esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Reliability Issues Due to the Process-Variation of 3D-NAND Flash  
3D-NAND flash memory is manufactured by stacking planar (2D) NAND cells. However, because of the nature of the stacking process, the frequency of errors may vary depending on each layer or cell location when the layers are stacked in this manner [1]. This problem can be influenced by factors such as structure distortion during processing, channel hole bending, and a change in the diameter of the channel hole from the upper layer to the lower layer. These process issues are representative factors that can affect flash memory reliability [2; 3].  
Figure 1: 2D (**a**) and 3D-NAND flash memory (**b**) structure [2].  
As a method to cope with 3D-NAND process variation, an error prediction method using machine learning [16] and LI-RAID [5] have been proposed. The error prediction method [16] predicts an error level through machine learning based on the raw bit error measured for each block and allocates a block group to be used according to the predicted level. LI-RAID [5] is the most similar to our proposed differentiated protection policy and it composes pages with different error rates into one stripe to keep the error rates of all stripes uniformly. The difference between LI-RAID and the proposed method is to adjust the protection strength by adjusting the number of parities. We believe that our proposed policy is a more flexible configuration.
## 3 Results and Discussion
### Single-Cell Oil (SCO)
#### 3.4.2 PCA Analysis
When two factors form an angle of 180\({}^{\circ}\) between each other, the cosine is \(-1\), therefore the two factors are negatively correlated. Two uncorrelated factors will form a 90\({}^{\circ}\) angle, where the cosine would be 0. Based on these, by observing the PCA biplot for _C. curvatus_ (Figure 9A), two distinct clusters of fatty acids occur: the ones that are located on the negative axis of PC1, which are all odd-chain fatty acids (OCFAs), and the ones on the positive axis of PC1, which are all even-chain fatty acids (ECFAs). Additionally, supporting the previous findings, all propionic acid scores are grouped on the negative axis of PC1, indicating a strong correlation with the OCFAs formation, while glucose, acetic, and butyric acid indicate a correlation with the ECFAs. The score for BSG VFAs falls in the middle of the PC1 axis, as it results in a combination of both fatty acid groups, while it has a strong association with fatty acids C22:1, C25:0, and C18:2. Based on the loading values for PC1 for _R. toruloides_ biplot (Figure 9B), it better differentiated the following fatty acids (factor loadings higher than 0.7): positive direction; C18:1, C14:0, C22:0, negative direction; C17:0, C17:1, C18:3. PC2 better explains the following fatty acids (factor loadings higher than 0.5): positive direction; C16:0, C18:3, negative direction; C24:0, C22:1, C18:2. By observing the scores for the substrates, it can be concluded that they form distinct groupings on the four quadrants of the biplot. The samples for propionic acid are grouped in the upper left quadrant, close to the OCFAs C17:0 and C17:1, verifying their strong relationship. The scores for butyric acid are grouped on the lower left quadrant, close to C18:2 fatty acid, which indicates a higher formation of that fatty acid in the SCO when butyric acid is used as a substrate. The samples for glucose and acetic acid are again grouped on the positive axis of PC1, proving a strong correlation with ECFAs, like C14:0, C16:1, C18:1, C20:0, and C22:0. The score for BSG VFAs (yellow) is located close to the middle of the axis, resulting in a combination of different fatty acid groups, and it shows a strong correlation with fatty acids C22:1 and C24:0, as it is close to their loading factors.
# Approximate Sensory Data Collection: A Survey
## 3 Compressive Sensing Based Approximate Data Collection Algorithms
### Distributed Algorithms for Generating Compression Bases
Such work was also based on the assumption that the sparsity of the sensory data is known. Meanwhile, the redundant information is also included in the compressed data since the random walk process may involve the same sensor many times. In [31], an implementation method of the compressive sensing was provided on condition that the lossy nature of the WSN is sufficiently explored. The authors of [31] thought that not only the information of sparsity but also the spatial correlation among sensors were known ahead, and both of such prior pieces of information are helpful with compressing sensory data during collection. By combining both the random sampling matrix and the random selection matrix, no computation on sensory nodes was required while achieving the desired data reduction. Such a method saves lots of energy for each sensory node while implementing the compressive sensing techniques. However, the assumption of such seems a little strong. The work in [32] proposed a hierarchy routing strategy to transmit and aggregate the data measurements returned by a compressive sensing technique. Based on [32], the whole network is divided into clusters, and the number of the required measurements in each cluster is decided according to the cluster size and the sparsity of the network. Since it still assumes that the sparsity of the whole network is known, the application of such schema is limited due to the strong assumption.
## 3 Registration Algorithms Based on Probability Stochastic Distribution Model
### Normal Distribution Transform (NDT)-Based  
The normal distribution transform (NDT) approach published by [PERSON] [84] is the most classical of the probability statistics-based registration algorithms. It uniformly voxels the 3D point cloud and determines the best match between the two pieces of point clouds by computing the probability density function of the points in each volume. The specific process is depicted in Figure 6.  
[PERSON] [85] generalized 2D-NDT and extended it to the registration of three-dimensional data, namely 3D-NDT. This improved algorithm would enhance the registration efficiency while ensuring accuracy. The ML-NDT algorithm [PERSON] and [PERSON] [86] developed is a multilayer-based normal distribution transformation method. The Gaussian probability function is replaced with the Mahalanobis distance function as the score function, and the Newton and LM methods are used to optimize the score function. The algorithm divides the point cloud into \(8n\) cells (\(n\) denotes the number of layers), and the registration speed is faster than with the previous NDT algorithm. [PERSON] and [PERSON] [87] suggested a multi-scale K-means normal distributions transform (MSKM-NDT), which separates point clouds into clusters based on \(K\)-means clustering and optimizes at various sizes in order to address the discontinuity problem of the cost function in NDT. Crossing local minima is a benefit of multi-scale optimization, and this approach converges more quickly as a result. [PERSON] et al. [88] proposed a segmented region growing normal distributions transform (SRG-NDT), which first removes the ground plane from the scan by a Gaussian-based segmentation algorithm before applying an effective region-growing algorithm to cluster the remaining points. Due to the removal of the ground points, the probability distribution is substantially smaller than that of the voxel method, the discontinuity of the cost function is overcome, and the speed is significantly increased. [PERSON] et al. [89] proposed an NDT algorithm with variable size voxels, and the accuracy of the algorithm was enhanced. [PERSON] and [PERSON] [90] proposed to convert the reference point cloud into a disk-like distribution suitable for point cloud structures to increase the registration accuracy of NDT. The algorithm's registration speed and accuracy are enhanced on the basis of this by capitalizing on the numerical model of hue and saturation. [PERSON] et al. [91] proposed a semantic-assisted normal distributions transform (SE-NDT). It applies PointNet to offer global features and connects with local features via a segmentation network to provide the category of the point, thus providing classification semantic information.  
The NDT's primary restrictions are comparable with those of the ICP. For one, the NDT is also heavily dependent on accurate initial values. When the estimated transform is updated sequentially, the transform points may pass through PDF cells and end up in the incorrect corresponding PDF cells.
# A 100 Gbps OFDM-Based 28 GHz Millimeter-Wave Radio over Fiber Fronthaul System for 5G
## 6 Results
#### Quality Factor and Bit Error Rate Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fiber Length (km)** & **Quality Factor** & **Bit Error Rate** &
\begin{tabular}{c} **Log** \\ **(Min BER)** \\ \end{tabular} \\ \hline
5 & 9.63 & \(4.97\times 10^{-24}\) & \(-23.30\) \\ \hline
10 & 8.81 & \(17.63\times 10^{-21}\) & \(-19.75\) \\ \hline
20 & 7.57 & \(70.48\times 10^{-18}\) & \(-16.15\) \\ \hline
35 & 6.39 & \(41.39\times 10^{-15}\) & \(-13.38\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Quality factor and bit error rate for 64-QAM employing coherent detection and using DSP compensation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fiber Length (km)** & **Quality Factor** & **Bit Error Rate** &
\begin{tabular}{c} **Log** \\ **(Min BER)** \\ \end{tabular} \\ \hline
5 & 5.57 & \(12.90\times 10^{-9}\) & \(-7.89\) \\ \hline
10 & 4.94 & \(1.79\times 10^{-6}\) & \(-5.75\) \\ \hline
20 & 3.76 & \(7.67\times 10^{-5}\) & \(-4.12\) \\ \hline
35 & 2.88 & \(30.86\times 10^{-5}\) & \(-3.51\) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Quality factor and bit error rate for 16-PSK employing coherent detection and using DSP compensation. Figure 16: (**a**) Shows the relationship between the received signal power and the fiber length for the modulation schemes when DSP compensation is not used (**b**) shows the relationship between the received signal power and the fiber length for the three modulation schemes when DSP compensation is used. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fiber Length (km)** & **Quality Factor** & **Bit Error Rate** &
\begin{tabular}{c} **Log** \\ **(Min BER)** \\ \end{tabular} \\ \hline
5 & 3.83 & \(5.21\times 10^{-5}\) & \(-4.28\) \\ \hline
10 & 3.17 & \(18.16\times 10^{-5}\) & \(-3.74\) \\ \hline
20 & 2.39 & \(0.002310169\) & \(-2.64\) \\ \hline
35 & 1.15 & \(0.031977416\) & \(-1.50\) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Quality factor and bit error rate for 16-PSK employing direct detection and using DSP compensation. Figure 15: (**a**) Shows the relationship between the system quality factors and the transmission distance (**b**) Shows the relationship between the bit error rate and the transmission distance.
# THz Range Low-Noise SIS Receivers for Space and Ground-Based Radio Astronomy +
## 4 Conclusions  
We designed, fabricated, and tested a DSB SIS mixer for the frequency range of 211-275 GHz. The uncorrected receiver noise temperature of approximately 16.5 \(\pm\) 2 K was measured at frequency 255 GHz, which exceeds the quantum value hf/k\({}_{\text{B}}\) only slightly. This mixer is developed as a prototype for the Millimetron Space mission or future astronomy instruments, and it can be used for novel ground-based radio telescopes (Suffa [53], LLAMA [54]).  
To upgrade the high-frequency receivers, we have employed Nb/AlN/NbN mixers incorporated in an NbTiN/Al line. The presented mixer technology in comparison with Nb/AlO\({}_{\text{x}}\)/Nb mixers looks quite promising: (1) a higher \(V_{g}\) value provides a 0.7 mV wider quasiparticle step for high frequencies; (2) high current density allows for a very broadband mixer; (3) heating of the SIS junctions is not critical for the operation of the mixer. SIS mixers designed to upgrade the CHAMP + high frequency array (790-950 GHz) on the APEX telescope exhibit DSB noise temperatures of 210 to 400 K. This improves sensitivity by up to 40% (i.e., almost a factor of 2 in observation time).  
Conceptualization, A.M.B., V.P.K.; methodology, K.I.R., A.V.K., R.H., L.V.F. and M.E.P.; mixer block design R.H., K.I.R., A.V.K. and A.M.B.; SIS circuit fabrication, L.V.F.; Tunnel barrier measurements, M.E.P. electromagnetic simulations were performed by, K.I.R., A.V.K. and A.M.B.; experiments were performed by, K.I.R., A.V.K., M.E.P., R.H., D.A.R.d.C.L. and A.M.B.; data analysis, K.I.R., A.V.K., M.E.P., A.M.B. and V.P.K.; writing and editing, [PERSON], [PERSON], and V.P.K.;  
Figure 14: The best noise temperature over the IF band as a function of LO frequency. The data are corrected for the beam splitter the same as in Figure 13.  
funding acquisition, A.M.B. and V.P.K. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the Russian Foundation for Basic Research (grant No. 19-52-80023). The development and fabrication of the 211-275 GHz SIS mixers was supported by the RSF grant 19-19-00618. The equipment of USU \"Cryointegral\" was used to carry out the research; USU is supported by a grant from the Ministry of Science and Higher Education of the Russian Federation, agreement No. 075-15-2021-667. The work is also supported by Netherlands Academy of science (NWO) grant \"NWO-FAPESP Advanced Instrumentation for Astronomy\" number 629.004.00.  
The authors declare no conflict of interest.
## 4 Results
### Centralized Learning vs. FL Based on Edge-Fog-Cloud Architecture
#### 4.2.7 Latency Performance
Latency reduction is achieved by minimizing the distance data need to travel for processing and aggregation. Local aggregation at fog devices can reduce the latency associated with transmitting data to a centralized cloud server. In cloud-centric architecture, all model updates are sent directly to the centralized cloud server for aggregation. Latency is determined by the network speed between edge devices and the cloud server, as well as the processing time for model aggregation at the cloud server. Latency can be higher in cloud-centric architecture compared to edge-fog-cloud architecture due to the longer distance data need to travel to reach the cloud server and the potential network congestion. Figure 15: Latency in edge–fog architecture. In FL, a process utilized in distributed machine learning systems, we implement an averaging training model approach. This method is particularly relevant in the context of the edge-fog-cloud architecture, where computational resources are distributed across various tiers. Specifically, within this architecture, there exist 5 edge devices linked to each fog device, while 5 fog devices are connected to the cloud server, amounting to a total of 5 fog devices and 25 edge devices. Upon completion of the training process on each edge device, a training model with a data size of 46 Kb is generated. Subsequently, these trained models are transmitted to the fog devices. Here, an averaging process takes place, wherein the training models received from the edge devices are combined to produce a unified training model, still maintaining a data size of 46 Kb. This averaging mechanism significantly reduces the amount of data that needs to be transmitted to the cloud. With the presence of fog devices, only 46 Kb of data needs to be sent to the cloud from five fog devices. However, in the absence of fog devices, all 25 edge devices would have to directly transmit their training models to the cloud server. This illustrates the efficiency gained by utilizing fog devices as intermediate computational processors in the edge-fog-cloud architecture, as it minimizes the burden on the cloud server by reducing the amount of data transmission required directly to it. Figure 16: Latency in fog-cloud architecture. Figure 17: Latency in cloud-centric architecture.
# Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
## 5 Policy Gradient for MA-Stream
### Evaluation Setup  
#### 6.1.1 Evaluated Policies  
We evaluate our model-free MAPG-finite algorithm along with three baselines, which are denoted by \"Even\", \"Adaptive\", and SARSA policies, as follows.  
**Policy MAPG-finite:** Our proposed algorithm leverages a model-free, multi-agent policy gradient to optimize the download bandwidth distribution among agents. Recall that, in the algorithm, multiple decisions/actions are made to either increase or decrease the download speed of specific users by one unit. During the training process, the two decision-making processes (to increase and decrease download speeds) need to perform a random exploration in a non-cognitive fashion, which often leads to a long exploration time and, thus, a slow convergence in the optimal policy. Suppose that the bandwidth distribution at time \(t\) is \(\pi_{t}\). To mitigate this problem during training, we suspend the exploration process if the same bandwidth distribution is observed again in the future, i.e., \(\pi_{t+x}=\pi_{t}\) for some \(x\in\mathbb{N}^{+}\).  
**Policy \"Even\":** The downloading bandwidth is evenly distributed to all users in the system. For instance, when the total bandwidth is 1500 KB/s, each of the five users will receive 300 KB/s for its downloading speed, regardless of its demand and preference. This one-size-fits-all policy equally distributes bandwidth among the users.  
**Policy \"Adaptive\":** The download bandwidth is split between the users in proportion to their desired video bitrates. This policy guarantees that users with a high data rate demand (i.e., those watching the high-resolution video) receive a higher downloading speed, whereas users with a low data rate demand receive a lower speed. Specifically, user \(k\) will be assigned a bandwidth of \(d_{k}=v_{k}B/\sum_{\kappa\in K}v_{\kappa}\), where \(v_{\kappa}\) is the desired video bit rate of user \(\kappa\).  
**Policy SARSA:** This policy leverages single-agent learning, SARSA [53; 54; 55], to distribute the download bandwidth to the users. It uses a standard policy gradient strategy with the same state/action definition of our proposed MAPG-finite. Without considering the nonlinear reward function feature, this policy simply utilizes the sum of the step reward as its immediate reward for learning. Note that we make the same state variables, including reward-related history information \(\bar{z}(l)\), known to the SARSA policy to boost the performance of this baseline.
# Conditional Knowledge Extraction Using Contextual Information Enhancement
## 5 Experiment
### Results Analysis
#### 5.2.2 Model Results Analysis  
Our optimized model was evaluated against the existing WoBERT model on the cpd-cn dataset, as shown in Table 4. The local conditional context enhancement proposed in this paper significantly improves the effectiveness of the information-matching method compared to the WoBERT model. We observed an improvement in Acc, F1, Recall, and Precision values by several percentages, further demonstrating the effectiveness of our approach.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Threshold** & **Accuracy** \\ \hline
82.5\% & 84.5\% \\
85\% & **85.5\%** \\
87.5\% & 82.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correct extraction rates corresponding to thresholds in the conditional phrase corpus. The bolded data indicates the best row of results.  
Additionally, Table 4 also presents the evaluation of our optimized model against the existing and baseline models on the English dataset. The experimental data in Table 4 demonstrates that our method enhances the effectiveness of the information matching method through local conditional context enhancement on the cpd-en dataset. Moreover, we compared our optimized model with the baseline model MIMO-MATCH and achieved significant improvements in Acc, F1, Recall, and Precision values, thus proving the effectiveness of our method.  
In this paper, the reasons for the different F1 values on the English and Chinese data sets are described below:  
1. Because of the difference between Chinese and English word splitting, there is no separator in the middle of each sentence in Chinese compared to English; instead, there is a sequence of consecutive Chinese characters connected to form a sentence, which leads to inconsistencies in the word splitting tools. In the Chinese dataset, this paper adds some specialized words to the dictionary to ensure the correctness of the word splitting. The inconsistency of Chinese word separation may have a negative impact on the model, but the inclusion of some specialized words can improve the accuracy of the separation and have a positive impact on the model.
2. English utilizes a multitude of conjunctions and prepositions to supplement gaps between actual words, creating dependent and independent clauses to reflect various relations in a sentence. On the other hand, Chinese emphasizes meaning more than form, utilizing a looser sentence structure that relies on the relations between the order of words before and after rather than as much semantic glue between actual words.
3. The WoBERT model utilizes the Jieba split to remove redundant parts of BERT's word list while incorporating 20,000 Chinese words; it can improve the model's performance and positively impact the model.
# Implications of GNSS-Inferred Tropopause Altitude Associated with Terrestrial Gamma-ray Flashes
## 3 Analyses and Results
### Thermal Structure of TGF-Producing Thunderstorms
The GNSS-RO temperature profiles associated with TGFs are deseasonalized by creating monthly mean profiles at each point on a \(5^{\circ}\times 5^{\circ}\) grid and removing the long-term monthly mean profile interpolated to each profile location. With this method, we are capable of quantifying the temperature signal of TGF-producing thunderstorms shown in Figure 5, characterized by an anomalously cold middle troposphere at around 7-9 km and an anomalously cold upper troposphere at around 15 km. Some of the temperature anomaly profiles also show warm anomalies between 10-15 km, and cold anomalies between 17 and 22 km, which is consistent with the temperature signal associated with deep convection. Figure 5: Averaged profiles of temperature anomaly within \(\pm 3\) h and within a radius of 300 km of a TGF event with a lapse-rate tropopause (LRT) altitude greater than 17 km (dotted lines), between 16–17 km (dashed lines), and below 16 km (dash-dotted lines) for (**a**) the Northern Hemisphere subtropics, (**b**) tropics, and (**c**) Southern Hemisphere subtropics. Red and blue curves represent continental and oceanic regions, respectively. Anomalies that exceed the 95% significance level appear in color. Most of the TGF-related anomaly magnitudes are greater than 2 \({}^{\circ}\)C, with a strongest cold signal of maximum 9 \({}^{\circ}\)C for high tropopause in the NH extratropics (Figure 5a). The TGF-related temperature profiles tend to have larger anomalies than deep convective cloud shown in previous studies [27; 28]. For example, the authors of [28] examined the temperature of tropopause-penetrating convection and found an anomalously warm upper troposphere (>2 \({}^{\circ}\)C) and an anomalously cold tropopause (<-3.5 \({}^{\circ}\)C). The colder troposphere is likely to indicate a preference for more ice in TGF-producing thunderstorms.