esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Scale Implementations for Twitter Sentiment Classification
## 5 Implementation
### Our Datasets for Evaluating MapReduce versus Spark Framework  
We evaluate our method using two Twitter datasets (one for hashtags and one for emoticons) that we have collected through the Twitter Search API [51] between November 2014 to August 2015. We have used four human non-biased judges to create a list of hashtags and a list emoticons that express strong sentiment (e.g., #amazed and : ()). Then, we proceed to a cleaning task to exclude from the lists the hashtags and emoticons that either were abused by Twitter users (e.g., #love) or returned a very small number of tweets. We ended up with a list of 13 hashtags (i.e., \(H=\{\#\)amazed, #awesome, #beautiful, #bored, #excited, #fun, #happy, #lol, #peace, #proud, #win, #wow, #wtf\}) and a list of four emoticons (i.e., \(E=\{\ :\ :\ \}\), :(, xD, <3)).  
We preprocessed the datasets that we collected and kept only the English tweets which contained five or more proper English words. To identify the proper English word, we used an available WN-based English dictionary and do not include two or more hashtags or emoticons from the aforementioned lists. Moreover, during preprocessing, we have replaced URL links, hashtags and references by URL/REF/TAG meta-words as stated in [7]. The final hashtags dataset contains 942,188 tweets (72,476 tweets for each class) and the final emoticons dataset contains \(1,337,508\) tweets (334,377 tweets for each class). The size of the hashtags dataset is 102.78 MB and the size of the emoticons dataset is 146.4 MB. In both datasets, hashtags and emoticons are used as sentiment labels and, for each sentiment label, there is an equal amount of tweets. Finally, in order to produce non-sentiment datasets, we used the Sentiment140 API [52; 53] and the dataset used in [54], which is publicly available [55]. We fed the no hashtags/emoticons tweets contained in this dataset into the Sentiment140 API and kept the set of neutral tweets. We produced two non-sentiment datasets by randomly sampling 72.476 and 334.377 tweets from the neutral dataset. These datasets are used for the binary classification experiments (see Section 4.1).  
We assess the classification performance of our algorithm using the 10-fold cross validation method and measuring the harmonic f-score. For the Bloom filter construction, we use 999 bits and three hash functions. In order to avoid a significant amount of computations that greatly affect the running performance of the algorithm, we define a weight threshold \(w=0.005\) for feature inclusion in the feature vectors. In essence, we eliminate the most frequent words that have no substantial contribution to the final outcome.
# Blockchain-Based Platform to Fight Disinformation Using Crowd Wisdom and Artificial Intelligence
## 4 Solution Description
### General Overview  
All the solutions proposed by various authors, which have been previously discussed, have some limitations. A major drawback is the fact that news publishers must use a particular platform to publish their news. This is the main reason why these solutions were not adopted by the mainstream. To address this problem, our solution does not impose a unique news platform. We let news agencies and publishers complete freedom as to where to publish their news and crawl their sites to fetch the news content into our blockchain-based system. Here, federated AI modules and crowd wisdom take over and analyze this content in a transparent and decentralized manner.  
The system architecture described in this paper integrates the blockchain technology, artificial intelligence, human actors, off-chain distributed data storage and web crawlers. Online news from different agencies and news publisher sites are automatically fetched, aggregated and analyzed in parallel by federated AI modules and human validators. The end results are made available through a web portal where everyone can come and check the truth behind online news articles together with detailed analysis proofs.  
Figure 1 presents the main features of overall architecture of the system.  
The main components are:  
* the FiDisD blockchain network, which stores information regarding the news items from the off-chain datastore,
* the web crawlers, which include the web scrapers, which are used to extract the news articles from the publisher agencies,
* the datasource management, which is used to determine what article URLs are used by which web crawlers and what web scrapers extract the actual article information,
* the off-chain datastore, which stored the extracted article information,  
Figure 1: System Architecture Overview.  
the federated AI and the crowd wisdom, which are used to validate the articles,
* the FiDisD portal application, which allows the users to view the extracted and validated articles,
* the client APIs, which can be used by third-party application to interrogate and obtain the information provided by the FiDisD solution.  
Regarding the implementation of the conceptual architecture from Figure 1, for simplicity's sake, the datasource management, the communication with the off-chain datasource, and other business logic required for exposing the required APIs are all encapsulated in the Offchain Core component.
## 3 Design of NexoNet System
### Threat Models  
Within the proposed NexoNet's incentive mechanism and PoAP consensus mechanism, several types of attacks could potentially endanger system security:  
* Rapid content publication attack: In the NexoNet, publishing and purchasing content allows users to accumulate PV, which can be used to participate in the consensus mechanism by voting. Thus, users might publish a vast number of low-quality works in a short timeframe, attempting to quickly amass substantial PV to participate in consensus voting and illicitly gain economic benefits. Such behavior could severely compromise the fairness of the system's user participation assessment algorithm, diminish the system's reputation, and increase network latency.
* Collusion attack: The maintenance and updating of the blockchain are conducted by maintainers within NexoNet. To reflect the decentralized nature of the system, which is governed by user autonomy, users vote for maintainers to determine their weight in the consensus mechanism, thereby supporting their involvement in subsequent block generation and validation. To obtain additional user votes and the right of bookkeeping in the consensus mechanism, maintainers might collude with some users, bribing them for more votes to gain greater authority over new block decisions and earn more from the blockchain system incentives mechanism.
* Byzantine attack: During the execution of the blockchain consensus mechanism, some maintainers might exhibit various erroneous conditions or malicious behaviors, including denial of service, incorrect generation and verification of blocks, and other Byzantine faults. Such attack could severely impact the operation of the blockchain consensus mechanism, for instance, reducing the efficiency of consensus operation or even causing the consensus mechanism to fail.
## 5 Security Analysis  
For the empirical evaluation of the PUF security metrics, we fabricated 30 PUF cores, extracted the binary PUF responses, and measured the corresponding analog output voltages under the impact of changing operating conditions. Table 1 shows all test cases applied in our experiments.  
The supply voltages of the PUF control logic peripherals \(VDD_{eval}\), \(VDD_{core}\), and \(V_{in}\) are provided by a Keithley HM80403 triple power supply. We calculate the reliability, uniqueness, and bit errors using the following procedure:  
**Step 1**: _Reference challenge._ A fixed reference challenge \(C_{ref}\) is generated, consisting of all combinations of inverter addresses emitted in lexicographic order without repetitions.  
**Step 2**: _Reference response._ For each PUF core instance, a reference response \(R_{ref}\) is extracted by applying the reference challenge under nominal operating conditions. The reference response is defined by determining the most occurring bit sequence out of a set of 125 PUF responses measured during the enrollment phase.  
**Step 3**: _Data acquisition._ As shown in Table 1, for all test cases, each PUF core instance is evaluated 125 times under the stated operating conditions and its responses are stored in a database.  
Figure 4: PUF platform in the climatic chamber.  
Figure 3: PUF platform software. (**a**) flow diagram of the PC software; (**b**) flow diagram of the microcontroller software (simplified).  
**Step 4**: _Analysis._ In terms of PUF security analysis, the reliability, uniqueness, and bit errors across all measured PUF cores are computed. Furthermore, by performing analog voltage measurements with all PUF cores, the impact of voltage variations on the PUF metrics is investigated.
## 4 Vibration Properties of the Proposed Metamaterials
### Analysis of the Influence of Design Parameters
#### 4.2.3 The Influence of Pin Material  
Through the structure modal analysis, it can be seen that changing the density of the CDCHM metal pins can realize the adjustment of the bandgaps. Figure 10 shows the bandgap characteristics of the CDCHM with metal pins of the same size and different materials (aluminum, copper and tungsten). It can be seen from the vibration modes that determine the first bandgap of the CDCHM with metal pins of different materials depicted in Figure 10e that the introduction of the metal pins produces a local resonance effect between the ring and the metal pins. As the metal pin is successively made of aluminum, copper and tungsten, the density gradually increases, and the CDCHM opens a new bandgap in the low-frequency region below 2000 Hz, which will bring better low-frequency vibration-suppression ability to the CDCHM. On the other hand, the distributed bandgaps in the case of adding low-density metal pins gradually concentrates to form a new bandgap with a wider width after the density increases, especially when the metal pin is made of tungsten due to a significant increase in the density, and the frequency band from 6000 Hz to 10,000 Hz can be almost fully covered by the bandgaps. As the density of the pins increases, the first bandgap gradually moves to the low frequency, and the bandgap width increases from 174 Hz to 219 Hz.  
According to the above analysis, the bandgap properties of the metamaterial can be regulated by adopting metal pins made of different materials. It offers a reference for the optimal design of metamaterials so as to meet the requirements of the different vibration absorption frequencies in applications.  
Figure 9: The influence of pin size on the bandgaps. (**a**) Structure diagram of CDCHM, (**b**) R = 4 mm, (**c**) R = 5 mm, (**d**) R = 6 mm; (**e**) vibration mode of the first gap.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 3 Results
### System Stability Evaluations  
#### 3.1.1 Flight Stability Test  
The floats attached to the drone reduced the stable hovering time (Table 1). The hovering durations controlled within a \(\pm\)1.5 m \(y\)-axis range were 34 \(\pm\) 6 s for PET bottles, 14 \(\pm\) 1 s for polystyrene foam bars, and \(>\)180 s for the base drone (Beaufort wind force scale 2-3, Table 1). In contrast, the results obtained using these floats were similar to those obtained under light wind conditions (Beaufort wind force scale 1-2, Table 1). With the disk-type floats (Figure 6c, for the highest floating stability among the candidate floats), the drone was unable to reach the test height (1.5 m). During the test, the GNSS accuracy indicated on the SplashDroneTM 4 controller [18] was 10 (\(<\)1 m location error), irrespective of the partially shaded (up to 40\({}^{\circ}\) of elevation angle) GNSS antenna by the upper mounter. The PET bottle floats were selected for subsequent field tests.
## 3 Results
### Microhardness (KHN)  
Figure 2 box plots present the median Knoop hardness (KHN) of the study groups at baseline (T0) and after whitening (T1). The study groups showed decreased KHN after whitening, except for specimens immersed in Group B (distilled water), which showed an increased KHN (341.5, 314.7-371.2). The lowest KHN was observed for Group E specimens (227.9, 202.3-272.3). Among the groups whitened with OTC products, iWhite Dark Stains Whitening Kit (Group D) specimens demonstrated the highest KHN (328, 314-340) and whitening pen (Group F) specimens demonstrated the lowest KHN (318.8, 311.4-323.7).  
Table 3 presents the significant difference in median KHN within the groups and between the groups at T0 and T1 measurement intervals. The comparison of KHN within the groups from T0 to T1 revealed a significant decrease in the hardness for Groups A, C, E, and F (\(p\) = 0.001). Group B specimens showed increased KNH from T0 to T1; however,  
Figure 2: Box plots of Knoop hardness (KHN) of the study groups at different measurement intervals. Group A—OpalescenceTM PF; Group B—distilled water; Group C—White Dark Stains Toothpaste; Group D—24 K white charcoal teeth-whitening pen; Group E—0.25% Citric.  
the difference was not statistically significant (\(p\) = 0.112). Similarly, group D specimens demonstrated a nonsignificant decrease in KHN from T0 to T1 (\(p\) = 0.29).  
The comparison of KHN showed no significant difference between the groups at T0 (H = 8.449, \(p\) = 0.133). However, the Kruskal-Wallis test demonstrated significant difference in KHN between the groups at T1 (H = 55.83, \(p\) < 0.0001). Hence, pairwise comparison was performed between the groups using the Mann-Whitney test, which showed significant difference in KHN between Group E vs. [Group A (\(p\) = 0.012), Group B (\(p\) < 0.0001), Group C (\(p\) < 0.0001), Group D (\(p\) < 0.0001), and Group F (\(p\) = 0.032)]; Group F vs. Group B (\(p\) = 0.005) and Group A vs. Group B (\(p\) < 0.013).
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 5 Conclusions  
In order to solve the problem of a current and voltage phase lag caused by various factors in the WPT system, this paper proposes a frequency-tracking method based on a FPGA to accurately track the current and voltage phase differences on the transmitting side. A frequency-tracking system and a control block diagram are built. A scale-down prototype is fabricated, and the experimental results show that the phase difference of the system remains at 11.5\({}^{\circ}\) under different transmission distances and load resistances. This satisfies the expected phase difference between 10.5\({}^{\circ}\)-13\({}^{\circ}\) and ensures that the system should always work under the ZVS state. In the range of over-coupling conditions, the output power and transmission efficiency are significantly improved compared with the fixed-frequency system.  
Conceptualization, X.Z. and Z.C.; methodology, X.Z.; software, Z.C.; validation, X.Z., Z.C., and Y.G.; formal analysis, X.P.; investigation, R.H.; resources, M.X.; data curation, X.Z.; writing--original draft preparation, Z.C.; writing--review and editing, X.Z.; visualization, Y.G.; supervision, X.P.; project administration, R.H.; funding acquisition, M.X. All authors have read and agreed to the published version of the manuscript.  
Figure 18: Voltage and current phase difference and coil distance, load resistance relationship diagram: (**a**) voltage and current phase difference versus load diagram; (**b**) voltage and current phase difference versus coil distance diagram.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52077153, and the Natural Science Foundation of Tianjin, grant number 20 JCYBJC00190.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Implementation, Evaluation Details
### Metrics for Retinal Vessel Segmentation  
Retinal vessel segmentation is a critical task in medical image analysis aimed at extracting blood vessels from retinal fundus images. Various evaluation metrics are employed to assess the performance of segmentation algorithms. In this section, we describe the metrics commonly used for evaluating retinal vessel segmentation.
###### Abstract  
A novel edge-termination structure for a SiC trench metal-oxide semiconductor field-effect transistor (MOSFET) power device is proposed. The key feature of the proposed structure is a periodically formed SiC trench with a bottom protection well (BPW) implantation region. The trench can be filled with oxide or gate materials. Indeed, it has almost the same cross-sectional structure as the active region of a SiC trench MOSFET. Therefore, there is little or no additional process loads. A conventional floating field ring (FFR) structure utilizes the spreading of the electric field in the periodically depleted surface region formed between a heavily doped equipotential region. On the other hand, in the trenched ring structure, an additional quasi-equipotential region is provided by the BPW region, which enables deeper and wider field-spreading profiles, and less field crowding at the edge region. The two-dimensional Technology Computer Aided Design (2D-TCAD) simulation results show that the proposed trenched ring-edge termination structures have an improved breakdown voltage compared to the conventional floating field ring structure.  
breakdown voltage; edge termination; conventional floating field ring; SiC trench structure; bottom protection well
## 3 Materials and Methods
### Error Metrics  
To assess the performance of the DL models, several statistical error metrics were used, such as the following:  
RMSE, described in Equation (4), calculates the square root of the mean of squared differences between predicted and actual values. Higher prediction accuracy is shown by lower RMSE values. MAPE, presented in Equation (5), measures the average percentage difference between predicted and actual values. Standard Deviation of Absolute Errors (Std\({}_{\text{AE}}\)), shown in Equation (6), evaluates the variability of prediction errors by calculating the Standard Deviation of Absolute Errors (AE). The Relative Absolute Error (RAE), defined in Equation (7), compares the model's performance to the mean prediction error, providing a comparative performance assessment. The Standard Deviation of Absolute Percentage Errors (Std\({}_{\text{APE}}\)), given in Equation (8), expresses the variation values in APE (Absolute Percentage Error), providing insights into the dependability of the model's predictions [61; 62].  
\[RMSE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(x_{i}^{*}-x_{i}\right)^{2}} \tag{4}\]  
\[MAPE=\frac{1}{N}\sum\limits_{i=1}^{N}\frac{\left|x_{i}^{*}-x_{i}\right|}{x_{i} ^{*}} \tag{5}\]  
\[Std_{AE}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(\left|x_{i}-x_{i}^{*} \right|\right.-MAE)^{2}} \tag{6}\]  
\[RAE=\frac{\sum_{i=1}^{N}\left|x_{i}-x_{i}^{*}\right|}{\sum\limits_{i=1}^{N} \left|x_{i}-\overline{x}\right|} \tag{7}\]  
\[Std_{APE}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(APE_{i}\right.-MAPE)^{2}} \tag{8}\]
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Position Aware Routing Protocols
#### 4.2.2 Adaptive Beacon Scheme for Geographic Routing (ABPP)  
[PERSON] and [PERSON] proposed an adaptive beacon scheme ABPP [43]. The mechanism consists of two parts:  
(1) Location prediction (PP) method combined with linear regression mechanism and local weighting: utilizing the position history of the UAV, the weighted linear regression model is used to predict the future position of the UAV;  
(2) Adaptive Beacon (AB) scheme: essentially, this method is a control method based on fuzzy controllers that use the basic ideas and theories of fuzzy mathematics. The input side is the prediction error and the output side is the beacon interval. It achieves adaptive adjustment of the beacon frequency. Figure 7 presents the overview block diagram of the AB scheme directly, \(e\) and \(e^{\prime}\) are the input of AB, \(bi\) represents the beacon interval, and is output of AB, then  
\[\begin{split} e&=\sqrt{(x-x(t_{c}))^{2}+(y-y(t_{c} ))^{2}},\\ e^{\prime}&=\frac{e-e_{l}}{t_{c}-t_{l}}.\end{split} \tag{3}\]  
where \(e\) is the prediction error in PP scheme and \(e^{\prime}\) is error change rate. Then, AB will carry out the process of fuzzy inference according to the fuzzy rules. After fuzzy inference, the fuzzy set of \(bi\) is given. To get crisp value of \(bi\), defuzzification process will be performed.  
This paper introduces ABPP into the classic geographic routing protocol GPSR [44]. As shown in Figure 8, the upper picture shows the original GPSR adjacent node information table, which contains the location and time information of \(k\) adjacent UAVs. Obviously, in the source table, the information it stores is not real-time, and there will be errors when exiting. The table we see below is a modified neighbor table. The latest beacon information is usually stored in such a container.  
**Advantages:** ABPP technology can not only predict location information accurately, but also achieve higher PDR (package delivered ratio) improvement while greatly reducing beacon overhead.  
**Disadvantages:** This paper ignored the stereoscopic property of network structure in FANET, only considered the \(x-plane\) and \(y-plane\) of nodes' behavior.  
Figure 7: Frame diagram of AB.