esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wireless Communication Technologies
### Short-Range Communication Technologies
#### 3.1.2 Bluetooth (IEEE 802.15.1)  
Bluetooth operates in the unlicensed 2.4 GHz frequency band with a communication range of 10 to 200 m. Bluetooth technology can be found in multiple versions with a data rate ranging from 1 to 3 Mbps. However, the maximum data rate can be reached up to 24 Mbps. The Bluetooth Special Interest Group (SIG) proposed Bluetooth Low Energy (BLE) in the Bluetooth 4.0 specifications. Bluetooth 5 [9] is the latest version of the Bluetooth core specifications. Bluetooth 5 mainly focuses on improvement in speed, transmission range, energy efficiency, and co-existence with other short-range technologies. Additionally, Bluetooth 5 is also able to broadcast richer data, which extends far beyond the location information and includes multimedia and URL's files. Considering the significant improvements, it seems like Bluetooth 5 is, in a true sense, a potential candidate for the low-cost and low-power deployment of future FANET. [PERSON] et al. [10] proposed a hybrid scheme using 802.15.1 for FANET. In the proposed scheme, data transmission between the member UAV was done by 802.15.1. In [11], the authors described the implementation of a wireless network platform using Bluetooth technology on a round robin scheduling algorithm. The established Bluetooth-based UAV network includes one master UAV station and up to seven slave UAV stations. The results authenticate that the proposed platform provides reliable communication with low-computational power. [PERSON] et al. [12] conducted a testbed on the basis of the same technology for multiple UAV. The testbed surfaces the way for practical employers of UAV network using Bluetooth technology.
## 3 The Proposed Octree-Hiding Scheme
### The Extraction Process
#### 3.1.3 Non-Blind with Error Correction Approach  
To compensate for the quality of the extracted image and the communication cost, a third extraction methodology is proposed. In this approach, the sender must first obtain the Octree cubes from both the carrier and host video segments. After that, the sender calculates the error between the two region sets and sends only the error information. At the receiver, the cubes are obtained from the carrier video and then combined with the error information received from the sender. Based on Figure 5, the output of the Octree representation is a vector consisting of zeros and ones. If an error has occurred within a specific index, the sender must send the correction of that specific index to the receiver.
# Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
## 3 Materials and Methods
### Data Pre-Processing  
The preparation of text training data for sentiment analysis, whether through deep learning or traditional machine learning, necessitates cleaning before the induction of the classification model. Because user communication is occasionally informal, the data are noisy and inconsistent, so the data require cleaning and transformation into a format that the classification model can understand.  
* Data cleaning The textual data are cleaned by converting all text to lowercase and removing missing values, stop words, punctuation, web links, numbers, special characters, and anything else that is not relevant and therefore can reduce the effectiveness of the sentiment analysis. Some examples of words that are irrelevant and uninformative are shown in Table 1.  
* Tokenization Tokenization is a critical aspect in natural language processing, and it involves the segmentation of simple natural language sentence data into distinct words or indi  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Noise Types** & **Example** \\ \hline Stop words & a, and, the, in, who, where, etc. \\ Special Characters &!, \#, \#,?, \%, etc. \\ Removing duplicate words & yeeees, happyyyyy, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Uninformative words in the dataset.  
Figure 1: The proposed system based on sentiment analysis.  
vidual symbolic tokens [31]. Following the cleaning process, the text data underwent tokenization, resulting in the decomposition of the data into individual words.
* Part Of Speech The part-of-speech (POS) of a word refers to its categorization in terms of syntax or morphological behavior. In English grammar, some of the widely recognized POS categories include nouns, verbs, adjectives, adverbs, pronouns, prepositions, conjunctions, and interjections. The process of POS tagging involves assigning the appropriate POS label to each word in a text. This step is crucial in opinion mining as it enables the extraction of features and opinion words from reviews. POS tagging can either be performed manually or through the use of a POS tagging tool [32].
* Lemmatization The individual words were then subjected to lemmatization, which transformed them into their base forms [33]. Finally, these base forms were transformed into numerical representations.
# Measurement of Structural Loads Using a Novel MEMS Extrinsic Fabry-Perot Strain Sensor
###### Abstract  
In this paper, microelectromechanical systems (MEMS) technology was used to fabricate a novel extrinsic fiber Fabry-Perot (EFFP) strain sensor; this fiber sensor is applied to measure load with higher precision for a small structure. The sensor cavity consists of two Fabry-Perot (FP) cavity mirrors that are processed by surface micromachining and then fused and spliced together by the silicon-glass anode bonding process. The initial cavity length can be strictly controlled, and the excellent parallelism of the two faces of the cavity results in a high interference fineness. Then, the anti-reflection coating process is applied to the sensor to improve the clarity of the interference signal with the cavity, with its wavelength working within the range of the C + L band. Next, the sensor placement is determined by the finite element software Nastran. Experimental results indicate that the sensor exhibits a good linear response (99.77%) to load changes and a high repeatability. Considering the strain transfer coefficient, the sensitivity for the tested structure load is as high as 35.6 pm/N. Due to the miniaturization, repeatability, and easy-to-batch production, the proposed sensor can be used as a reliable and practical force sensor.  
microelectromechanical systems; extrinsic Fabry-Perot interferometer; strain sensor; load measurement; repeatability +
Footnote †: journal: Applied Sciences
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 5 Experiment
### Ablation Studies
#### 5.3.1 Effectiveness of the Dual-Branch Architecture  
We first validated the effectiveness of the dual-branch architecture. We compared our full model with two variants of single branch models, and the experiment results are presented in Table 1. Here, \"DP branch\" (depth prediction branch) and \"DC branch\" (depth completion branch) represent the results obtained using only the depth prediction branch and only the depth completion branch, respectively. It is worth noting that to ensure fair comparison with the final model, the \"DC branch\" variant includes the encoder from the \"DP branch\" to utilize color image information for guidance during the processing. As shown in Table 1 and Figure 5(a), firstly, when trained and tested using only the depth prediction model, it achieved the best numerical results as it focuses solely on the monocular depth estimation task. Secondly, compared to the single-branch depth completion model, the dual-branch network exhibited a significant improvement with 500 sparse points. Furthermore, the superiority of the dual-branch model became more pronounced as the number of valid depth points decreased to 50, 5, or even 0. Specifically, in scenarios where sparse depth points were absent, its performance could rival that of models dedicated solely to the depth prediction branch.
## 10 Future Research Directions  
Future research should focus on the integration between IoT systems for indoor environmental management and smart home devices (e.g., smart windows, smart air purifiers, smart temperature control systems). In other words, IoT systems can automatically adjust the indoor environment based on environmental data and automatically provide a comfortable living environment for humans, which is especially useful for people with asthma, allergies, and other diseases.  
Regarding smart waste management, one future research direction is that smart bins have the ability to identify toxic metal waste, such as batteries. In addition, we can use cameras and computer vision algorithms to identify each type of waste, providing a more refined waste classification.  
For smart transportation, IoT systems play an important role in connected and autonomous vehicles, shared mobility systems (SMSs), and mobility-as-a-service (MaaS). SMSs are articulated transportation systems based on digital infrastructure that enable flexible sharing of vehicles and routes, helping to optimize resource utilization and facilitate collaboration across the mobility chain [116]. The IoT can connect autonomous vehicles and construct SMSs. Each vehicle can have GPS sensors to upload its position data to the central cloud, which can optimize the route of the vehicles in the whole system. Other transportation modes, such as bicycles, buses, and ships, can also be added to the SMS, which can be further developed into MaaS. MaaS integrates multiple transportation modes into one service platform [117]. Therefore, the ultimate MaaS platform can combine different modes of transportation to help users plan the fastest route to their destination. By analyzing global data, MaaS can organize autonomous vehicles to pick up different passengers on efficient routes, increasing operational efficiency and reducing carbon emissions on the way to pick up passengers.  
Furthermore, it has been discovered that citizen engagement is a crucial factor in the development of smart communities, smart transportation, and disaster management. Therefore, we suggest that future research combines IoT and citizen engagement. Social media can be a useful way to access citizen engagement data.  
Last but not least, one of the important future research directions is that an IoT system can cover most aspects of smart cities, because some sensors, communication technologies and protocols, and data analyses and applications are universal. This is important because it can reduce costs and remain environmentally friendly. Figure 3 describes the framework of this universal IoT system, which covers most aspects of smart cities. Citizens can use this integrated IoT system through a mobile application.
# Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
## 3 System Model
#### Linear and Non-Linear EH Models
We assume that all of the harvested energy at both nodes S and R during the first time slot of \(T/3\) is consumed for the transmission of the signal \(x\) in the consecutive time slots of each \(T/3\) since there is no available energy buffer at the power-constrained nodes. Moreover, high input power is limited to the threshold power \(P_{ht_{i}}\). In thesecond time slot, the received signal at node R is  
\[y_{r}=\sqrt{P_{s}L_{sr}}g_{1}x+n_{r}. \tag{3}\]  
In the third time slot, R forwards the amplified version of the received signal in (3). The received signal at D is then given as  
\[y_{d}=\sqrt{\frac{P_{r}L_{rd}}{G}}g_{2}y_{r}+n_{d}, \tag{4}\]  
where \(G=\mathrm{E}\{|y_{r}|^{2}\}=P_{s}L_{sr}|g_{1}|^{2}+N_{0}\) is the normalization factor. Substituting (3) in (4), we have  
\[y_{d}=\sqrt{\frac{P_{s}P_{r}L_{sr}}{G}}g_{1}g_{2}x+\sqrt{\frac{P_{r}L_{rd}}{G}} g_{2}n_{r}+n_{d}. \tag{5}\]  
The received SNR at D is calculated from (5) as  
\[\gamma=\frac{XY}{X+Y+1}\leq\min(X,Y), \tag{6}\]  
where \(X=ZX^{\prime}\), \(Y=WY^{\prime}\) and we assume a tight upper bound in (6). For simplicity, we assume \(Z=P_{s}\), \(W=P_{r}\), \(X^{\prime}=L_{sr}|g_{1}|^{2}/N_{0}\) and \(Y^{\prime}=L_{rd}|g_{2}|^{2}/N_{0}\). In addition, \(Z\sim\Gamma(m_{z},\phi_{z})\) and \(W\sim\Gamma(m_{w},\phi_{w})\) are Gamma distributed r.v.s with parameters \(m_{z}\) and \(m_{w}\), respectively, and the scaling parameters are \(\phi_{z}=m_{z}/\bar{z}\) and \(\phi_{w}=m_{w}/\bar{w}\). Moreover, \(\bar{z}=\eta_{s}P_{T}L_{sd}\Omega_{h_{z}}\) and \(\bar{w}=\eta_{r}P_{T}L_{rt}\Omega_{h_{r}}\). Furthermore, \(X^{\prime}\) and \(Y^{\prime}\) are exponentially distributed as \(\exp(\bar{x}^{\prime})\) and \(\exp(\bar{y}^{\prime})\), where \(\bar{x}^{\prime}=L_{sr}\Omega_{g_{1}}/N_{0}\) and \(\bar{y}^{\prime}=L_{rd}\Omega_{g_{2}}/N_{0}\), respectively. In (6), using ([54], eq. 6-81), the conditioned CDF of \(\gamma\) is expressed as  
\[F_{\gamma}(\gamma|Z,W) = F_{X}(\gamma|Z)+F_{Y}(\gamma|W)-F_{X}(\gamma|Z)F_{Y}(\gamma|W), \tag{7}\]  
where  
\[F_{X}(\gamma|Z)=1-\exp(-\frac{\gamma}{Z\bar{x}^{\prime}}), \tag{8}\] \[F_{Y}(\gamma|W)=1-\exp(-\frac{\gamma}{W\bar{y}^{\prime}}). \tag{9}\]  
Substituting both (8) and (9) in (7) and simplifying, the conditioned CDF in (7) is obtained as  
\[F_{\gamma}(\gamma|Z,W)=1-\exp(-\gamma(\frac{1}{Z\bar{x}^{\prime}}+\frac{1}{W \bar{y}^{\prime}})). \tag{10}\]
###### Abstract  
Nitrogen-doped carbon materials derived from N-containing conducting polymer have attracted significant attention due to their special electrochemical properties in the past two decades. Novel nitrogen-enriched carbon nanofibers (NCFs) have been prepared by one-step carbonization of p-toluene sulfonic acid (P-TSA) doped polyaniline (PANI) nanofibers, which are successfully synthesized via the rapid mixing oxidative polymerization at room temperature. NCFs with diameters ranging from 100 nm to 150 nm possess a highly specific surface area of 915 m\({}^{2}\) g\({}^{-1}\) and a relatively rich nitrogen content of 7.59 at %. Electrochemical measurements demonstrate that NCFs have high specific capacitance (172 F g\({}^{-1}\), 2 mV s\({}^{-1}\)) and satisfactory cycling stability (89% capacitance retention after 5000 cycles). The outstanding properties affirm that NCFs can be promising candidates for supercapacitor electrode materials. Interestingly, the carbonization of PANI opens the possibility to tailor the morphology of resulting nitrogen-enriched carbon materials by controlling the reaction conditions of PANI synthesis.  
Keywords:polyaniline; organic sulfonic acid; supercapacitor; carbon materials; nitrogen-doped; microstructure +
Footnote †: journal: Applied Physics Letters
# Monitoring Perishable Commodities Using Cellular IoT:
## 3 Results and Discussion
### Preliminary Test Results  
The data were correctly recorded in the cloud during the preliminary testing stage inside the refrigerator and freezer, as seen in Figure 13. The system was subjected to these conditions for two weeks. The setpoint temperatures were 6 \({}^{\circ}\)C and \(-19\)\({}^{\circ}\)C. It is observed that the measured temperature data adequately match the setpoints of the units where the device was located. In addition, the results of the relative humidity and CO\({}_{2}\) concentration can also be observed, along with the battery voltage, which were the most relevant parameters in this test, and which present quite reasonable values. The increase in temperature between days 319 and 320 corresponds to when the datalogger was taken out of the freezer and placed in the refrigerator. It can be observed that the battery provided the device with sufficient autonomy for the two weeks, with a capacity of 3000 mAh. These results validate the datalogger's baseline capabilities for measuring essential environmental parameters in perishable goods storage and provide a foundation for further testing under dynamic and mobile conditions.
###### Abstract  
A global maximum power point tracking (GMPPT) process must be applied for detecting the position of the GMPP operating point in the minimum possible search time in order to maximize the energy production of a photovoltaic (PV) system when its PV array operates under partial shading conditions. This paper presents a novel GMPPT method which is based on the application of a machine-learning algorithm. Compared to the existing GMPPT techniques, the proposed method has the advantage that it does not require knowledge of the operational characteristics of the PV modules comprising the PV system, or the PV array structure. Additionally, due to its inherent learning capability, it is capable of detecting the GMPP in significantly fewer search steps and, therefore, it is suitable for employment in PV applications, where the shading pattern may change quickly (e.g., wearable PV systems, building-integrated PV systems etc.). The numerical results presented in the paper demonstrate that the time required for detecting the global MPP, when unknown partial shading patterns are applied, is reduced by 80.5%-98.3% by executing the proposed Q-learning-based GMPPT algorithm, compared to the convergence time required by a GMPPT process based on the particle swarm optimization (PSO) algorithm.  
machine learning; maximum power point tracking (MPPT); particle swarm optimization (PSO); photovoltaic systems; reinforcement learning; Q-learning +
Footnote †: journal: Applied Sciences
## 3 Results
### Findings from the PESTEL Analysis
#### 3.4.4 Technical Opportunities and Challenges  
There are numerous technical opportunities that emerge with the adoption of DES. For example, regional areas are supplied by radial power networks (one line in/out). This type of distribution system is vulnerable to disruption, a fault in the line can cause the system to fail [73]. The simplicity of the set-up also means it is easy to connect remote towns to operate autonomously from the main grid as a DES (also referred to as a microgrid) because there is only one line in each direction. It is important to work with the utility to ensure their support. As part of this type of installation, sufficient battery storage is required. Through stakeholder interviews, we understood that two days of battery storage would be very useful in areas that are regularly disconnected. Battery storage would match diesel back-up, in the village of Omeo in East Gippsland/Victoria, diesel back-up only lasts four days. The majority of solar PV and battery storage systems are commercial and can provide enough electricity to towns for 3 days. Such an example is the Men's Shed (a communal workshop space) at Bawley Point (6.48 kW) [74]. Another issue that requires attention is to understand more about the safety of batteries in wildfires, e.g., Tesla batteries can often withstand wildfires [75]. Codified regions often have their own set of network designs that create challenges--e.g., on days of high wildfire risk, the network needs to be de-energised, and many regions have underground service lines--this adds to the complexity of technical solutions [73; 76]. Access is a major constraint for mobile emergency energy systems, particularly in rural regions [73]. Ongoing maintenance of technology in rural regions is already challenging for the conventional grid [_ibid_], and may be as challenging to maintain locally for an Emergency DES.
# Automatic Defect Detection for Web Offset Printing Based on Machine Vision
## 2 Methodology
### Defect Detection Based on Image Subtraction  
As shown in Figure 1, the proposed defect detection algorithm consists of two parts: template construction and defect detection for a printed image. Unlike the traditional method, the constructed template images include a reference image, a bright image and a dark image. The traditional defect detection method is image subtraction, where the defective image is subtracted from the reference image, and then a fixed threshold is employed to determine whether a pixel is defective. However, using a fixed threshold may easily lead to false positive or false negative pixels, since the fluctuating range of intensity value is different with the pixel position. To solve this problem, two tolerance images, named bright image and dark image, are built with respect to the reference image. The bright image and the dark image give the maximum and minimum deviations of a pixel value, as well as in combination with the reference image serve as template images for further defect detection.  
Figure 9: Concave-convex shape feature.  
Let \(\mathrm{I}_{i}(x,y)\) (\(i=1,2,\cdots,n\)) denote defect-free samples, where \(n\) is the number of defect-free samples. The reference image \(\mathrm{I}_{a}(x,y)\) and the standard deviation \(\sigma(x,y)\) of defect-free samples are expressed as Equations (7) and (8).  
\[\mathrm{I}_{a}(x,y)=\frac{1}{n}\sum_{i=1}^{n}\mathrm{I}_{i}(x,y) \tag{7}\]  
\[\sigma(x,y)=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\mathrm{I}_{i}(x,y)-\mathrm{ I}_{a}(x,y)\right)^{2}} \tag{8}\]  
The bright image \(\mathrm{T}_{b}(x,y)\) and the dark image \(\mathrm{T}_{d}(x,y)\) can be defined as follows.  
\[\mathrm{T}_{b}(x,y)=\mathrm{I}_{a}(x,y)+\max(a,b\times\sigma(x,y)) \tag{9}\]  
\[\mathrm{T}_{d}(x,y)=\mathrm{I}_{a}(x,y)-\max(a,b\times\sigma(x,y)) \tag{10}\]  
where \(a\) and \(b\) are two empirical values that need to be determined by experimentation.  
Besides the deviation of pixel values, there is still slight distortion of image contour because of changes to the material of printed matter and machinery shake, which cause false defects at the edge of patterns. To avoid this problem, operations of gray dilation and gray erosion with a \(3\times 3\) structure element are implemented for the bright image and dark image, respectively. Figure 10 gives an illustration of the reference image, bright image and dark image.  
For defect detection, the continuous collected web offset image is first cut to the same size as the reference image by the algorithm in Section 2.1, and then is aligned with the reference image. In the next process, the detected image is compared with the bright image and the dark image pixel-by-pixel. If a pixel value of the detected image is not in the range the bright image and dark image, it is identified as a defect pixel. Finally, a Blob analysis algorithm is used to label the defect region.