esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Techniques for Protecting the Privacy of Web Users
## 7 Conclusions  
The use of privacy-protection tools to ensure users' privacy has been widely explored; however, there exists no comprehensive tool that results in a more efficient, user-friendly, and effective solution. In conclusion, the findings indicate that current PPTs are inefficient at maintaining a good balance between preventing tracking and allowing essential functionalities of websites. Several factors may contribute to this weakness. For instance, current PPTs still rely on black lists or pre-defined lists of URL patterns that block JavaScript execution for third-party services. In order for blacklists to be continuously updated and maintained, human effort is required. Further problem is that most PPTs fail to inspect obfuscated JavaScript programs efficiently, and thus allow obfuscated tracking Scripts to bypass successfully.  
This paper introduced a systematic comparison of the most popular privacy protection tools in terms of achieving the balance between allowing and blocking functional and tracking JavaScript programs. The results found that Ghostery has the highest percentage of allowing most functioning scripts at 87% with the lowest average error rate (AER) of 0.145. However, Ghostery needs to be configured manually to provide effective protection against third-party services, which is difficult and time-consuming for end-users. While simultaneously, NoScript achieves the highest percentage of blocking most tracking scripts at 87%, since it is the highest blocker of external third-party services.  
After that, we examined the speed of the browser to render the web page finding that, blocking third-party services through PPTs substantially reduced the time it takes to load a web page when compared with the original result (PPTs Off). For example, Ghosteryimproves the average load time by 36.2% faster than the baseline, while Privacy Badger reduces the load time by only 7.1%.  
We believe that our study can help researchers and developers gain a comprehensive background about PPTs so they can devise an efficient approach to improve the detection rate and get an optimal balance between allow-functioning and block-tracking JavaScript programs. Furthermore, our findings can guide users in deciding on a privacy tool that is appropriate to their needs.  
Conceptualization, M.B.; Methodology, M.B.; Software, M.B.; Validation, M.B.; Formal analysis, M.B.; Investigation, M.B. and M.F.; Resources, M.B.; Data curation, M.B.; Writing--original draft, M.B.; Writing--review & editing, M.F.; Visualization, M.B.; Supervision, M.F.; Project administration, M.F.; Funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
This work was funded by King Faisal University, Saudi Arabia [Project No. GRANT2,932].  
Not applicable.  
This work was supported through the Annual Funding track by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. GRANT2,932].  
The authors declare no conflict of interest.
## 2 DTPPF Fault Identification Method
### Method for Identifying Open-Circuit Fault of ECFR Thyristor  
When the thyristor on the secondary side of the ECFR is disconnected, the impedance of the positive and negative half cycles is unequal, resulting in an even harmonics current in the DTPPF phase current.  
The total harmonic distortion caused by the even harmonics current during an open-circuit fault of the ECFR thyristor is  
\[\mathrm{THD_{act}}=\frac{\sqrt{\sum\limits_{k=0}^{q}I_{2(2k)}^{2}}}{I_{2\mathrm{RMS }(x)}}\times 100 \tag{21}\]  
where \(q\) is a constant and takes the values \(1,2,\,\cdots\).  
The per-unit value of the total harmonic distortion caused by the even harmonics current is  
\[K_{eh}=\frac{\mathrm{THD_{act}}}{\mathrm{THD_{avg}}} \tag{22}\]  
where \(\mathrm{THD_{avg}}\) is the total harmonic distortion caused by the even harmonics current during normal operation of the DTPPF.  
When \(K_{eh}>\mathrm{THD_{avg}}\), this indicates an open-circuit fault in the ECFR thyristor. Otherwise, the thyristor is operating normally. On this basis, together with the magnitude of the \(K_{eh}\), open-circuit faults in the thyristor can be identified.
## 2 Materials and Methods
### NMR Analysis  
All PET oligomers have been individually dissolved in a mixture of 600 \(\upmu\)L of deuterated chloroform and TFA (8:1, _v/v_), which was then shaken in a vortex and transferred into 5 mm NMR tubes. The experiments were performed on a Bruker (Rheinstetten, Germany) Avance 600 (nominal frequency 600.13 MHz) equipped with a 5 mm cryo-probe. The spectra were recorded at 298 K using a 90\({}^{\circ}\) flip angle, with an acquisition time of 3.0 s (64k data points) and a total recycling time of 4.0 s. A spectral width of 20 ppm with 256 scans and no sample rotation in the DQD acquisition mode were applied. Prior to Fourier transformation a 0.5 Hz line-broadening factor was applied, and all spectra were phase- and baseline-corrected using Bruker Topspin 3.2 software. Chemical shifts (\(\delta\)) for \({}^{1}\)H and \({}^{13}\)C NMR spectra are reported in parts per million (ppm) relative to the internal residual solvent signal of CDCl\({}_{3}\)-d: 7.2 ppm for \({}^{1}\)H and 77 ppm for \({}^{13}\)C APT.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
###### Abstract  
Nowadays, wireless sensor network which consists of numerous tiny sensors has been widely used. One of the major challenges in such networks is how to cover the sensing area effectively and maintain longer network lifetime with limited energy simultaneously. In this paper, we study hybrid sensor network which contains both static and mobile sensors. We divide monitoring area into Delaunay Triangulation (DT) by using of Delaunay theory, estimate static sensors coverage holes, calculate the number of assistant mobile sensors and then work out the positions of assisted mobile nodes in each triangle. Next, mobile sensors will move to heal the coverage holes. Compared with the similarity methods, the algorithm HCHA we proposed is simpler, the advantages of our algorithm mainly represents in the following aspects. Firstly, it is relatively simple to estimate coverage hole based on Delaunay in our proposed algorithm. Secondly, we figure out the quantitative number range of assisted sensors those need to heal the coverage holes. Thirdly, we come up with a kind of deployment rule of assisted sensors.  
coverage; estimation; healing; hybrid sensor networks +
Footnote †: journal: Journal of Sensors
# A Cross-Chain-Based Access Control Framework for Cloud Environment
## 4 The Framework's Functionality
### Security Features  
We now explain how the proposed architecture provides the following security requirements:  
* Confidentiality: For the confidentiality of cloud resources and assets, we integrated a fine-grained attribute-based access control on the controlling node as an entry point to the cloud through cross-chain. It verifies the validity of the received access transaction and restricts access to only authenticated users with valid transaction attributes. For the confidentiality of the transactions carrying the attributes, each cloud  
Figure 3: Flow diagram of a cloud client transaction.  
user transaction is encrypted (as in step 4 of transaction processing) and broadcast over a cross-chain network. The cloud users access the sought resources through a signed URL with the CSC's private key using Google API (Oauth 2). To maintain the confidentiality, this URL is generated securely by the controlling node in regard to the actions authorized to the users on the cloud resources, with an expiration time specific to the cloud resources' usage.
* Blockchain security: In order to protect our network from malicious attacks, we have adopted the IBC protocol. This protocol takes advantage of the existing consensus mechanism of the nodes in the network, in our case, Tendermint, using a byzantine fault tolerance that handles a 1/3 of the network nodes being malicious or faulty and a set of validators to agree on a block before making it valid. The PoS mechanism also ensures that no entity controls the network, as it is challenging to stack all the tokens. The cross-chain communication between the sending and receiving chains uses the Merkle roots and commitment, which are default features of the IBC protocol. The access transaction originating from the sending chain is recorded in a state Merkle tree. The control node (receiving chain) verifies the existence and integrity of the received transaction using a Merkle proof generated by the sending chain, while the commitment proofs guarantee that the transaction attributes are validated and committed in the sending chain's state. This combination of cryptographic proofs ensures data integrity and authenticity.
* Privacy: In this framework, cloud users are identified using unique account addresses derived from their generated public key. This approach enhances the privacy of the user's personal information. After a user signs a transaction in plain text, it undergoes encryption. Validators can verify the transparent parts of the transaction, including the signature, the cloud user's public key, and the transaction hash, while the transaction's metadata and access attributes remain encrypted.
## 4 Types of Catalysts Used in Transesterification
### Nano-Catalysts
#### 4.5.1 Clay Derived Nano-Catalysts  
Nowadays, nano-sized clay catalysts are being considered by researchers. A clay catalyst can efficiently catalyze hydrogenation, acylation, esterification, transesterification, epoxidation, alkylation and cyclic condensation. Clay and its minerals (Phyllosilicates) vary in composition and have different particle sizes. Inorganic clay as a support catalyst is gaining more significance, owning to its ecofriendly nature along with its abundance and economic feasibility. Phyllosilicates, (Al\({}_{2}\)-xMgx) Si\({}_{4}\)O\({}_{10}\)(OH)\({}_{2}\)(M.nH\({}_{2}\)O) having layered structure with tetrahedral and octahedral sheets (2:1) and form via volcanic ashes or weathering of soil and rocks [90]. The cation exchange capacity of clay enhances, owing to isomorphous substitution in sheets' interlayers. Hence, this make them promising and potential candiadates for heterogeneous nano-catalysis in the biodiesel synthesis process [90; 91]. They are modified by various techniques such as intercalated catalysts, pillared interlayer clays, hybrids and composites, clay supported catalysts, ion exchanged clays, hierarchically structured clays, acid activated clays and layered double hydroxides. These techniques have been implemented in order to obtain a synergetic effect: basic-acidic in  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Nano-** \\ **Catalysts** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Synthesis** \\ **Technique** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Size of** \\ **Nanoparticles** \\ **(nm)** \\ \end{tabular} } & \multicolumn{6}{c}{**Operating Parameters**} & \multirow{2}{*}{\
\begin{tabular}{c} **Yield of** \\ **Total** \\ **Constr. (\%)** \\ \end{tabular} } \\  & & & & **Ofi to** & & & & & **Biodiesel** \\  & & & **Molar** & **Constr. (\%)** & **Temp.** & **(\({}^{\circ}\)C)** & **(H.)** & **(\%)** & **(\%)** & **Reference** \\ \hline \hline \multicolumn{10}{c}{**Calcium Oxide Catalyst**} \\ \hline CuO-CaO &
\begin{tabular}{c} Co- \\ Precipitation \\ **Calcination-** \\ **Hex  
catalyst to efficiently convert non-edible feedstock to biofuel [90]. Similarly to montmorillonite, sepiolite, kaolinite and bentonite are the best candidates to be used as catalysts, since they have a high cation exchange capacity and, hence, more adsorption ability beholden to large surface area and great porosity. [PERSON] et al. (2019) achieved an 85% biodiesel yield from non-edible seed oil of _Prunus cerasoides_ by Cd-Mn modified montmorillonite clay catalysts [92]. Similarly, wild non-edible seed oil of _Celastrus paniculatus_ has been converted into biodiesel under optimized conditions (1:12 methanol:oil, 120 \({}^{\circ}\)C temp., 3.5% catalyst loading and 3 h reaction time) and 89.42% biodiesel yield was achieved by novel trimetallic montmorillonite clay catalyst [93]. There are studies that cover utilization of clay catalysts for transesterification reaction are discussed in Table 8.
## 3 VirtFogSim: A View of the Simulated Formal Models
### Supported Task Allocation Strategies and Adaptive Resource Allocation  
In this subsection, we describe the supported task allocation strategies provided by the _VirtFogSim_ and listed in Table 2. We begin with the _parallel Only-Task Allocation Strategy (_O_TAS_par_) function that acts as follows:  
1. it performs task allocation by implementing (in a parallel way) a genetic algorithm. For this purpose, _O_TAS_par_ calls the _Crossover_ and _Mutation_ functions over a randomly-generated population of \(\{0,1,2\}\)-ternary task allocation vectors of size \(PS\);
2. it evaluates the energy of each tested ternary allocation vector by computing the corresponding energies: \(\mathcal{E}_{M}\), \(\mathcal{E}_{F}\), \(\mathcal{E}_{C}\), \(\mathcal{E}_{WIFI}\), \(\mathcal{E}_{WD}\), and \(\mathcal{E}_{CELL}\) under the static (e.g., not optimized) maximal resource allocation vector: \(\overrightarrow{RS}^{MAX}=\left[f^{MAX}_{M},f^{MAX}_{F},f^{MAX}_{C},R^{MAX}_{ U},R^{MAX}_{D},B^{MAX}_{U},B^{MAX}_{D},R_{0}\right]\) (bit/s). For this purpose, _O_TAS_par_ not longer calls the _RAP_p_ function, but calls the _evaluatestaticenergy_p_ function. The _O_TAS_par_ outputs are:  
1. the \(V\)-tuple \((0,1,2)\)-ternary minimum-energy task allocation vector: \(x\_OTAS\), which is computed by applying the genetic algorithm run by _O_TAS_par_ under \(\overrightarrow{RS}^{MAX}\);
2. the corresponding total energy: \(\mathcal{E}_{OTAS}\) (Joule) and network energy: \(E^{NET}_{OTAS}\) (Joule) consumed by \(\vec{x}_{OTAS}\) under the (aforementioned) fixed maximal resource allocation vector \(\overrightarrow{RS}^{MAX}\). The function _O_TAS_par_ assumes that all the global variables of parallel _VirtFogSim_ are already setup. It also utilizes the following main set of local variables: _Popmatrix_, _Childlist_, _Mutationlist_, and _Candidatelist_, all of dimensions \(PS\times(V+1)\). Algorithm 1 reports a pseudo-code of the \(O\_TAS\_par\) function. Due to the utilization of the parfor-cycle, the resulting asymptotic computational complexity of the \(O\_TAS\_par\) function scales up as: \(\mathcal{O}\left(\left(PS\times G_{MAX}\right)\right)/n_{core}\)), where:  
1. \(PS\) is the population size of the checked task allocation vectors;
2. \(G_{MAX}\) is the number of iterations (that is, the number of generations) run by the genetic algorithm implemented by \(O\_TAS\_par\); and,
3. \(n_{core}\geq 1\) is the number of available parallel cores that support the execution of \(VirtFogSim\). ```
function:\(\left[\vec{x}_{OTAS},\,\mathcal{E}_{OTAS},\,\mathcal{E}_{OTAS}^{NET}\right]=O\_TAS\_par \left(PS,\,\mathcal{C}F,G_{MAX},MN\right)\). Output:\(\left(1\times V\right)\) ternary vector of the best task allocation \(\vec{x}_{OTAS}\in\left\{0,1,2\right\}^{V}\) under the fixed maximal resource allocation vector; scalar total energy \(\mathcal{E}_{OTAS}\) (Joule) and network energy \(\mathcal{E}_{OTAS}^{NET}\) (Joule) consumed by \(\vec{x}_{OTAS}\) under the fixed maximal allocation vector.
## 3 Implementation of Dynamic Balance Method
### Corrected Mass Distribution and Residual Unbalance Treatment  
Due to the limitation of the number of stud holes on the counterweight plate, the length and quality of the stud, the specific amplitude and phase counterweight vector cannot be satisfied. Therefore, the counterweight vector can be decomposed to obtain the appropriate weight on the existing stud hole.  
In addition, there are various errors in the dynamic balance correction process. For example, due to the assembly error caused by the installation of components such as rotors and sensors, the marker cannot be aligned with the center of the starting position stud hole, resulting in a phase error in which the zero phase of the counterweight vector and the displacement vector is not uniform. Ultimately, it causes an error between the actual counterweight vector and the theoretically calculated value. There must be residual imbalance in the system after one dynamic balance correction. The influence coefficient can be calculated again and the rotor can be corrected for dynamic balance several times until the accuracy requirements are met.  
Figure 7: Schematic diagram of counterweight plate and fiber optic sensor.
## 3 Analysis of Channel
### Doppler Effect  
The Doppler effect can be caused by the relative movement between the receiving end and the transmitting end or the movement of objects in the channel. The change of the channel with time, that is, the time-varying characteristics of the channel, is called the Doppler effect. If the receiver moves away from the transmitter at a speed \(v\), the distance \(l\) between the two will continue to increase. Then:  
\[E(t)=E_{0}\cdot\cos\left(2\pi t\left(f_{c}-\frac{v}{\lambda}\right)-k_{0}l_{0}\right) \tag{13}\]  
If the transmitter movement direction is consistent with the wave propagation direction, the Doppler effect is:  
\[v=-\frac{v}{\lambda}=-f_{c}\cdot\frac{v}{c_{0}} \tag{14}\]  
Otherwise, the Doppler effect is:  
\[v=-\frac{v}{\lambda}\cos(\gamma)=-f_{c}\cdot\frac{v}{c_{0}}\cos(\gamma) \tag{15}\]  
where \(v\cos(\gamma)\) is the velocity component of the wave propagation direction.  
In mobile communications, the Doppler effect is the main parameter that distinguishes other communication modes. Fading and Doppler shift cause a large number of consecutive bits to be destroyed. This leads to a significant drop in system performance, resulting in time-selective fading of the channel.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 3 The MASU Framework
### Data Retrieval Mechanism
#### 3.6.3 Data Dissemination Manager  
As shown in Figure 7, the Data Dissemination Manager (DDM) establishes three data dissemination mechanisms: Broadcast, Point-to-Point and Server-Mediated.  
The broadcast and point-to-point mechanisms offer different methods to create peer-to-peer proximity networks amongst devices. It allows the MASU units to share information directly among them, without depending on any centralized server. This fact opens up the possibility to integrate the MASU framework with IoT, allowing that the units could interact with nearby networked objects and  
Figure 7: Example scenario of data dissemination.  
sensors. In the case of broadcast data dissemination, the data sent by a unit can be received by all the others. On the other hand, the point-to-point mechanism only allow data transfer between pairs of units and therefore only the particular unit that was specified as destination of the data can receive and use it.  
Finally, the Server-Mediated data dissemination allows communication between units that are not in the same local network. This mechanism enables data transfer between two independent groups of units connected to different local networks but reachable through the Internet. For instance, this two groups could be units that are participating in two different collaborative sensing activities. Furthermore, the MASU framework can decide to make use of this server to perform some resource intensive aggregation, processing or classification tasks to optimize the use of local hardware resources of the units.
## 1 Introduction  
Usually, for each research field it is possible to retrieve a very large collection of related papers that define the state-of-art of an argument, often the relationship between the searched argument and the retrieved papers have a different degree of correlation dependent on the content of the retrieved articles.  
Organising the retrieved collection of papers in relation of theirs content and degree of correlation is not a trivial task.  
In literature, many different approaches to document classification have been defined [1]. Some of these approaches require a training phase (supervised approaches) and need a set of annotated documents to train the system [2]; some others instead do not require the training phase and attempt to classify documents in an unsupervised way (unsupervised approaches) [3].  
Although several unsupervised systems have been defined to classify different types of documents, all these systems are unable to assign a meaningful label to these documents.  
The traditional unsupervised approaches, like Latent Dirichlet Allocation (LDA from now) [4], realise a document clusterization and extract a list of topics composed by a list of more relevant (frequency-based approach) terms and their related weights.  
The supervised classification methods, instead, make possible the identification of a predetermined class or label related to a document but need to be trained by a set of labelled documents.  
The key idea of this work is to adopt a hybrid strategy to achieve best performance in document classification exploiting both supervised, semi-supervised and unsupervised techniques in the same approach.  
To reach this aim we have defined and implemented the Scientific Publication Classifier (SPUCL from now) system, a semi-supervised document classifier able to assign a human-readable label to classified documents. SPUCL aims to assess critically the scientific literature.  
We tested the SPUCL system by adopting it in a practical research task: classifying papers reporting application of analytical DNA methods to the authenticity testing of complex food products.  
Our approach performs unsupervised pre-training by using LDA, followed by a fine-tuning step realised through Word2 Vec and a labelling step performed with the application of the cosine similarity metric.  
SPUCL exploits the powerful of Neural Networks that have found new interest both for computing power of current electronic systems and for new technologies [5; 6; 7].  
The following sections will be described the background of document classification from statistical methods to deep learning techniques, the architecture of SPUCL classifier. Following we will present an application of SPUCL system, in this experiment SPUCL classifies a set of 119 scientific papers in the food analytic research field, the discussion of the achieved results and the conclusions.