esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conclusions  
In this study, we successfully fabricated ZnO and ZnO/ZnS core/shell NW arrays on flexible substrates and demonstrated the piezotonic and piezo-phototronic effects of both devices under applied external compressive strain and UV irradiation. By depositing a ZnS shell layer on the ZnO NWs, the interface states of the ZnO NWs were reduced, which enhanced the charge transport but also suppressed the recombination of photogenerated carriers to achieve a high-performance photodetector owing to the type-II band structure. Under a compressive strain of \(-\)0.15%, the relative increase in the output current from the ZnO/ZnS core/shell NWs was 91.1% compared to that under a \"no strain\" condition, whereas that of the ZnO NWs was 42.7% under the same condition. These results can be attributed to not only the charge separation caused by the type-II band structure but also the piezopotential changes at the junction interface of the core/shell NWs induced by external strain, which enabled efficient charge transport. Furthermore, to demonstrate the change in the Schottky junction caused by the strain-induced piezoelectric potential, we calculated the SBH changes according to the change in the compressive strain applied to the ZnO and ZnO/ZnS core/shell NWs. As the external compressive strain increased, the strain-induced positive piezopotential reduced the SBH, resulting in efficient charge transport. However, upon UV illumination, although the photocurrent was significantly enhanced, the slope of the SBH change decreased, thus inducing a decrease in the relative increase in the photocurrent under a compressive strain of \(-\)0.15% compared to that under the \"no strain\" condition owing to the piezoelectric screening effect. This study provides an in-depth understanding of the physical modulation mechanism, which provides insights into improving the performance of ZnO-NW-based piezotronic and piezo-phototronic devices for various applications.
## 3 Results and Discussion
### Tree-Based Feature Importance
From this data we can safely determine that the CF has relationships with the majority of the features that are responsible for prediction performance. Of those features, LP, MPS, and SLED are the most important features for affecting prediction of the classification model. CF has a strong correlation to these three features.
## 2 The Basic Principles  
### The Basic Principle of the DWDH Method and the EPISM Method  
#### 2.1.1 The Basic Principle of the DWDH Method  
The DWDH method converts sampled perspective images into a rearranged image for exposing. This algorithm is actually a pixel transformation from the film plane of the camera to the SLM plane of hogel. It is usually called 'I to S' transformation. The principle of the DWDH method is shown in Figure 1, There are six main planes: hologram plane, SLM plane, projected SLM plane, camera plane, film plane, and projected film plane.  
In this conversion, the camera lens and the print head of the holographic optical printer are regarded as a point, and a test ray passes through the camera lens and hogel. The pixel where the ray intersects the SLM plane is replaced by the pixel where the ray intersects the film plane, the 'I to S' transformation of a pixel is completed. All pixels on the SLM are replaced by p  
Figure 1: Ray-tracing principle of “I to S” transformation by DWDH method.  
a rearranged image for exposing is obtained. All the hogels on the hologram plane were exposed, and the DWDH holographic stereogram was obtained.  
The pixel correspondence of the DWDH method can accurately reproduce the 3D scene, but the resolution of the sampled image is determined by the number of hogels; high-quality holographic stereograms often require hundreds of thousands of sampled images.
## 11 Further Research Directions and Future Considerations
### Software-Defined Optical Transmission
#### 11.5.2 Sliceable Transceivers  
The increasing growth in traffic and its dynamic nature can be supported by deploying more network resources that are seemingly static. However, in a situation where the cost scales linearly with the required bandwidth, support for the new services might not be cost-effective. To address this, the optical spectrum has to be utilized efficiently. Therefore, with elastic optical networks (EONs) in conjunction with the required bandwidth variable reconfigurable optical add-drop multiplexers (ROADMs), as well as rate and/or reach adaptive (flexible) optical transponders, different transmission parameters such as the baud rates, bit rates, subcarriers, and modulation formats can be dynamically changed and aggregated [175; 180; 181]. Then, the transmission bandwidth can be sliced and software-controlled for a dynamic adaptation and simultaneous support of numerous independent traffic demands and optical reaches [161; 181]. Some of such efforts are based on the adoption of VCSELs with a multicarrier scheme, flexible bandwidth wavelength selective switches, and dense photonic integration [182; 183; 184; 185]. Furthermore, besides being able to address the dynamic increase in the traffic, the sliceable bandwidth variable transponder (S-BVT) applies to high rates of migration and network restoration. Nevertheless, intense and concerted research efforts are still on to realize transponders that are power- and cost-effective, to support the required features [181; 186].  
Furthermore, parallelization in optical transceiver design is ongoing mainly to address the limitation of an electrical bandwidth [181; 186].
# Surface Coatings and Treatments for Controlled Hydrate Formation: A Mini Review
## 3 Hydrate-Public Surface Treatments and Coatings
### Other Surface Treatments
In order to access the efficiency of the coatings to reduce hydrate growth at atmospheric pressure, bare/coated substrates were placed in a cold platform containing THF solutions and the hydrate growth was recorded, and the area calculated using a software (Figure 4). High-pressure experiments with methane as a hydrate former were also carried out in an autoclave to understand the performance and durability of these coatings under field conditions. A steel substrate with one side coated (96% PDMS + 4% SiO2) and the other uncoated was placed in the autoclave at a 65\({}^{\circ}\) angle and partly immersed in the liquid phase. Later, the induction time of the hydrate formation was measured, and it was found that the coated surface delayed the hydrate formation considerably. Moreover, there was no hydrate growth on the coated side, whereas the uncoated side of the substrate was fully covered with hydrate. However, after the high-pressure experiments, the coating immersed inside the liquid phase remained intact, whereas outside the liquid phase got detached, probably due to direct contact with the high-pressure gas phase. Scanning Electron Microscopy was used to obtain the morphologies of bare and coated surfaces and suggested that addition of SiO2 can alter the surface structure of the coatings. SiO2 formed a micro-nanostructure that isolated the contacts between hydrate and the coated surfaces, resulting in reduced adhesion and growth of hydrates. The authors concluded that, despite good inhibition performance by PDMS coating, there was still an issue of its stability under a high-pressure environment.
# Robust Cross-Validation of Predictive Models Used in Credit Default Risk
## 8 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Lending**} & \multicolumn{2}{c}{**Lending**} & \multicolumn{2}{c}{**Lending**} \\
**Validator** & \multicolumn{2}{c}{**Club–N=500**} & \multicolumn{2}{c}{**Club–N=1000**} & \multicolumn{2}{c}{**Club–N=1500**} \\ \cline{2-7}  & \multicolumn{1}{c}{**Error**} & \multicolumn{1}{c}{**Rank**} & \multicolumn{1}{c}{**Error**} & \multicolumn{1}{c}{**Rank**} & \multicolumn{1}{c}{**Error**} & \multicolumn{1}{c}{**Rank**} \\ \hline CV:DOB-WOE/10/50 & 0.3337 & 1 & 0.2514 & 2 & 0.2233 & 3 \\ CV:DOB-WOE/15/50 & 0.3342 & 1 & 0.2546 & 3 & 0.2260 & 4 \\ CV:DOB-WOE/20/50 & 0.3360 & 2 & 0.2564 & 5 & 0.2278 & 5 \\ CV:DOB-WOE/5/50 & 0.3362 & 2 & 0.2491 & 1 & 0.2223 & 2 \\ CV:DOB-SCV/5/50 & 0.3388 & 2 & 0.2462 & 1 & 0.2191 & 1 \\ HO:MCCV/0.1/1000 & 0.3422 & 2 & 0.2587 & 6 & 0.2301 & 6 \\ CV:DOB-SCV/10/50 & 0.3424 & 3 & 0.2506 & 2 & 0.2213 & 2 \\ CV:DB-SCV1/15/50 & 0.3427 & 3 & 0.2563 & 4 & 0.2284 & 5 \\ CV:DB-SCV1/5/50 & 0.3430 & 3 & 0.2535 & 3 & 0.2285 & 5 \\ CV:DOB-SCV/15/50 & 0.3446 & 3 & 0.2552 & 4 & 0.2248 & 3 \\ CV:DB-SCV2/5/50 & 0.3448 & 3 & 0.2557 & 4 & 0.2298 & 6 \\ CV:DB-SCV1/10/50 & 0.3462 & 4 & 0.2544 & 3 & 0.2285 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact of the sample size on the selection error and ranking of each validation method for the Lending Club 2009 credit dataset. Selection error is given by the normalized Kendall Tau averaged over 150 random samples of sizes \(N\) = 500, 1000, and 1500.
## 2 Materials and Methods
### Permutation Feature Importance  
The permutation feature importance breaks the relationship between a feature and the target; therefore, it can be used to calculate the feature importance from the decrease in the model score. When the values of a feature are randomly shuffled, the link between the feature and the target is broken [39]. Furthermore, the permutation feature importance can be computed several times with different random permutations of a feature [31]. The permutation feature importance is defined as [31]:  
\[i_{j}=s-\frac{1}{K}\sum_{k=1}^{K}s_{k,j} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Best Value** & **Worst Value** \\ \hline MSE & 0 & \(+\infty\) \\ \hline RMSE & 0 & \(+\infty\) \\ \hline \(R^{2}\) & \(+1\) & \(-\infty\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The best values and worst values of MSE, RMSE, and \(R^{2}\).  
where \(i_{j}\) is the permutation feature importance of feature \(f_{j}\), \(s\) is the score of the model \(m\) which is trained on the dataset \(D\), \(K\) is the number of different random permutations, and \(s_{k,j}\) is the score of the model \(m\) on data \(\widetilde{D}_{k,j}\) (data \(\widetilde{D}_{k,j}\) is generated by the randomly shuffling feature \(f_{j}\) of the dataset \(D\)) [31].  
In summary, we used \(R^{2}\) as a metric to evaluate the score of the model, and the number of times to permute a feature was set to 100. Finally, we implemented the permutation feature importance on the test set, and the random_state parameter of the permutation_importance was set to 0.
# Switchable Fiber Ring Laser Sensor for Air Pressure Based on Mach-Zehnder Interferometer
## 3 Results and Discussion

Table 3: Sensitivity of pressure sensor for MZI structure. Figure 9: Response of the shift in the wavelength in the laser ring configuration when the pressure is increased in the gas cell where the TCFMI is. It needs to be emphasized that the 120 m fiber reel generated power losses. Although this power decreases, extending the laser sensor dynamic range is possible because the gain (G) is locked to the ring cavity losses (\(\beta\)ring); therefore, if these losses are small, the gain will be equal. Moreover, the high feedback power at the YDF (\(P_{s}^{in}\)), caused by the low cavity loss, intensely saturated the YDF gain [43]. Then, according to Figure 4b, G and firing are altered by the TCMFI in the ring fiber sensor cavity when pressure is applied. As a result, the change alters the wavelength emissions of the laser output. To characterize the fiber laser within the ring configuration, we employed the Mach-Zehnder optical filter, which exhibited optimal performance prior to any pressure variations. With this setup, we achieved a well-defined Gaussian profile centered at \(\lambda\) = 1063.04 nm and with a side mode suppression ratio of 34.17 dB. With a maximum pressure of 25 PSI for the switching action on the right side of the spectrum, we have 0 to 25 PSI, and on the left side, we have 25 to 40 PSI. These results are presented in Figure 8. Finally, it is necessary to mention the suitable stability obtained in the laser emission, as shown in Figure 11, in addition to the excellent repeatability in laser switching.
## 6 Stability Analysis of Cataclastic Loose Rock Slope After Support Under Seismic Conditions
### Deformation Rate Analysis of Typical Section  
The undisturbed state of the central axis section (A-A\({}^{\prime}\)) of the excavation slope is analyzed after the implementation of support measures, and the velocity nephogram is investigated after 1, 2, 3, 4, and 5 s of seismic wave loading application (Figure 28).  
When the cataclastic loose rock mass slope of the mixing plant after the implementation of the support measures is not disturbed by external factors under self-weight stress, the slope is in a stable state, producing a negligible velocity (approximately 5.3 \(\times\) 10\({}^{-6}\) m/s) near the Q\({}^{\text{cell}}\) sediment of the excavation slope. The seismic wave loaded for 1 s gradually propagates from the bottom of the model to the upper slope surface. The bottom of the model is mostly affected by the seismic wave, and its velocity is approximately 0.87 m/s. When the seismic wave is loaded for 2 s, as the seismic wave is gradually transferred from the bottom to the inside of the slope, the slope interior begins to experience disturbance, the Q\({}^{\text{cell}}\) deformation rate of the shallow surface of the excavation slope with weak physical and mechanical strength gradually starts to increase, the supporting measures gradually fail, the excavation slope begins to collapse, and the maximum speed of some collapsed rocks is 1.19 m/s. When the seismic wave is loaded for 3-4 s, the internal cracks of the Q\({}^{\text{cell}}\) soil continue to expand, the collapse deformation dominated by shear failure is intensified, and the maximum velocity of some collapsed rock blocks can reach 2.39 m/s. When the seismic wave is loaded for 5 s, the cumulative deformation rate of the block at the collapse  
Figure 28: Time history velocity nephogram of the A–A\({}^{\prime}\) section after seismic wave loading (unit: m/s). (a) 0 s (b) 1 s (c) 2 s (d) 3 s (e) 4 s (f) 5 s.  
Figure 27: Total displacement curves of monitoring points.  
site reaches the maximum--approximately 3.11 m/s--and the supporting measures are completely ineffective.
# Decentralized Storage with Access Control and Data Persistence for e-Book Stores
## 4 Proposed System
### Two Specific Usage Scenarios  
To facilitate a clear understanding of the roles of individual components in the proposed system, we outline two specific usage scenarios. The first scenario involves the upload of e-book data. See Figure 1 for an illustration. The author A of an e-book uploads their e-book data to the proposed system. The system employs Reed-Solomon codes to divide the data into \(k\) shards. The value \(k\) corresponds to the number of node groups, which will be described later. The register function of the ACC is called to record metadata, such as the title and price of the e-book, in the blockchain. Next, each of the \(k\) shards is uploaded to the d-book-repository. The system automatically propagates each shard to multiple storage nodes that constitute the d-book-repository, enabling efficient distribution without requiring [PERSON] (the author) to wait for the entire process to be completed. Upon receiving a shard from [PERSON], each storage node verifies the integrity of the data to ensure they has not been tampered with; the specific tamper detection method will be detailed later.  
On the other hand, when reader B purchases a book and seeks to obtain its e-book data, the following steps are executed (see Figure 2 for illustration). Initially, [PERSON] acquires the non-fungible token (NFT) of the desired e-book by invoking the mint function of the ACC. Subsequently, [PERSON] establishes a connection with the d-book-repository to identify the storage nodes holding the necessary shards for reconstructing the e-book data. Afterwards, [PERSON] sends a request for a shard to each storage node, where the request contains the \"book title and the signature of reader [PERSON]\", where the signature is generated by the private key of [PERSON]'s Ethereum account with the PeerID of the receiver as the message. Upon receipt of the request, each storage node verifies the Ethereum address of sender [PERSON] from the signature and consults the ACC to confirm the access rights to the requested book of [PERSON]'s account. Upon confirmation, the requested shard is sent back to [PERSON]. Subsequently, [PERSON] restores the e-book data by decoding the shard using Reed-Solomon code after verifying the integrity of the received shards.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 4 Fall Detection with WiFi Signal Based on LSTM Model
### Fall Detection  
#### 4.5.1 Selection of Detection Model  
The collected CSI data were brought into the applicable deep learning network model for training, a suitable fall detection classification model was established, and the test data were detected through the model. When the CSI amplitude information was used as the input data, the Multilayer Perceptron (MLP) deep learning model had many parameters, a slow convergence speed, and a high computing cost. The CNN deep learning model presents the problem of gradient disappearance, which easily leads to performance degradation. The traditional RNN model presents the problem of gradient disappearance when backpropagating, cannot capture the long-term dependence of CSI, and has the problem of gradient explosion. Transformer is a deep learning model architecture for Natural Language Processing (NLP) and other sequence-to-sequence tasks that require too many parameters, high training costs, and large amounts of labeled CSI data to be collected for learning. Therefore, after comprehensive consideration of the cost and the computing power of the terminal, in this study, we finally chose to use the LSTM deep learning model and combine it with a CNN for training and fall detection.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 4 Implementation and Validation
### Implementing the Blockchain Network
#### 4.2.2 Channel Creation for Privacy Preservation  
Channels allow groups of companies to transact without revealing their data to entities that are not channel members. Each channel has its own ledger, which is shared among the peers that have joined that channel.  
From a technical perspective, channels can be considered as messaging paths between peers through the ordering service. Since peers can be joined to multiple channels without information passing from one channel to another, an ordering service may have multiple channels running through it without exposing data to organizations on other channels. This feature is of paramount importance for privacy preservation and a key motivation to choose Hyperledger Fabric as a candidate to implement the model.  
Channels will allow patients to privately communicate sensitive data with specific peers. Thus, data access is preserved from unwanted persons, even from the cloud storage provider.  
Figure 4 shows the privatization of the transactions between a patient and a doctor in two different organizations. The doctor can consult the data of a patient according to the privileges defined in the secret token communicated to both actor sides through one channel.  
In Figure 5, we display data of the owner device related to the communicated token. Only the actor with privilege can display the data of the owner device to retrieve the data originating from the device.
## 3 Results
### Precipitation Detection Ability  
Figure 6 depicts the rainfall detection ability (POD, FAR, CSI) of the various GPPs. The results show that the POD values of ERA5 are highest among all products for the precipitation classes between 0 and 5 mm/day but are among the lowest for rainfall classes > 20 mm. As a matter of fact, the POD of ERA5 decreases steadily with increasing daily rainfall amount. The POD of ARC2, IMERG, RFEv2, and CHIRPS are similar (approximately 0.5 to 0.6) for the precipitation classes < 1 mm/day. However, the POD of IMERG remains rather constant in the range of 0.2-100 mm/day, whereas the PODs of ARC2 and RFEv2 decrease steadily. The POD of CHIRPS first increases slightly up to 5 mm/day and then decreases thereafter. PERSIANN performs worst of all GPPs for the lowest precipitation classes, but the POD tends to increase with increasing rainfall. For precipitation classes > 100 mm/day, only the PERSIANN product has a high POD value, while the PODs for all other GPPs tend towards 0.  
The FARs of all GPPs increase steadily between 0.2 and 20 mm/day. For rainfall classes \(\leq\) 100 mm/day, IMERG shows similar or better performance than all other products. For daily rainfall > 150 mm, only PERSIANN has a low FAR.  
Based on the CSI value, the ERA5 product performs best in detecting precipitation in the 0.2-2 mm/day precipitation range. IMERG performs best in detecting rainfall in the range of 2-100 mm/day. Only PERSIANN performs well for rainfall > 150 mm/day. Both IMERG and PERSIANN show rather constant performance in terms of CSI in the range of 0.2-100 mm, but IMERG outperforms PERSIANN in this range.  
Figure 5: Probability distribution function (PDF) of daily rainfall intensities based on GPP and rain gauge data from September 2018 to August 2020. Note logarithmic (Log10) scale for Y axis.
# Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis
## 3 Classification of Lung Ultrasound Data  
### Methods  
#### 3.1.1 Data Processing  
Due to data availability, all experiments are conducted on the convex ultrasound probes. For the same reason we exclude the three non-COVID-19 viral pneumonia videos (Table 1) and focus on the distinction of healthy lungs, bacterial pneumonia and COVID-19 viral pneumonia. We manually processed all convex ultrasound recordings (179 videos and 53 images) and split the videos into images at a frame rate of 3 Hz (with maximal 30 frames per video, leading to a database of 1204 COVID-19, 704 bacterial pneumonia, and 1326 healthy images. The videos are diverse in length and kind (\(160\pm 144\) frames) with a frame rate of \(25\pm 10\) Hz. All images were cropped to a quadratic window excluding measure bars, texts and artifacts on the borders before they were resized to \(224\times 224\) pixels (for examples see Figure 1A). Apart from the independent test data, all reported results were obtained in a 5-fold cross validation stratified by the number of samples per class. Data was split on a patient-level, hence it was ensured that the frames of a single video are present within a single fold only, and that the number of videos per class is similar in all folds. All models were trained to classify images as COVID-19, pneumonia, healthy, or uninformative. The latter consists of ImageNet[68] pictures as well as neck US data [69]; added for the purpose of detecting out-of-distribution samples. This is particularly relevant for public web-based inference services. In this paper, we present all results _omitting the uninformative class_, as it is not relevant for the analysis of differential diagnosis performance and would bias the results, that is, lead to a higher classification accuracy due to the recall and precision of almost 100% for the uninformative class (please refer to Appendix C.1 for results including uninformative data). Furthermore, we use data augmentation, specifically flips and rotations (up to 10\({}^{\circ}\)) and translations (up to 10%) to diversify the dataset and prevent overfitting.