esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Fuzzy c-Means  
A single truck may exhibit specific operating patterns over a multi-day period, and to retrieve these hidden and repeated patterns efficiently, the FCM clustering algorithm is used. FCM is a typical divisional clustering algorithm proposed by [PERSON] [24], and is mainly used to classify data distributed in a multi-dimensional data space into a certain number of classes. In the general process, the sample set \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) is divided into \(C\) fuzzy sets according to certain criteria, where \(C\) is a pre-specified number of categories. Next, the clustering centers of each category are determined to minimize the objective function of the non-similarity index. The clustering results are displayed in the clustering center matrix \(K\) and the clustering effect graph [25; 26]. The algorithm aims to minimize the weighted intra-class sum of squares shown in Equation (1). This study utilizes the sum of squared errors (SSE) as a metric to evaluate clustering performance. As the number of clusters \(k\) increases, the intra-cluster data cohesion improves, leading to a continuous decrease in the SSE value. When \(k\) is less than the true number of clusters, increasing \(k\) significantly enhances the cohesion, resulting in a sharp decline in SSE. However, once \(k\) reaches the true number of clusters, the rate of SSE reduction slows considerably and gradually stabilizes with further increases in \(k\).  
\[J(X,u,K)=\sum\limits_{i=1}^{C}\sum\limits_{j=1}^{n}{{{{(u_{ij})}^{m}}\left\|{{{ \chi_{j}}-{k_{i}}}}\right\|}^{2}} \tag{1}\]  
where \(X\) denotes the sample set, \(n\) denotes the total number of samples, and \(C\) denotes the number of categories and clustering centers. \(u_{i,j}\) denotes the \(i\)-th row and \(j\)-th column in the matrix \(U\), \(\left\|{{{\chi_{j}}-{k_{i}}}}\right\|\) is the Euclidean distance between the \(i\)-th cluster center and the \(j\)-th sample, and \(j\) is the weighted index with an optimal range of 1.5 to 2.5 (chosen as 2 in this study).  
The requirement to minimize the objective function by constructing Lagrange multipliers and finding the partial derivatives of all input parameters is represented by Equations (2) and (3).  
\[u_{i,j}=\frac{1}{{{(\sum\limits_{k=1}^{C}{{\frac{{{d_{i,j}}}}{{{d_{k,j}}}}}) \frac{2}{m}}}}} \tag{2}\]  
\[k=\frac{{{\sum\limits_{k=1}^{n}{{{{(u_{i,j})}^{m}}{x_{j}}}}}}}{{{\sum\limits_{ j=1}^{n}{{{(u_{i,j})}^{m}}}}}} \tag{3}\]  
where \(u_{i,j}\) denotes the fuzzy affiliation matrix and \(k\) denotes the fuzzy clustering center matrix. \(d_{i,j}\) denotes the Euclidean distance between the \(j\)-th data point and the centroid of the \(i\)-th cluster. It measures how far the data point is from a specific cluster center. \(d_{k,j}\) denotes the Euclidean distance between the \(j\)-th data point and the centroid of the \(k\)-th cluster, where \(k\) varies across all clusters (from 1 to n).
# A MAP Overhead Aware Two-Dimensional OFDMA Burst Construction Algorithm
###### Abstract  
Conventional orthogonal frequency division multiple access (OFDMA) burst construction methods can only support limited numbers of connections due to the map overhead and corresponding limitations in the numbers of orthogonal resources blocks, which limits the capacity of current 4G and the following 5 th generation (5G) networks. This study therefore provides a novel OFDMA burst construction algorithm and enhanced burst indexing aware algorithm (EHA), which try to maximize the throughput while considering the subchannel diversity and optimizing burst indexing issues. The EHA not only allocates the subchannels with the best channel quality for each burst, but also groups the bursts to alleviate the MAP overhead. Simulation results showed that the EHA yields two times the throughput that has been achieved using previous algorithms under a heavy load. Two contributions of the EHA are: (1) the overhead of burst indexing decreases because massive numbers of connections can be accommodated by one burst; and (2) the overall throughput increases due to that one connection with large data transferring requirements can be split and distributed into several bursts and placed on the subchannels with good channel quality to adopt better modulation coding scheme (MCS), if the saved bandwidth in this burst construction is more than the increased overhead of burst indexing.  
OFDMA; burst construction; map overhead +
Footnote †: journal: Applied Informatics
## 2 Results and Discussion  
### Quantitative Profile Sensory Analysis  
In order to provide a comprehensive understanding of the aromatic profile of all twelve model mead products, quantitative profile sensory analysis was carried out by a qualified team of experts. The results of the sensory evaluation, categorized by the type of honey used in the preparation, are shown in Figures 1-3.  
It has been noted that the general odor intensity of meads prepared from the same types of honey was very similar in each group. Evident differences can be noted between meads from different honey sources not only in overall aroma intensity but also in individual descriptors. All buckwheat-based meads (BNS, BNSM, BNY, and BBY) were noted for the highest general odor intensity (from 7.5 to 8.6), followed by tilia-based meads (TNS, TNSM, TNY, and TBY, score range from 3.8 to 5.5). The lowest values for general odor intensity were noted for acacia-based meads (ANS, ANSM, ANY, and ABY) with scores ranging from 2.2 to 2.8. Based on the profile sensory analysis, the buckwheat mead aroma was dominated by such attributes as honey (maximum score 8.6 for BNY), malty (maximum score 6.2 for BBY), rum (maximum score 6.1 for BNSM), fermented (maximum score 5.5 for BNS), and alcoholic (maximum score 5.5 for BNY). Floral and yeasts were slightly less perceptible (maximum scores of 3.0 and 4.0 for BBY and BNS, respectively). On the other hand, the sensation of floral aroma was more pronounced in tilia and acacia meads and exceeded that of rum, which was dominant in buckwheat mead. Considering the effect of word boiling on the final aroma of the meads, it has been seen that boiling increased the honey and malty sensation, while it decreased the alcoholic aroma sensation. This effect has been observed in buckwheat and linden meads but not so much in acacia. Meads prepared from acacia honey showed greater variability in the aroma caused by the different types of fermentation. As shown in Figure 1, spontaneous fermentation, both with and without _G. geotrichum_, gave higher scores in honey, malty, and yeasty descriptors. In general, all meads with spontaneous fermentation were characterized by stronger yeasty, fermented, and, in the case of tilia mead, alcoholic aromas. The addition of _G. geotrichum_ during fermentation noticeably increased the floral and rum sensation in acacia mead, floral and fermented in buckwheat mead, and rum, alcoholic, and honey in tilia meads.
## 4 Results
### Knowledge Base Distribution and Robustness Tests
#### 4.2.2 Robustness of the Protocol and Application Layer  
The current version of the protocol, version 0.3, was tested in more than 130,000 cycles of awake and sleep, with no errors in the sensors or the base station, even with the host forced off in or with the sensors in motion. This number of cycles represents an average of 15 days of running time with sleep periods around ten seconds.
## 3 Experimental Section
### FCS Data Analysis  
Fluorescence autocorrelation data was obtained from SPCM64 software (version 9.82, Becker&Hickl GmbH, Berlin, Germany), and raw data were extracted from burst analyser software (burst analyser 2, Becker&Hickl, Berlin, Germany). The time tagged photon arrival data were time binned using different binning settings. The time-binned data were subject to autocorrelation by utilizing a customized algorithm, inspired by others [37], implemented in MATLAB (MathWorks Inc., Natick, MA, USA), see further Supplementary Material. The acquired autocorrelation curves were fitted using two different models. The  
Figure 2: Schematic diagram of the experimental setup enabling multiphoton laser scanning microscopy (MPM)-FCS.  
primary model utilized in this work was based on the original model [13] as introduced in the theory section (see Equation (2)), and here referred to as \(\mathrm{G(\tau)}_{\mathrm{simple}}\)  
\[(\tau)_{\mathrm{simple}}=\frac{\mathrm{G(0)}}{1+\frac{\mathrm{\tau}}{\tau_{ \mathrm{D}}}} \tag{7}\]  
In addition, according to previous literature [22] an extended model should be used for FCS in confocal setting. This model is here referred to as \(\mathrm{G(\tau)}_{\mathrm{ext~{}fit}}\) corresponding to:  
\[\mathrm{G(\tau)}_{\mathrm{ext~{}fit}}=\frac{\mathrm{G(0)}}{1+\frac{\mathrm{ \tau}}{\tau_{\mathrm{D}}}\sqrt{1+\mathrm{a}^{2}\Big{(}\frac{\mathrm{\tau}}{ \tau_{\mathrm{D}}}\Big{)}}} \tag{8}\]  
where the parameter a is introduced as a dimensional parameter defining the dimension of excitation volume.
## 1 Introduction  
Over the past few years, the quality of direct and indirect restorations has improved in terms of the adhesion strength, longevity and composition of the resin matrix and filler [1].  
However, like all dental materials, the composites undergo deterioration processes as a consequence of mechanical (cyclic fatigue and wear) or thermal stresses and chemical degradation (enzymatic, hydrolytic and acidic) [2; 3].  
Fractures, marginal bacterial infiltration, dentin treatments, teeth or restoration color changes, indirect restorations or endodontic post detachment can also compromise the result and require a makeover [4; 5; 6; 7; 8; 9].  
In these cases, complete replacement can be a long and expensive procedure with the possibility of further healthy dental tissue loss and an increased risk of pulp exposure. Therefore, repairing the restoration by preserving parts or rebonding an indirect restoration can be an advantageous alternative as these techniques are less invasive and allow the prolongation of the efficiency of conservative therapies over time [5; 10; 11; 12; 13; 14; 15; 16].  
The prognosis of a repair and maintenance treatment depends on the adhesion strength achieved between the old restoration and the new composite material layer [10].  
In clinical practice, the adhesion techniques and mechanisms at the dental tissues-composite or composite-composite interfaces are different [17].  
The adhesion strength between two composite surfaces depends on their chemical composition and characteristics such as the roughness, conditioning procedures and ability to become wet of the polymerized surface [10].  
Furthermore, contrary to what happens in the composite layering technique, during reparation, the material integration process is hindered by the relative lack of unpolymerized monomers [18; 19; 20].  
After intraoral photopolymerization, the conversion rate from monomer to polymer is between 45% and 70% because not all monomers participate in the polymerization and the remaining monomers are available for new adhesion [21; 22; 23].  
Considering that the number of unsaturated double bonds in monomers decrease over time, the adhesion strength reduces by between 25% and 80%, and consequently the effectiveness of the repair process is time-related [18; 19; 20].  
The adhesion of new composite to an old restoration is achieved with a new bond established by residual monomers and micromechanical retention that exploits the surface irregularities of the old restoration.  
In order to improve this type of adhesion, various surface treatments have been described in the literature such as surface roughening with diamond burs of different granulometry, sandblasting with aluminum oxide or silica sand, acid etching and the application of hydrogen peroxide or silane.  
A systematic review of the data still appears insufficient to indicate the best method for repairing Bis-GMA-based resins [11; 14; 18; 21; 22]. The aim of this study is to analyze the effects of chemical and mechanical surface treatments and their combination on the roughness of a supra-nano composite resin surface.
## 2 Results
### Large-Signal Investigation for PA Design
#### 2.2.2 Comparison with Other Configurations
It can be seen in the table that at an IMD3 level of \(-35\) dBc, the proposed 3-stack configuration has highest efficiency of \(35\%\) compared with that of the other configurations, by controlling the gate bias voltages. This verifies the advantage of the proposed configuration in term of both efficiency and linearity improvement. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Configurations** & **IMD3 Level** & **PAE** \\ \hline Single-stage & \(-35\) dBc & X \\ Conventional cascode & \(-35\) dBc & \(4.5\%\) \\ Independently biased cascode & \(-35\) dBc & \(22.8\%\) \\ Conventional 3-stack & \(-35\) dBc & \(25\%\) \\ Independently biased 3-stack & \(-35\) dBc & \(35\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of linearity and efficiency performance. Figure 14: Efficiency characteristic. Figure 15: Linearity (IMD3) characteristic. Finally, to demonstrate the advantage of such a proposed independently biased 3-stack circuit structure in terms of bandwidth, the PAE bandwidths of the 5 circuit configurations are compared to each other, as shown in Figure 16 below. It can be clearly seen in the figure that the proposed 3-stack configuration exhibits superior PAE bandwidth over the others. The PAEs of the single-stage and independently biased cascode configurations drop significantly at both lower and upper frequency regions, while the PAEs of the conventional cascode and conventional 3-stack configurations are severely reduced in both the regions compared with other ones. The proposed configuration can maintain high efficiency in a wide frequency range compared with the other methods.
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 4 The Proposed Model (WFSE)
### Syllable-Aware Language Model  
We adopt [PERSON]'s [25] version of the language model that uses finite output vocabulary. This enables us to properly compare language models using the perplexity values because they provide similar event spaces. Figure 2 illustrates the architecture of our language model that employs the LSTM, a variant of the recurrent neural network language model of [PERSON] et al. [3]. The language model uses the generated word embeddings from syllable embeddings to make predictions of a target word, hence the name syllable-aware. Given a sequence of words \(w_{1:t}=w_{1},...,w_{t}\) and \(w_{t}\in V\) where \(V\) is a finite vocabulary set, we compute:  
\[P(w_{1},..w_{t})=\prod_{t=1}^{T}P(y_{t}|w_{1:t-1}) \tag{9}\]  
where \(y_{t}=w_{t}\) if \(w_{t}\) is in the output vocabulary and \(y_{t}=\) UNK otherwise. We report the perplexity, that is, the geometry average as:  
\[Perplexity=\frac{1}{\prod_{t=1}^{T}P(y_{t}|w_{1:t-1})} \tag{10}\]  
The training in the language model involves minimizing the negative log-likelihood (NLL) of the sequence, which is given by:  
\[NLL=-\sum_{t=1}^{T}\log P(y_{t}|w_{1:t-1}) \tag{11}\]  
In particular, with the LSTM, once we have generated the word embeddings \(w_{1:t}\) for a sequence of words, we produce a sequence of states \(h_{1:t}\) and then predict the next word accordingly using the following equations:  
\[h_{t}=LSTM(w_{t},h_{t-1}) \tag{12}\]  
\[P(y_{t+1}|y_{1:t})=Softmax(\mathbf{V}^{T}.h_{t}) \tag{13}\]  
where \(w_{t}\) is the word representation, \(h_{t-1}\) is the previous state, \(y_{t+1}\) is the predicted target word, \(y_{1:t}\) are the context words and \(\mathbf{V}\) is the weight matrix.  
Figure 2: The syllable-aware language model architecture. The model demonstrates prediction of the next word _asubuhi_ from its context.  
Note that the softmax is a d \(\times\) V table, which encodes the likelihood of every word type in a given context and V is from a finite output vocabulary. The LSTM uses \(x_{t}=w_{t}\), \(h_{t-1}\), \(c_{t-1}\) to determine \(h_{t}\) as follows:  
\[\begin{split} i_{t}&=\sigma(\textbf{W}^{i}x_{t}+ \textbf{U}^{i}h_{t-1}+b^{i})\\ f_{t}&=\sigma(\textbf{W}^{f}x_{t}+\textbf{U}^{f}h_{t -1}+b^{f})\\ o_{t}&=\sigma(\textbf{W}^{o}x_{t}+\textbf{U}^{o}h_{t -1}+b^{o})\\ g_{t}&=tanh(\textbf{W}^{s}x_{t}+\textbf{U}^{s}h_{t -1}+b^{s})\\ c_{t}&=f_{t}\circ c_{t-1}+i_{t}\circ g_{t}\\ h_{t}&=o_{t}\circ tanh(c_{t})\end{split} \tag{14}\]  
where \(\sigma\), \(tanh\) and \(\circ\) are element-wise sigmoid, hyperbolic tangent and multiplication functions, respectively. \(i_{t}\), \(f_{t}\) and \(o_{t}\) are the input, forget and output gates, respectively.
# Air Quality Assessment of a School in an Industrialized Area of Southern Italy
### Italian Studies on School Air Quality  
In Italy, few studies have been conducted on the indoor air quality of schools, as it appears in a study promoted and carried out by the Tuscan regional authority [10]. Among the few studies, a VOC indoor/outdoor screening campaign performed in a high environmental risk site of South Italy, Taranto city, highlighted a significant indoor contribution from terpenes and 2-butoxyethanol [11]. Even if the school was located close to an industrial area, outdoor VOC concentrations were low in comparison to studies conducted in similar areas of the world. The use of high time resolution monitoring equipment facilitated the identification of VOC emission patterns of possible indoor sources and confirmed cleaning activities, occurring after the pupils leave, as one of the sources [11]. During an indoor VOC monitoring campaign performed in schools in a suburban area of the Po plain in Italy [12], it was highlighted that the classrooms were characterized by low pollutant concentrations, even if there was a contribution from indoor contamination for most of the investigated components. Daily trends confirm that VOCs have mainly an indoor origin [12]. Diffusion passive samplers have been widely used in indoor/outdoor VOC monitoring because of the absence of a power supply, their easy handling, and analytical robustness [4; 11; 12]. However, continuous and real-time detectors are contemporarily employed in order to obtain high-resolution data that allows tracking of the activation of sources during the monitoring period [11; 12].  
The present study focuses on the city of Galatina (Apulia Region, Italy). The area hosts a large cemetery and other small industries. For several decades, elevated male mortality rates for lung cancer in the Lecce province, compared with the national values, have been reported. Recently, a cluster analysis of lung cancer incidence was detected in the hinterland of the province, including the area of Galatina. Another study [13] showed that children aged 6-8 years living in the area had a higher frequency of micronucleated cells in oral mucosa than children living in other areas of the Lecce Province not included in the cluster area. The aim of this work is to assess the indoor air quality in a school building located in Galatina (Lecce) using both passive cartridges and active systems for VOC monitoring.
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 2 Background  
### Laplacian Mesh Smoothing  
#### 2.1.1 Ordinary Laplacian Mesh Smoothing  
Laplacian mesh smoothing is one of the most widely used smoothing methods. The core idea of Laplacian mesh smoothing is straightforward. First, the first-order domain [29] of every internal node is determined in the mesh. Second, the coordinate of each internal node is updated iteratively to the center of mass of its first-order neighboring nodes until the result converges. Laplacian mesh smoothing does not change the topology of the mesh; and the iterative calculation of nodes in the algorithm is easy to parallelize; thus, it is easy to exploit in practical applications. Figure 1 briefly compares a tetrahedral mesh before and after Laplacian smoothing.
# A Blockchain Network Admission Control Mechanism Using Anonymous Identity-Based Cryptography
## 3 Proposed Mechanism
### Architecture  
The architecture is illustrated in Figure 1.  
_VKGC (Verification Key Generation Center)_: Multiple VKGCs exist to generate multi-level identity pseudonyms and corresponding private keys for users based on their real identities. The final VKGC in the chain generates a communication identifier for the user. However, VKGC does not directly generate the private key corresponding to the communication identifiers, it only produces the necessary communication key material.  
_CKGC (Communication Key Generation Center)_: CKGC verifies the legality of anonymous users based on their identity pseudonyms and signatures. It also works with the communication key material to distribute and generate a communication private key for the user.  
_User:_ The user utilizes the anonymous authentication service provided by the mechanism. They construct a pseudonym chain, authenticate their legality using their real identity, and request multi-level identity pseudonyms and a communication identifier from VKGC. The user then requests a communication private key corresponding to the communication identifier from CKGC. The communication identifier and private key are used for anonymous authentication with validating node.  
_Validating Nodes_: The validating node in blockchain has a verifying port and a communicating port. The verifying port transmits the verifying results to the communication port using IPC (inter-process communication). Based on this, the communicating port updates the blacklist and whitelist and filters network layer traffic upon receiving it.  
_IP Options Field_: Used to provide additional functionality or control information to IP datagrams. These fields are usually included in the IPv4 header, after the fixed header section. We add content to the IP option field to place the anonymous identifiers. In the subsequent network layer admission, the anonymous identifiers and the temporary IP address are bound as an access mark. The size is approximately a few tens of bytes. And it is compatible with current IP protocols, meaning it can be correctly forwarded by routers.  
\begin{table}
\begin{tabular}{c c} \hline
**Symbol** & **Definition** \\ \hline \(ds\) & User private key \\ \(kv\) & VKGC private key \\ \(P_{pub-p}\) & VKGC public key \\ \(kc\) & CKGC private key \\ \(P_{pub-c}\) & CKGC public key \\ \(H_{1},H_{2},H\) & Hash function \\ \(salt\) & Random value \\ _timestamp_ & Time stamp \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: System architecture.
# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 5 Analysis and Results
### Stage 2: Layered Evaluation, Analyzing the Impact of Quantization per Layer  
In this experiment, we independently quantized the weights of each layer over a range of 2 to 8 bits. Contrary to the previous stage, we used the full range in quantization bits. The quantization of inputs, activations, and non-considered layers was set to 8 bits. This was due to the results observed in the previous section, where it was shown that quantifying these two parameters does not strongly affect the inference cost and classification accuracy. An example of how this stage was carried out is shown in Table 3, where a particular layer is quantized at 2 bits, keeping the remaining layers quantized at 8 bits. As can be inferred, for the VGG10 1D-CNN, we executed ten runs per quantization level.