esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Nanometer-scale measurement devices with high accuracy and absolute long-range positioning capability are increasingly demanded in the field of computer numerical control machining. To meet this demand, the present report proposes a capacitive absolute linear displacement sensor with time-grating that employs a vernier-type structure based on a previously proposed single-row capacitive sensing structure. The novel proposed vernier-type absolute time-grating (VATG) sensor employs two capacitor rows, each with an equivalent measurement range. The first capacitor row is designed with \(n\) periods to realize fine measurement, while the second capacitor row is designed with \(n-1\) periods, and the phase difference between the second row and the first row is employed to obtain absolute positioning information. A prototype VATG sensor with a total measurement range of 600 mm and \(n\) = 150 is fabricated using printed circuit board manufacturing technology, and its measurement performance is evaluated experimentally. Harmonic analysis demonstrates that the measurement error mainly consists of first-harmonic error, which is mostly caused by signal crosstalk. Accordingly, an optimized prototype VATG sensor is fabricated by adding a shielding layer between the two capacitor rows and designing a differential induction structure. Experimental results demonstrate that the measurement error of the optimized prototype sensor is \(\pm\)1.25 \(\upmu\)m over the full 600 mm range and \(\pm\)0.25 \(\upmu\)m over a single 4 mm period.  
capative linear displacement sensor; vernier-type absolute structure; differential sensing structure; time-grating +
Footnote †: journal: Applied Sciences
## 3 Method
### Training  
#### 3.5.1 Experimental Configuration  
The training process for AttenFlow's components was conducted on a high-performance computing cluster equipped with eight NVIDIA A100-PCIE-40 GB GPUs (NVIDIA, Santa Clara, CA, USA) interconnected via NVLink for high-bandwidth GPU-to-GPU communication. The system featured AMD EPYC 7763 64-Core Processors operating at 2.45 GHz, supported by 512 GB DDR4 ECC RAM with a theoretical peak bandwidth of 204.8 GB/s. Storage infrastructure comprised four NVMe SSDs configured in RAID0 for a total capacity of 10 TB, ensuring minimal I/O bottlenecks during intensive training operations.
## 4 Principles of Fuel Cell
### Types of Fuel Cell
#### 4.1.4 Polymer Electrolyte Membrane Fuel Cell (PEMFC)  
Solid polymer membranes are used as the electrolyte in PEMFCs. The polymer membrane is perfluorosulfonic acid referred to as Nafion. This polymer membrane is acidic; hence, the ions transported are hydrogen ions or protons. The PEMFC is fueled with pure hydrogen, and the oxidant is air or pure oxygen. PEMFCs are low temperature fuel cells that conduct hydrogen ions (\(\mathrm{H^{+}}\)), making them not fuel-flexible. These fuel cells are the most widely used in the transportation sector because they are low temperature FCs, operating around 80 \({}^{\circ}\)C, thus they have relatively short starting and stopping times. Another advantage to PEMFCs is that they have very high efficiency and power density in the vehicle engine size class [45]. These features are well-suited to a vehicle power source where power density is desired and the dynamic power demands are significant. Some of the drawbacks to PEMFCs include a higher risk of CO poisoning and that they require cooling.  
The overall chemical reaction for PEMFC is:  
\[\mathrm{H_{2}}\to 2\mathrm{H^{+}}+2\mathrm{e^{-}} \tag{9}\]  
The chemical half reactions for the PEMFC are as follows:  
Oxidation reaction:  
\[\mathrm{H_{2}}\to 2\mathrm{H^{+}}+2\mathrm{e^{-}} \tag{10}\]  
Reduction reaction:  
\[\frac{1}{2}\mathrm{O_{2}}+2\mathrm{H^{+}}+2\mathrm{e^{-}}\to\mathrm{H_{2}O} \tag{11}\]  
The PEMFC in an electric vehicle is taking the role of the internal combustion engine in a conventional vehicle, as it is the driving power source in the FC electric vehicle. Thus far, the PEMFC is the only fuel cell found to be suitable for automobiles. By pairing PEMFCs with rechargeable batteries, a hybrid vehicle is created that is competitive with both battery electric and fossil fuel vehicles. The electric motor, the battery, and the PEMFC are the three interdependent parts of this hybrid vehicle [46]. Figure 3 shows the basic structure of an FC-based electrical vehicle.  
Different configurations of the hybrid electric vehicle are discussed in the following section.
## 2 Literature Review
### Challenges with Class Imbalance in Medical Datasets  
A major problem with CVD predictive modeling is imbalanced class distributions. For instance, in many existing cardiac databases, compared to individuals at low or no risk, there are far fewer samples of patients at high risk of heart attacks. This imbalance skews predictions, favoring the majority class and reducing sensitivity for underrepresented groups ([PERSON] et al., 2017; [PERSON] et al., 2017).  
Addressing this problem has led to the widespread usage of the SMOTE for dataset balancing. In order to improve the model's performance on unbalanced datasets, SMOTE creates synthetic samples for the minority class by interpolating the current data points. [PERSON] et al. (2018) examined ML-based heart attack prediction using the UCI dataset without manual FE. SMOTE was used to fix the issue of class imbalance and boost classification accuracy. In order to guarantee high reliability in heart attack prediction, they discovered that a fine-tuned SMOTE-based ANN outperforms existing models. [PERSON] et al. (2018) used the Synthetic Minority Over-sampling Technique - Edited Nearest Neighbors (SMOTE-ENN) for class imbalance correction and optimized hyperparameters to assess seven different classification algorithms. The algorithms included RF, LR, KNN, and neural networks. When compared to more conventional models, KNN performed best to achieve an accuracy of 99% and AUC (0.99). The study's findings stress the significance of tackling class imbalance and optimizing feature selection to enhance prediction accuracy and showcase KNN as a promising contender for early CVD diagnosis.  
[PERSON] et al. (2018) presented an automated cervical cancer prediction system that used KNN imputation and SMOTE to handle missing values and class imbalance. An exceptional accuracy of 99.99% was attained by a stacked ensemble Voting Classifier, combining three ML models. By comparing several scenarios, the study showed that KNN-imputed SMOTE features greatly improved prediction performance. [PERSON] et al. (2018) improved the accuracy, precision, and recall of many classifiers by their evaluation of several resampling approaches, including Adaptive Synthetic Sampling (ADASYN) and SMOTE. Although resampling sometimes overestimated positive instances due to calibration challenges, enhanced discrimination ability, particularly for predicting high- and low-risk instances, increases clinical utility.  
[PERSON]. [PERSON] and [PERSON] (2018) suggested a hybrid strategy that combines SMOTE, undersampling, particle swarm optimization (PSO), and MetaCost to address this problem. It was observed that Ensemble Learning enhanced AUC, recall, and F1-score, whereas MetaCost enhanced sensitivity in experiments conducted on nine medical datasets. SMOTE significantly enhanced AUC, and undersampling improved sensitivity and decreased misclassification costs for very imbalanced data. In order to maximize predictive performance in medical diagnosis, the overall findings suggest that resampling approaches should be used depending on the imbalance ratio within the dataset.
## 6 Discussion
### Comparison w.r.t Sentence Length  
Figure 6 further compares the accuracy of EBAGCN and Entity BERT under different sentence lengths.  
The causes of poor model performance may be more than one. For example, long sentences are likely to affect the accuracy of classification for the following reasons:  
* Contextual meanings for long sentences are more difficult to capture and represent.
* The position of key tokens, such as a predicate, is noisy and, therefore, difficult to determine.  
Intuitively, lacking model interpretability of non-local syntactic relations, sequence-based models such as Entity BERT cannot sufficiently capture long-distance dependence. Thus, the accuracy of Entity BERT drops fiercely as predicted as shown in Figure 6 when the sentence length grows. However, such a performance degradation can be alleviated using EBAGCN, which suggests that catching a mass of non-local syntactic relations helps the proposed model accurately infer the meaning of the entity, especially in longer sentences.  
Figure 5: Semantic distribution maps of BERT before (**left**) and after fine-tuning (**right**). “WV” refers to the original semantics of entities, “bert-M” refers to the semantic representation of metonymic entities in BERT before fine-tuning, “bert-L” refers to the semantic representation of literal entities in BERT before fine-tuning, “ft-bert-M” refers to the semantic representation of metonymy entities in BERT after fine-tuning, “ft-bert-l” refers to the semantic representation of literal entities in BERT after fine-tuning.  
The proposed EBAGCN solve the problem of complex sentence patterns and difficult syntactic understanding. By means of GCN based on the attention mechanism, the semantic and syntactic representation of the context is jointly trained. GCN effectively help integrate syntactic knowledge into vector representation, while attention mechanism highlights the expression of key information in dependency, which eliminates the syntactic noise to a certain extent.
## 3 Related Work
### Implementation  
This study was built on three basic modules, namely, the blockchain, Shibboleth, and HL7. The user identity, such as a CNIC or passport number, is required by the client to fetch his home station information to create a URL to communicate with the HL7 server. Net-HL7 was used in this study, with the help of a PHP parser through the PEAR extension. Figure 7 shows the code snippet of the interaction with the HL7 server.  
When the client requests patient records from the remote HL7 server, the client connected with the network is redirected to the second module, Shibboleth, for authentication, and the Shibboleth module receives authentication from HL7 client. This blockchain network records all of the transactions during the process. The network communication work using the Apache shid daemon and its source is located at http.conf/ (etc/apache2/http.conf) on Apache, which redirects each web request to the mod ship. The communication between IdP and shibd uses Security Assertion Markup Language (SAML), and SSL is also implied to ensure security. During the execution, the service provider trusts the authentication of IdP and creates an authentication assertion as a response of authenticity. The Federated Identity Authentication (FIA) system is used by the Shibboleth for the consensus. When a remote station wants to access the information from the home station using HL7 standards, the FIA registers the remote station with a unique id. Figure 8 shows SAML snippets when a remote station wants to access the information from the home station  
Figure 6: Proposed system’s sequence diagram.  
```
1. Procedure: BlockchainNetworkAccessRegistration
2. Requirements from Health Personnel for Patient Data Access: 1. Personnel Role Pr,
3. Required Time Rt, Credentials for the Blockchain Network access CrBN
4. Patient Pt, ViewNetwork Vn, HealthPersonnel Hp, CreateTransactions Ct,
5. BroadcastToNetwork BtN, AskForAuthorization AFA, Terms and Conditions Tc, Smart
6. Contract SC, BlockchainNetwork BN, PatientUniqueld Pull,
7. If Pt registered then {
8. Vn ();
9. If Hp authorized Pt data then {
10. If Hp authorized to Ct then {
11. Ct (); BtN 0;
12. }
13. }
14. Else { AFA ();
15. If Hp AFA then {
16. If (Pr, Rt ==true) {return CrBN;}
17. }
18.}
19. Else {
20. If Patient agrees on Tc then {
21. Create SC ();
22. BtN 0;
23. If BN approves then {Return Puld;}
24. Else {Request denied}
25. }
26. }
```  
**Algorithm 3**Blockchain Network Access and Registration  
The complete implementation process of this study is shown in Figure 9. Hyperledger Composer uses a business application model (BNA) for data manipulation, in which every response received or dispatch is registered against the unique id of each user. In this study, the BNA append-only mode was used to document all of the transactions on the blockchain.  
Figure 7: Interaction with HL7 server.  
The authorized persons from each organization can view this ledger via an interface, and the ledger can also be used to track or audit any transaction.
# Towards Optimizing Garlic Combine Harvester Design with Logistic Regression
## 3 Design and Analysis of Key Components
### Reciprocating Cutter  
The schematic diagram of the reciprocating cutter working principle is shown in Figure 5, which is mainly composed of a spindle, swing ring, swing fork, swing fork shaft, swing arm and guide rod. The angle between the pin axis and the vertical line is called the swing ring angle \(\alpha\), and the swing range of the swing ring is \(2\alpha\).  
The parameter relationships among the tool path \(s\), swing angle \(\alpha\) and swing arm length \(l\) were as follows:  
\[l=\frac{s}{2\sin\alpha} \tag{6}\]  
where \(l\) is the swing arm length, mm. \(s\) is the moving knife stroke, mm. \(\alpha\) is the swing angle, \({}^{\circ}\).  
The moving knife stroke was \(s=76.2\) mm, and the swing angle was \(\alpha=15^{\circ}\). We substituted them into Formula (6) to obtain the swing arm length \(l\approx 147\) mm. Considering factors such as deviations, the design value was 150 mm.  
Figure 5: Principle diagram of reciprocating cutter. Note: 1. spindle; 2. swing fork; 3. swing ring; 4. swing fork shaft; 5. swing arm; 6. guide rod; 7. cutter.  
Additionally:  
\[v_{p}=\beta v_{a}=\frac{n_{2}s}{30} \tag{7}\]  
where \(v_{p}\) is the average speed of the moving knife, m\(\cdot\)s\({}^{-1}\). \(\beta\) is the cutter speed ratio and its value is usually set to 1.9. \(n_{2}\) is the swing mechanism speed, r\(\cdot\)min\({}^{-1}\).  
With this, we obtained \(v_{a}\) = 0.29 to 0.53 m\(\cdot\)s\({}^{-1}\). Then, \(v_{p}\) = 0.55 to 1.01 m\(\cdot\)s\({}^{-1}\) and \(n_{2}\) = 216.9 to 396.5 r\(\cdot\)min\({}^{-1}\).
## 2 Related Work
### Out-of-Context Object Detection  
Detecting out-of-context objects involves identifying objects in images that do not fit the contextual relations within the scene [19; 20]. Out-of-context objects can be detected using graph-based models that capture relationships between objects [19]. As an example, [PERSON] et al. [21] made use of a graph contextual reasoning network to explicitly capture contextual cues from neighboring objects in order to detect out-of-context objects. The proposed graph convolutional recurrent network (GCRN) framework consists of two components: learning object representations and components for capturing context for object detection.  
Several scene contextual constraints were proposed in [22] in order to improve detection performance. As a postprocessing step, scene contextual constraints can be applied to many state-of-the-art detectors, including faster region-based convolutional neural networks (Faster R-CNN) and You Only Look Once (YOLO). They demonstrated that their model led to an improvement in detection performance over state-of-the-art models.  
An additional form of out-of-context detection involves pairing misleading or false captions with images, creating out-of-context multimodal misinformation. One approach to tackling this issue was presented in [23]: a self-supervised method that uses image-text matching to detect out-of-context image and text pairs. The model uses an input image and two captions from different sources. By comparing the captions to visual elements in the image, the model predicts whether the image was used out of context, achieving a detection accuracy of 85%. Another study by [PERSON] et al. [24] proposed a method to detect out-of-context misinformation using a combination of neural and symbolic techniques. Using the abstract meaning representation (AMR) of the caption, the method symbolically decodes text modality information into fact queries. The relationships between visual and textual elements are then mapped to a symbolic graph. Compared with various baselines, the model detected out-of-context multimodal misinformation more accurately.  
Our study identifies confidential objects in images. This problem is different from identifying objects out of context because confidential objects depicted in an image can be found in their natural environment and are not necessarily out of context. As an example, an image may depict a workplace setting with confidential documents, which may be normal for the setting but still constitutes a privacy risk if leaked.
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
## 3 Results and Discussion  
### Adsorption Experiment  
#### 3.1.1 Effect of pH on the Adsorption of Cs  
The pH value of the aqueous solution will affect the surface charge of the adsorbent, the ionization degree and morphology of the metal ions, and the surface metal-binding sites. Under the conditions of the pH values of 3, 4, 5, 6, 7, 8, and 9, all other parameters were kept unchanged (adsorbent usage: 0.1 g; contact time: 24 h; initial concentration: 0.79 mg\(\cdot\)L\({}^{-1}\); and temperature: 298 K). The effect of pH on the adsorption of Cs\({}^{+}\) was studied. The results are shown in Figure 1. The maximum adsorption capacity of Cs\({}^{+}\) was obtained at pH 8, which may be related to the alkaline nature of the geomaterials. Therefore, a pH of 8 was selected for further study.  
Figure 1: Effect of pH on the adsorption of Cs by fly ash-based geomaterials and slag-based geomaterials.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 4 Proposed Methodology
### Conflict-Free DODAG Construction (Algorithm 2)
#### 4.2.3 Lightweight Network Monitoring  
The WSN must guarantee operation without interruptions (or minimal disruption) and meet the QoS requirements for QoS-aware applications. If the topology changes (due to mobility, disappearance of nodes, or important changes in QoS link values), advertisements for the edge servers are needed. If a local repair of the RPL can correct the problem, no intervention is needed from the servers. The changes are indicated thanks to monitoring performed by the sensor nodes. Since the availability of the nodes and the quality of the communications (the link values) should be controlled, active monitoring is applied [58].  
Figure 4: Reconfiguration of a DODAG.  
To balance the power load of monitors, an alternating and scheduled set of active monitors is applied [62]. The measured values are sent to the BR (and edge servers) using a dedicated simple DODAG.  
Note that the protocol is also suitable for applications where finding multiple feasible paths is important (e.g., in networks where fault tolerance or alternative paths are required).
## 6 Palladium
### Alylic Substitution  
Several methods for the allylic substitution using polymer-bound catalysts exist, in general involving the use of a chiral or non-chiral ligand anchored to a solid support that is subsequently treated with a palladium reagent before application in catalysis. These methods will be briefly summarized here. [PERSON] and [PERSON] performed allylic sulfonation under aqueous conditions using two different Tentagel-supported phosphine palladium catalysts [36]. \(\pi\)-Allyl complex **17** (Scheme 15 a), anchored to the PS-PEG resin via an aromatic amide, was efficient in the sulfonylation of both acyclic as well as cyclic carbonates, and could also be recycled several times without any loss of activity. The chiral complex **18** (Scheme 15b) was applied towards the asymmetric allylic sulfonylation of cycloheptenyl carbonate with up to 81% ee, while a smaller ring size in the substrate, _i.e._ cyclopentenyl or cyclohexenyl gave product with lower enantioselectivity (33-45% ee). The same catalyst was also applied in the desymmetrization of various _meso_ compounds via allylic substitution, affording up to 99% ee when phenol was used as the nucleophile [37].  
**Scheme 15. (a)** Allylic sulfonylation using a Tentagel-supported palladium catalyst. (b) Asymmetric allylic sulfonylation of a cycloheptenyl carbonate substrate.  
[PERSON] and co-workers investigated the use of polymer-bound phosphoramidites, phosphites and related ligands in the asymmetric substitution of an allylic acetate using dimethylmalonate as the nucleophile, but found that although the conversion was high, the enantioselectivity was rather modest and not at the same level as seen for the solution phase counterparts [38]. Better results were obtained by [PERSON], [PERSON] and colleagues who applied polymer bound diphenylphosphinooxazoline (PHOX) ligands in enantioselective amination of allylic acetates [39]. The chiral ligand was attached to the polystyrene support using a click reaction (Scheme 16). Reference ligands containing the same spacers and triazole connecting unit were also prepared for comparative solution phase studies. Several different parameters, such as the spacer length (**19a** and **19b**), presence or absence of potassium acetate, and the nature of the counterion were studied. Reactions involving the polymer-bound complexes in general gave both high yields and good to excellent enantioselectivity with a range of amine nucleophiles. A longer spacer length (as in **19b**) was found to give products in somewhat higher enantiomeric excess, and the use of microwave heating allowed the reaction times to be shortened substantially. The optimal ligand was also tested in a continuous flow system, adapted for use in amicrowave reactor. Although the conversion dropped somewhat, the enantioselectivity still remained on par with earlier results.  
**Scheme 16.** Palladium-catalyzed allylic amination using a supported PHOX-ligand.