals using the adaptive notch filtering technique. As a part of the ongoing development of FGI-GSRx, the authors of this manuscript implemented a jamming event detector by constantly monitoring the received signal strength. The implemented jamming event detection technique is computationally inexpensive, intuitive, and applicable for detection of jamming on any GNSS signal. Section 2.2 offers a detailed overview of the implemented jamming event detector. Aside from the jamming event detector implementation, other necessary modifications to FGI-GSRx were made to execute different combinations of GNSS signals in accordance with the requirements from different experimental scenarios.
## 2 Contribution of This Work
### Closed Loop Congestion Mitigation
#### Notification  
A notification is a means of alerting the central control body to a deviation from the norm and triggering the implementation of the necessary countermeasures according to a predefined program. A combination of the above indicators is used by some notification approaches in IoT networks to detect congestion. According to reference [18] there is a limit on the length of the queue. When the buffer level exceeds the specified maximum value, congestion is detected, and a notification is sent.
## 1 Introduction
Furthermore, there are many types of well log curves, including gamma ray (GR), gamma-gamma density log (GG), self-potential (SP), caliper log (CALI), normal resistivity log (NR), neutron porosity log (PHIN), bulk density log (RHOB), and interval transit time (DT). In practical applications, however, the data are often incomplete, preventing trained models from being effectively applied to incomplete test datasets. In this study, we collected 70,572 well log samples from 22 wells in the Southern Sichuan region. The well log curves include the most commonly used normal resistivity logging, natural gamma logging, and gamma-gamma density logging. Our objective is to establish a generalized lithology classification model that can be transferred to different regions. We aim to explore the extent to which machine learning models can achieve accurate lithology classification with limited information. Additionally, we intend to conduct a systematic and comprehensive comparison of machine learning methods (Random Forest, RF) and deep learning methods (Convolutional Neural Network (CNN) and Residual Neural Network (RNN)) for predicting lithofacies using well log data. This study also seeks to determine whether neural network models, which are more adept at image recognition, outperform machine learning models (e.g., Random Forest) in well log curve identification.
## 4 Performance Evaluation
### Experimental Results
#### 4.3.2 Memory Utilization Results  
The y-axis in Figure 6 shows the memory used, ranging from 0 to the maximum of 8196 MB, and the x-axis shows the time in seconds. In Figure 6, we present the simulation up to the 500 s mark, as there are no changes beyond this point, and for reasons of clarity and space. The memory use trends of the four algorithms are similar inside the simulated edge cloud environment, which is consistent with the findings of Section 4.3.1 regarding CPU utilization.  
Figure 5: CPU Utilization per algorithm.  
As they assign demanding workloads, all algorithms first display notable spikes in memory utilization that emphasize the initial surge in resource demand at the beginning of the simulation. Memory consumption levels out across servers as the simulation goes on, suggesting that memory resources are effectively managed to keep up with workload demands.  
In particular, the power-saving mechanism shows sporadic memory consumption spikes, indicating adaptive task distribution adjustments to maximize resource utilization over time. The location-based algorithm, on the other hand, exhibits repeated memory use spikes because of its dynamic task relocation method, which is dependent on user movement and demand patterns. The location-based method maintains lower peaks in memory consumption despite these oscillations than other algorithms, suggesting a more equitable and effective distribution of memory resources. Despite these fluctuations, the location-based algorithm appears to manage available memory and task scheduling effectively, resulting in lower average memory consumption, as shown in more detail in Section 4.3.6.  
The memory consumption data, taken as a whole, highlight how crucial adaptive resource-management techniques are to edge computing. Algorithms like the location-based method shown here, which efficiently balance memory allocation with workload needs, are critical to improving resource efficiency and satisfying dynamic demands in real-world edge computing installations.  
The worst-fit and best-fit algorithms, after an initial spike in memory usage, stabilize and do not exhibit further spikes in utilization. This stability can be attributed to their simpler nature of workload allocation, which appears to offer an advantage over more complex algorithms. Consequently, simpler algorithms tend to maintain a more stable performance profile.  
In summary, the memory utilization results show how well the resource management methods distribute workloads and make use of edge server computational resources. While the location-based algorithm's dynamic allocation demonstrates its flexibility and reactivity to shifting needs, the worst-fit algorithm's high and stable memory utilization rates demonstrate effective load balancing.  
These results offer a thorough comprehension of memory-management techniques, directing the creation of resource management solutions for edge computing environments that are more effective and flexible.  
Figure 6: Memory utilization per algorithm.
## 3 Results and Discussion
### Effect of HPP on Sensorial Attributes  
The results of the sensory evaluation of the _Serra da Estrela_ cheeses are presented in Table 3. Some significant differences (\(p<0.05\)) between the Ch\({}_{\rm C}\) and HPP-treated cheeses were found in terms of the appearance, odor, texture and taste attributes. Instrumental measurement of color showed that the color of the paste of the Ch\({}_{\rm C}\) cheeses became darker than the paste of the HPP-treated cheeses (less luminosity and less yellowness) [24] during storage; this was particularly observed for the sensory evaluation of color at 15 months of storage, for which significant differences (\(p<0.05\)) were found between all the HPP-treated and Ch\({}_{\rm C}\) cheeses. [PERSON] et al. (2013) [22] also found that HPP-treated Ibores cheeses (raw goat's milk at 50 days of ripening at 400 and 600 MPa/7 min) were yellower than control cheeses. At 15 months of storage, the lower consistency (in paste appearance and texture) attributed to Ch\({}_{\rm C}\) relativity to the HPP-treated P1 cheeses was in agreement with the textural measurement, with the results showing that the HPP-treated P1 cheeses became harder and more consistent. The lactic, acid, animal and short-chain fatty acid odors did not significantly change for the P1 cheeses during the whole storage period (\(p>0.05\)). Also, no significant differences in odor intensity between HPP-treated (400 or 600 MPa/5 min) and control raw ewe's [34,35] or cow's cheeses [36] during 10, 60 and 240 days of storage were reported. Significant odor differences between the Ch\({}_{\rm C}\) and HPP-treated cheeses were found for the P2 (acid and short-chain fatty acids) and P3 (acid) cheees at 90 days storage; these results may derive from some particular heterogeneity of the samples or microbial endogenous enzyme activity. No significant differences between the Ch\({}_{\rm C}\) and P3 cheeses (\(p>0.05\)) were found in terms of texture attributes, showing that consistency and friability were not affected by HPP up to 15 months. Similarly, a HPP treatment (400 MPa/10 min at 50 days of ripening) on La Serena cheeses also revealed no significant effect on texture preference [12]. On the other hand, the P1 cheeses were perceived to be harder than the Ch\({}_{\rm C}\) cheeses at 15 months of storage (\(p<0.05\)). Conversely, a hardness and friability decrease was observed for HPP
## 3 Stationary Localization Algorithms
### Centralized Localization Algorithms
#### 3.1.4 An Probabilistic Localization Method (PLM)  
To mitigate distance measurement error in localization process, multi-iteration measurement and least squares scheme are often adopted in terrestrial applications. However, in underwater applications, the multi-iteration scheme is not practical due to high communication cost. Meanwhile, it has been observed that the probability distribution of distance measurement error often follows a certain pattern, which can be utilized to further improve localization accuracy. In [17], both uniform error distribution and normal error distribution are considered. Then, an Probabilistic Localization Method (PLM) is proposed to improve localization accuracy.  
Figure 2: The Hyperbola-Based Localization Scheme.  
PLM method consists of two steps. In the first step, every two neighboring anchor nodes calculate positions of unknown nodes using a circle-based or hyperbola-based approach. The second step is to determine the final locations of unknown nodes by using the probability distribution of distance measurement error. Localization coverage and accuracy can be improved by utilizing more anchor nodes. However, computation complexity and energy consumption increase greatly. Simulation results indicate that PLM method can significantly improve localization accuracy. Compared with other methods, e.g., minimum mean absolute error (MMAE) and minimum mean squared error (MMSE) based statistical approaches, PLM method requires less information exchange. However, the two ideal error distributions may not make sense in real UWSNs environment.
## 3 Energy-Efficient Architectures
### Temporal Architectures
Tensor cores are a new structure designed to accelerate large matrix operations and perform mixed-precision Matrix Multiply-and-Accumulate (MMAC) calculations in a single operation. The recently announced Nvidia Ampere A100 supports a new numerical format called Tensor Format (TF32) that has the range of 32-bit floating point (FP32) numbers and the precision of 16-bit floating point numbers, using a 19-bit representation. TF32 format on A100 architecture  
Figure 7: Comparison of the number of CPU cores and GPUs. CPUs and GPUs models have been selected for different targets, e.g., personal computers or servers, and different price ranges. For the CPUs, the gray and black lines correspond to the minimum and the maximum cores of a family, respectively. For the GPUs, the black lines represent the number of CUDA cores, and the gray line represents the Tensor cores present in the Nvidia Tesla V100 only. provides a 10x performance increase compared to FP32 format on V100 architecture [58]. Moreover, the Tensor cores in A100 architecture are optimized to exploit sparsity for an additional boost (2\(\times\)) of the performances (see Section 3.2.1 for an insight of sparsity in NNs).
## 2 Data and Methods  
### COSMIC-2 GNSS RO Data  
Level-2 C2 GNSS RO moisture soundings are obtained from the COSMIC Data Analysis and Archive Center (CDAAC) at the University Corporation for Atmospheric Research (UCAR). We use the new \"wetPf2\" retrieval, which provides refractivity, temperature, and moisture from near the surface to approximately 60 km. The \"wetPf2\" product was designed to improve upon the older \"wetPf\" retrieval by using a variational regularization on the Abel inversion during the retrieval process, which enhances the RO measurement with the aid of prior information [30]. The focus of the improvement was on the lower troposphere where the Abel inversion is hampered by a larger measurement uncertainty, especially by a considerable negative measurement bias [30]. The vertical resolution of RO soundings varies from ~200 m in the lower troposphere to ~500 m in the upper troposphere [31] while the horizontal resolution is ~200 km [28]. The retrieved profiles are reported as a function of geometric height above mean sea level and the profiles are quality controlled by excluding the ones with \"bad\" flags (such as if the observation bending angles exceed the climatology by a specific threshold).  
Figure 1 shows the C2 sampling and profile penetration to at least 1 km above the surface (including elevation), using 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) grids within the tropics and subtropics from September-December 2019. C2 sampling is ideal for tropical studies, as over 400 profiles per grid box were observed around the equator during the four-month period. Sampling slowly decreases into the subtropics, with minimum sampling totals between 150-200 profiles observed around 40\({}^{\circ}\) latitude. Additionally, C2 profile penetration below at least 1 km is generally very good, with most regions throughout the tropics and subtropics observing a profile penetration of >70%, with some drier regions seeing a penetration of >90%. Regions that observe slight decreases in penetration are typically over the mountainous regions (e.g., the Andes and Tibet Plateau) with more severe penetration issues (<50%) occurring above the Southern Hemisphere stratocumulus cloud regions.
## 3 Results
### DC Voltage Air Dielectric Strength Measurement Results  
In the dielectric strength experiments, the separation distance between the conductor and the material was set at 5 cm, and five rounds of experiments were performed by varying the material, type of voltage, and electrical line thickness. For cypress and Douglas fir, the heat release rate was high. Moreover, as aforementioned, the experiments could not be performed with the 2 mm electrical line due to the large dip; therefore, the experiments were performed only with the 6 mm line and ACSR conductors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PE** & **PP** & **Cypress** & **Douglas Fir** \\ \hline Average HRR (heat release rate) (kW) & 2.25 & 2.65 & 0.82 & 0.96 \\ \hline Average HRRPUA (heat release rate per unit area) (kW/m\({}^{2}\)) & 198.97 & 234.15 & 82.19 & 95.65 \\ \hline Max HRR (kW) & 4.56 & 4.87 & 2.66 & 3.92 \\ \hline Max HRRPUA (kW/m\({}^{2}\)) & 296.21 & 316.22 & 266.02 & 392.13 \\ \hline E.HOC (effective heat of combustion) (kJ/kg) & 38,730 & 37,909 & 13,107 & 10,235 \\ \hline CO\_Y (CO yield) (g/g) & 0.022 & 0.320 & 0.046 & 0.048 \\ \hline CO2\_Y (CO2 yield) (g/g) & 2.356 & 2.304 & 1.159 & 0.747 \\ \hline Soot\_Y (soot yield) (g/g) & 0.046 & 0.061 & 0.000 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fire property measurement result.  
Figure 6: (**a**) PP HRR, (**b**) PE HRR, (**c**) cypress HRR, (**d**) Douglas fir HRR.  
The dielectric breakdown voltage measurement results obtained from the dielectric strength experiments are shown in Table 5. There was no significant difference between the experimental results for the 2 mm line, 6 mm line, and other electrical lines used in the experiments. It was hence deduced that the distance from the bottom of the electrical line to the surface of the combustible material was more important than the thickness of the electrical line.
# Bindings and RESTlets: A Novel Set of CoAP-Based Application Enablers to Build IoT Applications
## 3 Related Work  
### Sensor-Actuator Interaction  
The traditional approach for applications that require sensor-actuator interaction was to statically associate related devices at the time of deployment. This approach lacks flexibility. One of the earliest works that attempts flexible binding is the ZigBee End Device binding [9]. As stated in the specification, devices with similar End Device Profile and matching cluster ID can be bound. This dynamic binding method places many strict requirements on the devices that will be bound and, as a result, lacks the flexibility of binding any device with any other device that our solution provides. Another notable work that attempts to improve the limitation of ZigBee End Device Binding is given on [10]. This work avoids the requirement of matching cluster ID by matching sensor events with actuator actions. However, this solution is also based on ZigBee and devices that are not compatible with ZigBee cannot be included in the binding process and hence still lacks the flexibility we desire. To achieve maximum flexibility of binding any two devices, working on open standards is preferable. In line with this, the CoRE Interfaces draft [8] specifies how CoAP methods can be used to achieve flexible binding. The mechanism proposed in the draft allows end devices to establish a binding relationship between two resources through discovery mechanisms or through human intervention and then synchronizes the content of the involved resources. This solution has its advantages as it provides a generic solution that can be used in interface descriptions. However, the solution focuses on synchronizing the contents of two resources on different end devices. It is not possible to execute a specific action on the other device. Additional programming logic is still required to send the appropriate trigger to the same or different actuator.
# Numerical Simulation and Analysis of Fish-Like Robots Swarm
## 3 Results and Discussion
### Comparison between Different Shapes  
We only compared the average Froude efficiency of the different fish-like robots swarms. The performance of a single fish-like robot was also simulated as a baseline. As shown in Figure 21, in the stable formation configurations of fishes at a constant spacing and speed, the tandem swarm has the highest efficiency when the spacing is less than \(1.25L\), while the rectangle swarm was more efficient when the spacing was larger than \(1.25L\). As the spacing increased, the efficiency of the diamond swarm was lower than that of a single fish, and the performance of the phalanx swarm was the worst.  
There were two factors that affected the Froude efficiency: thrust and lateral power loss. The thrust was large at close spacing, but the side-by-side fish caused a large lateral power loss, so the tandem swarm has the highest Froude efficient at close spacing. At large spacing, the rectangle swarm and the diamond swarm were almost unaffected by side-by-side fish. However, the rectangle swarm can obtain stronger and larger wakes than the diamond swarm, so the efficiency of the rectangle swarm was the highest. The phalanx swarm had the worst performance because it was not affected by the wake.  
The simulation results suggest that we should choose the tandem swarm when the spacing between fishes is less than \(1.25L\), and should choose the rectangle swarm when the spacing is larger.  
Figure 19: The wake of the rectangle swarm at the spacing of \(1.5L\).  
Figure 20: The contours in pressure field of the rectangle swarm at the spacing of \(1.5L\).
## 3 Machine Learning Techniques for Data Preprocessing
### Data Cluster Analysis
#### 3.2.2 Sparse Subspace Representation  
As a sparse learning method, sparse subspace representation is another manner to construct the similarity matrix in spectral clustering. Generally speaking, a high-dimensional dataset is distributed in the union of several low-dimensional subspaces. Hence, the representation of high-dimensional data is characterized by the sparseness [48; 49; 50].  
It is supposed that the dataset locates in the union of several disjoint linear subspaces. The purpose of cluster analysis is to recognize and separate these subspaces. If \(\widetilde{\mathbf{A}}\) is noise-free and there exist sufficient samples or points in each subspace, then the \(i\)-th sample can be expressed by the linear combination of the remainder, \(i=1,2,3,\ldots,n\). Thus, it holds that  
\[\widetilde{\mathbf{a}}_{i}=v_{1i}\widetilde{\mathbf{a}}_{1}+v_{2i}\widetilde{\mathbf{a}}_{2 }+\ldots+v_{i-1,i}\widetilde{\mathbf{a}}_{i-1}+v_{i+1,i}\widetilde{\mathbf{a}}_{i+1}+ \ldots+v_{ni}\widetilde{\mathbf{a}}_{n} \tag{3}\]  
where \(v_{ji}(j\
eq i)\) is the linear representation coefficient of the \(j\)-th sample. Let \(\mathbf{v}_{i}=\left(v_{1i},v_{2i},\ldots,v_{ni}\right)^{\mathsf{T}}\), where \(v_{ii}=0\). If \(v_{ji}\) is large in the sense of absolute value, the \(i\)-th and the \(j\)-th samples probably have strong similarity.  
Equation (3) can be written as \(\widetilde{\mathbf{a}}_{i}=\widetilde{\mathbf{A}}\mathbf{v}_{i}\). In detailed implementation, the optimal coefficient vector \(\mathbf{v}_{i}\) can be obtained by solving the following \(l_{p}\)-norm optimization problem:  
\[\min_{\widetilde{\mathbf{v}}_{i}}\left\|\mathbf{v}_{i}\right\|_{p},\ \text{s.t.}\ \widetilde{\mathbf{a}}_{i}= \widetilde{\mathbf{A}}\mathbf{v}_{i} \tag{4}\]The value of \(p\) is commonly set to 1 or 2. The samples matrix \(\widetilde{A}\) is frequently corrupted by the superposition of small dense noise and large sparse noise. Under this circumstance, the vector \(\widetilde{\mathbf{a}}_{i}\) is rewritten as:  
\[\widetilde{\mathbf{a}}_{i}=\widetilde{A}\mathbf{v}_{i}+\mathbf{e}_{i}+\mathbf{o}_{i} \tag{5}\]  
where \(\mathbf{e}_{i}\) is a large sparse noise vector and the noise vector \(\mathbf{o}_{i}\) is dense. We assume that \(\mathbf{e}_{i}\) follows a Gaussian distribution, both \(\mathbf{e}_{i}\) and \(\mathbf{v}_{i}\) obey two multivariate Laplacian distributions. By applying the maximum likelihood estimation, we formulate an optimization problem:  
\[\min_{\mathbf{v}_{i},\mathbf{e}_{i},\mathbf{o}_{i}}\|v_{i}\|_{1}+\lambda_{1}\|\mathbf{e}_{i}\| _{1}+\lambda_{2}\|\widetilde{\mathbf{a}}\|_{2}^{2}/2,\text{ s.t.}\;\widetilde{\mathbf{a}}_{i}= \widetilde{A}\mathbf{v}_{i}+\mathbf{e}_{i}+\mathbf{o}_{i} \tag{6}\]  
where \(\lambda_{1}\geq 0\) and \(\lambda_{2}\geq 0\) are two regularization parameters. Hence, the minimization problem (6), also named as sparse subspace representation, is more robust than problem (4) in dealing with dense and large sparse noise.