esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 4 Conclusions  
The designed solution shows that a secure Diffie-Hellman key-exchange can be efficiently performed between smartphones and in-vehicle head units in the audio domain. This is further complemented by a visual channel which uses a QR code. The bit error rate in the audio transmission is generally below 10% and the key recovery rate greater than 50%. As a more general conclusion that can be drawn from the experiments over all devices, the decay in the key recovery rate and the increase in the BER are highly dependent on the device characteristics. That is, the quality of the speakers and microphones proved to be very non-uniform over the devices which we tested. If environment noise is further extracted for a more resilient pairing, the error rate of the channel doubles. In this respect, leaving the audio channel one-sided, i.e., only from the smartphone to the head unit, and using a visual channel from the head unit to the phone will increase the pairing success rate while maintaining the security offered by the extracted in-vehicle noise.  
While the bandwidth of audio-visual channels is clearly lower, they are still effective for exchanging small elements such as cryptographic keys that can be later used to enforce the security of traditional Wi-Fi channels. This hybrid approach should be beneficial both from a security and performance perspective. So far, our experiments did not address the pairing of multiple devices, but we leave this as potential future work. The cryptographic operations required by the elliptic curve version of the Diffie-Hellman protocol are very easy to handle by modern smartphones as proved by our experimental results. Since the SPEKE protocol is not supported by default in the Android cryptography toolset, we had to implement this on our own, but this required only a moderate effort and good understanding of the elliptic curve library.  
Conceptualization, B.G.; methodology, B.G.; software, A.A. and P.I.; validation, A.A. and P.I.; investigation, A.A.; resources, A.A. and B.G.; writing--original draft preparation, A.A., A.B., P.I. and B.G.; writing--review and editing, A.A., A.B., P.I. and B.G.; supervision, B.G.; project administration, B.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussion
There were large differences in remote sensing LST accuracy for different surface temperature ranges in our results (Figure 3 and Table 3), and the ranges of surface temperature were affected by factors such as day/night, season [44], land cover, soil moisture, and vegetation condition [45]. Analysis of the effects of environmental factors confirmed that the preceding factors had a large effect on LST accuracy. The poor performance ofLST products in Dongting Lake area may be related to the interference of remote sensing signals at the meeting of land surface and water surface. However, the lack of water surface observation stations in the in situ measurements limits our in-depth analysis of this point. Another factor that affects LST is shade: surfaces that are shaded are significantly cooler than sunlit surfaces. The effects of this factor are incorporated in the effects of elevation, landform, and slope on LST. FY-4A LST was less accurate than Himawari-8 LST, which indicates that the FY-4A product is in need of improvement. However, as the prototype of a new generation of Chinese geostationary meteorological satellites, FY-4A is inherently experimental, and therefore systematic errors due to instrument performance are unavoidable. It has been found that some TIR bands of AGRI are not very stable in terms of satellite calibration [46]. It has also been found that although the retrieval algorithm used to produce FY-4A LST is similar to that used for Geostationary Operational Environmental Satellites (GOES)-R LST, observational variation in FY-4A LST is much greater [47]; this may also be related to the relatively poor performance of the sensor. LST is a fundamental surface parameter that is detected by the primary sensors of China's new generation of geostationary meteorological satellites. This comprehensive evaluation of the FY-4A LST product confirms the satellite has a good detection capability, but it does not yet match foreign satellite products in terms of overall accuracy. We can improve the retrieval algorithm by incorporating the influencing factors we identified or perhaps by using data-driven models based on artificial neural network methods to retrieve quantitative parameters from the remote sensing Chinese satellites, which is the goal of our next research project. In conclusion, FY-4A, the first satellite of the FY-4 series, has successfully completed its task of comprehensively prototyping China's second-generation geostationary orbit meteorological satellite program and observation technologies. Its observation performance has exceeded expectations, and the data products are being used in live meteorological applications. FY-4B has also been launched and will soon be in operation, and we look forward to the increasingly important role that Chinese meteorological satellites will play in the future.
# MLUTNet: A Neural Network for Memory Based Reconfigurable Logic Device Architecture
### Memory Based Reconfigurable Logic Device  
Recently, a kind of memory based reconfigurable logic device (MRLD) [7], taking multiple look-up tables (MLUT) as the core component structure, has demonstrated attractive benefits, such as low production cost, low power, and small delay, and programmable function to implement designed circuit logic. It is regarded as an promising alternation to FPGA in some respects. Its structure is shown in Figure 1.  
MRLD is an MLUT array constructed with a special internal connection structure. MLUTs are basic reconfigurable elements that consist of synchronous SRAM and asynchronous SRAM, with \(m\)-bit address inputs and \(m\)-bit data outputs forming a pair of interconnects. For an MLUT, the \(m\)-bit address input comes from the data outputs of its neighboring MLUTs, and the \(m\)-bit outputs are connected to the address inputs of these MLUTs. Each MLUT can operate as a memory block or a logical wiring block.  
Compared to logic and switch block independent FPGAs, the MRLD has the following advantages: each MLUT can be used as a logic block or wiring element by configuring the corresponding truth table in the SRAM; the address input/data output of the MLUT will be the input and output of the configured logic circuit (or wiring element). Since the logic blocks and wiring elements are configured in the SRAM, there is no longer a need for as many interconnect resources as in an FPGA, making it possible to have high density reconfigurable devices with small latency and low power.
## 3 Optimization of the Field-Null Configuration  
Robust plasma breakdown in tokamaks relies significantly on the quality of the field-null configuration, i.e., the absolute value of the poloidal field \(|B_{\perp}|\), the area and shape of the field-null region, and its alignment with the high-loop-voltage region. In particular, robust breakdown with a low toroidal electric field \(E_{\phi}\leq 0.3\) V/m is desirable on large tokamaks like ITER [9], due to the limited change rate of coil currents for super conductors. However, for now, on HL-3, the breakdown is not very reliable even with \(\left|E_{\phi}\right|\) as high as \(1.0\sim 1.2\) V/m. Thus, there is an urgent need for improving the breakdown scenario on HL-3. One of the key issues in breakdown improvement is how to compensate for the influence of eddy currents in the passive conductors to obtain a good field-null configuration. Based on the simulation model in Section 2, an iterative scheme is introduced to minimize the average \(|B_{\perp}|\) in a given region and simultaneously provide the corresponding PF coil current waveforms. The scheme is simple enough to be calculated swiftly, and the eddy currents in passive structures are self-consistently treated to ensure a reliable solution for compensation. The remaining part of this section tells the details of the optimization scheme and typical examples of optimized field-null configurations.
## 3 UniChain Architecture
### Smart Contracts
#### 3.4.1 Nodes Contract (NC)  
NC is a global contract that preserves the registration, the mining, and certain overwrite procedures for the blockchain. The NC determines voter nodes in the system along with the \"block's creator\" node, which will create the next block. It maps a node identification string to its associated Ethereum address identity, i.e., public key. The adoption of the identification strings rather than the public key directly is to allow already existing IDs to be used. Procedures and policies coded into the NC regulate adding and registering new IDs. This contract additionally determines the role of each participant node within the system i.e., university node, student node, third party, etc. in order to recognize nodes that have already registered and thus avoiding the case of double registration. For those nodes with a \"university node\" role, additional data fields will be appended to maintain the degree of the node along with a Boolean data field that determines whether a node is considered as a voter node or not. Additionally, the NC maps the Ethereum address of the node with its associated SRHC address.  
* For determining the voter nodes, the NC stores the degree of each university node within the system that in turn will be used for calculating the average degree of the network and selecting voter nodes.
* For selecting the \"block's creator\", the NC assigns this task to the university node that has the least degree among all university nodes in the system.
* For mining, the NC functions using the PoA consensus algorithm [23] integrated with the proposed incentive mechanism.
* For the registration process, voter nodes in the NC are responsible for validating other nodes that demand a role with \"higher levels\" upon being added to the system. Voter nodes guarantee that no threat will be created to the system. Generally, during the blockchain initialization stage, the NC will be empty. An administrative node (i.e., temporary node) will be added as an initial university node and will be removed once enough nodes have joined the system.
* For the overwriting procedures (i.e., for nodes that may leave the system, such as a university node goes out of business or those nodes that may harm the system), the NC can be used to perform the overwriting procedures to revoke permissions associated with that nodes. The overwriting procedure is accomplished by submitting a request to remove a node from the system via a voter node and reaching the majority of votes by the rest of voters. This would then require overwriting the type of a node as terminated, removing it from the NC, and deleting its related information from the various contracts.
## 4 Discussion
### MRI-Based Radiomic Features (Table 1)  
MRI features have been widely investigated in many retrospective clinical studies with good performance results in assessing HPV+ status [31; 32; 33]. In this context [PERSON] et al. [34] recently analyzed 141 patients and 2092 radiomic features extracted from both Primary Tumor (PT) and Largest Pathological Nodes (LN) on Contrast-Enhanced (CE) T1-weighted and T2-weighted MRI. They found that a model built upon PT and LN-fused imaging features outperformed the sole use of PT or LN features in the prediction of p16 status and that a fusion model based on multisequencing yielded better performance than single-sequence models, with an AUC of 0.91. In the same year, a Dutch study [35] supported the role of MRI-radiomics as a potential imaging biomarker with an AUC of 0.79 in a population of 249 patients. Among selected features (\(n\) = 498) in pre-treatment T1-weighted MRI scans were tumor intensity variation, sphericity, diameter and compactness. The combination of clinical and radiomic models provided an even higher performance, with an AUC value of 0.89. The combined model also outperformed the only radiomic one (AUC 0.871 vs. AUC 0.764) in [PERSON] et al. [11] in a population of 153 patients whose features were extracted from pre-treatment T1-weighted MRI scans. Clinical variables included gender, age, tobacco and alcohol exposure.  
[PERSON] et al. [36] investigated three machine-learning classifiers when using multisequencing features (Apparent Diffusion Coefficient (ADC), T1-weighted imaging, fat-suppressed T2-weighted imaging, fat-suppressed CE T1-weighted imaging). Logistic regression and random forest classifier yielded higher accuracy than XG boost classifier for the determination of HPV infection status (AUC 0.77 and 0.76 vs. 0.71).  
In [PERSON] et al.'s work [37], texture analysis was performed on T2-weighted, contrast-enhanced T1-weighted and ADC maps obtained from Diffusion-Weighted Imaging (DWI) series. They found that ADC was significantly lower in HPV-positive oropharyngeal squamous cell carcinoma compared with HPV-oropharyngeal squamous cell carcinoma. The reason for this finding remains unknown, but some hypotheses may be conceived. The first one is the small stromal volume found in HPV-positive disease [38]; also, the nests of lymphoid cells surrounding cancer cells in HPV+ disease could explain the increased tissue density and the consequent lower ADC [39].  
Despite the favorable data in this context, clinical application of radiomics is hampered by laborious and time-consuming tumor segmentation. Alternative delineation strategies could overcome these obstacles. As a matter of fact, [PERSON] et al. [40] in 2022, investigated different delineation methods for detecting HPV status in OPSCC with the result that 2D simple delineation of the largest tumor axial diameter outperformed 3D delineation (AUC 0.84 vs. AUC 0.76). Faster delineation could improve adoption of radiomics in clinical practice.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
## 4 Problem Statement and First-Cut Method
### First-Cut Method  
From the previous subsection, we know that a histogram with uneven data distribution may often fail to meet the relative accuracy requirements for small bins when it adds the same absolute noise to all bins to satisfy \(\epsilon\)-DP. Then, can we use two straightforward solutions? Therein, one is adding an equal relative noise to each bin in the histogram to meet the accuracy requirements for all of the bins, and the other one is taking a scale parameter that meets the relative accuracy requirement of the minimum bin as the overall scale parameter. The answer is no. Although these two solutions are simple and brute-force, they are not the best and maybe lead to privacy disaster in some extreme cases.  
Firstly, we assume that the same unit noise is \(\gamma\), and \(\gamma=\frac{\lambda_{j}}{H_{j}}\) for \(j\in[1,n]\), where \(H_{j}\) is perturbed by Laplace noise with \(\lambda_{j}\) as its scale parameter, respectively. Since \(H_{1}\leq H_{2}\leq\cdots\leq H_{n}\), it has \(\lambda_{1}\leq\lambda_{2}\leq\cdots\leq\lambda_{n}\). According to \(\lambda_{j}=\frac{1}{\epsilon_{j}}\), it has \(\epsilon_{1}\geq\epsilon_{2}\geq\cdots\geq\epsilon_{n}\). Then, according to Property 1 of DP, it can be seen that it satisfies \(\epsilon=max(\epsilon_{j})=\epsilon_{1}\)-DP, where \(j\in[1,n]\). In the extreme case of \(H_{1}<<H_{n}\), then \(\epsilon_{1}>>\epsilon_{n}\); this may cause privacy disaster.  
Secondly, according to the histogram publishing method adding the same noise to each bin to satisfy \(\epsilon\)-DP, the relative noise of each bin is assumed to be \(\gamma_{j}=\frac{\lambda}{H_{j}}\), where \(\lambda=\frac{1}{\epsilon}\) and \(j\in[1,n]\). In the extreme case where the accuracy requirement of the minimum bin is \(\gamma<<\gamma_{1}\), the perturbation has to be implemented with the scale parameter \(\lambda^{\prime}\) in order to meet the accuracy requirement of the minimum bin, where \(\lambda^{\prime}=H_{1}\gamma<<H_{1}\gamma_{1}=\lambda\). From \(\lambda^{\prime}=\frac{1}{\epsilon^{\prime}}<<\lambda=\frac{1}{\epsilon}\), it has \(\epsilon^{\prime}>>\epsilon\), which also may lead to a privacy disaster.  
Although these two solutions can directly meet the availability requirements of small bins, they may violate \(\epsilon\)-DP seriously. Furthermore, as we have learned, the availability of small bins will also impact the overall availability of the histogram. Consequently, if the requirement of satisfying \(\epsilon\)-DP remains, is it feasible to prioritize enhancing the availability of small bins while concurrently considering the histogram's availability (the mean relative error of histogram)? In the next section, we will propose a sanitization algorithm based on this question.
## 5 System Operational Simulation
### Visualization of System Operation  
Based on the simulation platform illustrated in section 5.1, we can visualize and monitor the system operation in OMNet++ [33]. The purpose of the visualization is to investigate the initialization/configuration of the system and the performance of the routing protocols. Besides, we need to know if the polling and data exchange messages can be transferred correctly or not, and if the distributed data mining algorithm is performed as expect.  
The network topology of the simulation is designed as Figure 7. There are 18 sensor nodes, including 12 SSN nodes from tic[0] to tic[11] and 6 MSN nodes from tic[12] to tic[17]. Data can be sent and received in bi-directions along the edges. We use the air pollution data of four pollutants NO, NO\({}_{2}\), SO\({}_{2}\), and O\({}_{3}\) at 1-minute intervals in urban environment from 8:00 to 17:59 within a day as the sampled data for each MSN. The total number of the dataset is 7200.  
The simulation is performed on the computer with P4 CPU, 3.2 GHz main frequency. The link delay is set to be 100 ms. The running details of the system can be monitored as shown in Figure 8.  
Figure 7: The network topology of the simulation.  
After the initialization, all the nodes begin to exchange the polling and data messages. We can see from Figure 8(a) that all the messages can be sent and received between the source node and the destination node hop by hop. Figure 8(b) shows the receiving procedures of every packet in tic[1] and tic[2] within 10.7421875 seconds, while tic[1] received 50 messages and tic[2] received 71 messages. These messages include all the control messages (such as initialization, topology discovery and routing information), polling messages and data exchange messages. The receiving speeds in these nodes present an approximately linear increasing, which means the system can send and receive messages with very low collision and packet lost.
# Protograph LDPC Code Design for Asynchronous Random Access +
## 3 Code Design for the Asynchronous Random Access Channel
### Code Design for Gaussian Interference
#### 3.4.3 Asymptotic Results for Gaussian Interference  
In Figure 6, we depict iterative decoding thresholds \(\sigma_{\iota,\text{th}}^{2}\) versus \(\alpha\) on the surrogate channel with Gaussian interference for the code ensembles with base matrices \(\mathbf{B}_{\text{A}}\), \(\mathbf{B}_{\text{5G}}\) and \(\mathbf{B}_{\text{5G}}^{\pi}\). In all three cases, the design rate \(R_{c}=1/2\) and \(E_{s}/N_{0}=6\) dB. We provide thresholds for the cases when the interferer is at the beginning or at the end of a packet. As a reference, we also show \(\sigma_{\iota,\text{o}}^{2}\) versus \(\alpha\) from the outage capacity expression in (3).  
Observe from the figure that the ad-hoc code design (base matrix \(\mathbf{B}_{\text{A}}\)) performs close to the capacity curve. Furthermore, owing to the symmetry constraint in (6), the code ensemble shows the same behaviour for the interference being at the beginning or at the end of a codeword. The 5G-like protograph code ensemble (base matrix \(\mathbf{B}_{\text{5G}}\)) performs differently if the interferer is from the left or right. By permuting the code (base matrix \(\mathbf{B}_{\text{5G}}^{\pi}\)) as described in Section 3.4.2, the code ensemble performance gets closer to capacity. Further, for \(\alpha\) close to one, both 5G-like and ad-hoc code design perform similarly. This is because here, the channel is like a conventional AWGN channel for which 5G codes are known to be among the best LDPC codes.
## 4 Experiment and Results
### Statistical BertGCN with a Longer Input Sequence  
The longer model with 280 tokens showed a relatively consistent performance pattern (Figure 2), with no significant difference observed in the performance when varying the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \(\lambda\) = 0.0 & \(\lambda\) = 0.1 & \(\lambda\) = 0.2 & \(\lambda\) = 0.3 & \(\lambda\) = 0.4 & \(\lambda\) = 0.5 & \(\lambda\) = 0.6 & \(\lambda\) = 0.7 & \(\lambda\) = 0.8 & \(\lambda\) = 0.9 & \(\lambda\) = 1.0 \\ \hline Clued & 0.8553 & 0.8569 & 0.8585 & 0.8661 & 0.8663 & 0.8679 & 0.8711 & 0.8665 & 0.8720 & 0.8644 & 0.8663 \\ Un-clued & 0.8613 & 0.8535 & 0.8635 & 0.8606 & 0.8711 & 0.8645 & 0.8629 & 0.8668 & 0.8665 & 0.8663 & 0.8674 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validation accuracy by \(\lambda\) (length = 128 tokens).  
composition parameter \(\lambda\). The baseline model achieved a validation accuracy of approximately 89.50% for both the cloud and un-clued datasets, which was close to the highest accuracy. The cloud model performed best at a \(\lambda\) condition of 0.8, reaching a validation accuracy of 89.63%, while the un-clued model achieved its highest accuracy at a \(\lambda\) condition of 0.3, with a value of 89.79%. The test accuracy of the best model was 86.61%for the cloud case and 85.75% for the un-clued case, respectively. Conversely, the lowest accuracy for both datasets was observed at the \(\lambda\) condition of 1.0, with the cloud and un-clued models recording values of 88.16% and 88.67%, respectively (Table 2).  
In summary, Table 2 shows that the performance of a model trained with a fixed 280 tokens does not improve by varying \(\lambda\). On the contrary, it decreases as \(\lambda\) approaches 1.0. These findings suggest that \(\lambda\) does not play a significant role when Bert embeddings can be fully used.