esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Joint processing coordinated multipoint transmission (JP-CoMP) has gained high attention as part of the effort to cope with the increasing levels of demand in the next-generation wireless communications systems. By clustering neighboring cells and with cooperative transmission within each cluster, JP-CoMP efficiently mitigates inter-cell interference and improves the overall system throughput. However, choosing the optimal clustering is formulated as a nonlinear mathematical problem, making it very challenging to find a practical solution. In this paper, we propose a distributed cell clustering algorithm that maximizes the overall throughput of the JP-CoMP scheme. The proposed algorithm renders the nonlinear mathematical problem of JP-CoMP clustering into an approximated linear formulation and introduces a multi-layer message-passing framework in order to find an efficient solution with a very low computational load. The main advantages of the proposed algorithm are that i) it enables distributed control among neighboring cells without the need for any central coordinators of the network; (ii) the computational load imposed on each cell is kept to a minimum; and, (iii) required message exchanges via backhaul result in only small levels of overhead on the network. The simulation results verify that the proposed algorithm finds an efficient JP-CoMP clustering that outperforms previous algorithms in terms of both the sum throughput and edge user throughput. Moreover, the convergence properties and the computational complexity of the proposed algorithm are compared with those of previous algorithms, confirming its usefulness in practical implementations.  
c 28 June 2020; Accepted: 24 July 2020; Published: 27 July 2020
## 2 Concerns Related to User Identity and Privacy in 5G Core Networks
\begin{table}
\begin{tabular}{l l l l} \hline \multicolumn{4}{c}{**VNF Abbreviation Resolutions**} \\ \hline
5G & Direct Discovery Name Management Function & NEF & Network Exposure Function \\ \hline
5G-EIR & Equipment Identity Register & NRF & Network Repository Function \\ \hline AAnF & Authentication and Key Management for Applications (AKMA) Anchor Function & NSACF & Network Slice Admission Control Function \\ \hline ADRF & Analytics Data Repository Function & NSSAAF & Network Slice Specific Authentication and Authorization Function \\ \hline AF & Application Function & NSSF & Network Slice Selection Function \\ \hline AMF & Access and Mobility Management Function & NSWOF & Non-Seamless WLAN Offload Function \\ \hline AUSF & Authentication Server Function & NWDAF & Network Data Analytics Function \\ \hline BSF & Binding Support Function & PCF & Policy Control Function \\ \hline CHF & Charging Function & SBC & Session Border Controller \\ \hline CSCF & Call Session Control Function & SCP & Service Communication Proxy \\ \hline DCCF & Data Collection Configuration Function & SEPP & Security Edge Protection Proxy \\ \hline DCMF & Distributed Control and Management Framework & SMF & Session Management Function \\ \hline DCSF & Data Channel Signaling Function & SMSF & Short Message Service Function \\ \hline EASDF & Edge Application Server Discovery Function & TNGF & Trusted Non-3 GPP Gateway Function \\ \hline GMLC & Gateway Mobile Location Centre & TSCTSF & Time-Sensitive Communication Time Synchronization Function \\ \hline HSS & Home Subscriber Server & TSN AF & Time-Sensitive Network (TSN) Application Function \\ \hline IBCF & Interconnection Border Control Function & TWIF & Trusted WLAN Interworking Function \\ \hline LMF & Location Management Function & UCMF & User Equipment (UE) Capability Management Function \\ \hline \end{tabular}
\end{table} 
Table 1: List of 5G VNF abbreviations and names corresponding to Figure 1.  
Figure 1: A depiction of the systematic domain elements and components that comprise the 5G cellular system. Orange outlined elements represent the access networks. Within the 5G Core, pink and gray elements show the user plane and edge computing management, green illustrates subscriber and session data stores, and blue shows core VNFs and services.
###### Abstract
The freezing temperature is an important factor affecting the microstructure of thawed soils. Though several NMR-based researches have been conducted to characterize pore water distributions in saturated soils, the effect of freezing temperatures on the microstructure in the thawed soils has not been systematically studied using the NMRI technique. It continues to be challenging to quantitatively characterize pore size distributions of thawed soils in subway wall. Since the relaxation time (\(T_{2}\)) spectrums from NMRI of targeted molecules reveal the pore structures of surrounding water molecules, it is expected that the measurement of \(T_{2}\) spectrums may be beneficial to elucidate the microscopic mechanisms of pore structure variations in thawed soils at different freezing temperatures [25]. Moreover, the NMRI technique has been of higher importance to exploring the pore water distributions in porous material for its visualization function [26]. Thus, the measurement of NMRI technique acts as a promising method to interpret the pore distribution characteristics of thawed soils after the process of artificial freezing. The main purpose of the present study is to assess the effect of freezing temperatures on evolution patterns of microstructure in granite residual soils based on NMRI measurements. First, the pore volume distribution curves of soils at different freezing temperatures were obtained based on \(T_{2}\) spectrums. Subsequently, the \(T_{2}\)-weighted cross-sectional images, which intuitively demonstrated the pore structures, were captured and studied. Lastly, the validity and accuracy of the NMRI measurements were verified by MIP testing results.
# Deep Learning-Based Detection Technology for SQL Injection Research and Implementation
## 3 Framework Overview  
The schematic representation of the system's overall architecture is illustrated in Figure 1. The overall architecture of our proposed system includes two stages: offline training and online testing. During the offline training phase, substantial samples crawled by crawlers and generated by SQL injection tools are labeled as positive and negative samples. After that, through the sample preprocessing module, structured data can be obtained, including encoding processing, generalization, word segmentation, etc. At last, the model is trained using the preprocessed structured data to obtain a classifier after vectorization. In the online testing phase, the data to be tested is preprocessed in the same way as in the offline training phase. Subsequently, the preprocessed data are fed into the trained classifier to ascertain the presence of an SQL injection attack.  
The data preprocessing module consists of data cleaning, decoding, and restoration of various encoded data, generalization, excessive rewriting, and tokenization. The generalization process mainly includes converting all letters to lowercase, representing strings in date format with the term \"date,\" representing integers with the digit 0, and representing hexadecimal data starting with \"0x\" with the field \"0x1234\". In response to the overriding bypass method, it mainly filters long strings containing keywords and uses keywords instead of long strings. Tokenization is performed based on blank separation, with the preservation of certain sensitive special characters.  
The sample vectorization module is responsible for converting preprocessed samples into numerical matrices that can be directly input into the algorithm module. This is achieved through various steps such as TF-IDF processing and Word2 Vec processing, which extract both term frequency and semantic information from the preprocessed samples. The resulting sample matrix is then used for subsequent model training.  
The algorithmic model module is primarily responsible for implementing the overall structure of the deep learning models based on the improved TextCNN and Bi-LSTM. Itinvolves training the models to obtain the detector, and the detailed model architecture will be elaborated in Section 4.  
The online testing module is primarily responsible for using the trained classifier to detect newly appearing SQL queries and output the detection results.
## 6 Results and Comparison
### Hardware and Software Requirements  
The Verilog (HDL) language was utilized to implement the two-stage pipelined architecture. The implementation was carried out on various Xilinx devices, which included Virtex 4, Virtex 5, Virtex 6, and Virtex 7. To synthesize the design, we selected the Xilinx ISE (14.7) design suite as our platform of choice.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results
### In-Depth Analysis
#### 5.3.4 Influence of Facial Activity  
Another type of subject movement is the movement of facial muscles, commonly observed during talking, eating, or expressing emotions. Such movement leads to deformations in the shape of the face and stretching and moving of the skin, potentially leading to interference with the underlying BVP signal. We study the impact of facial movement on our rPPG method and present the results in Figure 10. The datasets included in this study are those containing labelled talking/eating condition (PURE, MoLi-PPG, VIPL-HR, EatingSet) as well as those exhibiting high facial expression activity (MMSE-HR). In all datasets, we see that talking leads to poorer performance as compared to when the face is static. Videos with higher facial arousal (an indication of facial expression activity) in MMSE-HR (facial expressions and arousal on MMSE-HR were obtained via an automated facial expression analysis tool called FaceReader [63].) also result in a higher error rate for HR estimation. However, while eating, chewing motion does not seem to significantly influence accuracy. In fact, closer observation revealed that occlusion of the face caused when subjects take a bike/sip seem to be the larger cause of errors. The largest duration of facial occlusion happens when subjects take a sip (glass and hand covers the face), leading to a lower HR/HRV estimation accuracy.
## 1 Introduction
Finally, Bi-LSTM is employed to extract the contextual information from the feature-generated CNN layers to perform the sentiment analysis. To overcome overfitting, we applied Gaussian noise and Gaussian Dropout as regularization in the input layer. We selected four standard English Twitter data because it is an interpersonal interaction site growing with the use of short forms with challenges due to many misspellings, including polysemy and informal language. Social media users commonly post abbreviations and misspellings, so one spelling mistake may change the whole sentence's viewpoint. Likewise, we applied preprocessor tasks that remove noise, lemmatize and estimate special characters. We experimented on a diverse amount of accessible benchmark datasets for our proposed model's effectiveness. Wilcoxon signed-rank test is utilized to verify the existence of significant difference between each pair of methods for sentiment classification. The main contributions are as follows:  
* A novel text representation scheme based on TF-IDF feature weighting and pre-trained Glove word embedding has been presented to extract significant features for sentiment analysis. * We propose a novel attention-based CNN-Bi-LSTM model to improve accuracy and reduce overfitting. The model adopts the advantages of both CNN and LSTM to improve sentiment knowledge and accuracy. To avoid overfitting, we applied Gaussian noise and Gaussian Dropout. * The attention mechanism is used to pay suitable attention to different words to improve the feature expression ability. * We performed a comparative experiment on four Twitter datasets to assess the proposed architecture's effectiveness by improved accuracy. The rest of the paper is organized as follows: Section 2 discusses the related works. Then, we present the architecture of the framework in Section 3. Next, Section 4 provides the experiment setup and implementation and experimental results and analysis, and finally, Section 5 concludes this work.
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 4 LoRaWAN Protocol
### LoRaWAN Regional Parameters and Classes  
This section discusses the main components that will undergo modifications to form LoRaCog. It is important for our research's objective to give an overview of these components. LoRa utilizes unlicensed ISM bands, i.e., 868 MHz in Europe, 915 MHz in North America, and 433 MHz in Asia.  
To easily follow the work later in this paper, we highlight here LoRa's regional parameters for EU bands (European Union) [42]. The protocol requests for three channels to be set at manufacturing by default for every ED (868.1, 868.3 and 868.5 MHz with a bandwidth (BW) 125 kHz), and the rest of the channels will be dynamically allocated during operation. Those channels are the minimum set that all network gateways shall be listening to and cannot be modified through the \"NewChannelReq\" command to guarantee a minimal common channel set between EDs and network gateways.  
LoRaWAN EDs can implement different classes as shown in Figure 2. LoRa is a bidirectional communication protocol, which means communication between ED and GW can flow in both directions. As detailed in the LoRaWAN 1.0.3 specification document [42], there are three classes (Class A, B and C). The EDs of Class A (see Figure 3) allow for bidirectional communications whereby each uplink transmission is followed by two short downlink receipt windows. The transmission slot scheduled by the ED is based on its own communication needs with a small variation based on a random time basis (ALOHA-type of protocol) [43].  
The Class A operation is the lowest power system for applications that only require downlink communication from the server shortly after the ED has sent an uplink transmission. Downlink communications from the server at any other time will have to wait until the next scheduled uplink.  
LoRaWAN works as star topology, which means GW is in a direct link with EDs. In other words, the EDs can communicate only with GW and not with each other.  
The system consists, in addition to EDs of GWs, a Network Server (NS), a Join Server (JS), and an application server. As the intention is not to explain LoRaWAN in detail, we will explain some main elements that contribute and/or undergo modifications to produce LoRaCog in the following section.
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL
### Intracavity Photon-Assisted Tunneling
When \(\mathrm{I_{B}}\) is fixed on \(\mathrm{I_{B}}^{(2c)}\), which is obtained from the zero-chirp case, and the voltage signal weight of \(V_{CB}\) is changed, the voltage change \(\Delta V_{CB}\) is as follows:  
\[\Delta V_{CB}=\Delta V_{CB}^{(m)}\equiv(1-m)\Delta V_{0}+m\Delta V_{CB}^{(zc)}, \tag{6}\]  
where \(\Delta V_{CB}^{(m)}\) is the weighted voltage signal, \(\Delta V_{0}\) is the constant portion of \(\Delta V_{CB}^{(zc)}\) before pulse generation, and \(\Delta V_{CB}^{(zc)}\) is the \(V_{CB}\) at zero chirp. A large m (\(0\leq m\leq 1\)) indicates more chirp cancellation and a greater voltage weight. As \(\Delta\
u(\mathrm{V})\) increases, and \(\Delta V_{CB}\) and \(\Delta\
u(\mathrm{I})\) negate each other more, the overall frequency shift and number of chirps are smaller. Moreover, the weakening of the FK absorption due to the decrease in \(V_{CB}\) increases the output light intensity. In this study, the simulated eye diagrams for a 10 Gb/s data transmission rate is still clear after transmitting 100 km with zero chirp (i.e., \(m=1\)) and a pulse width of 0.1 ns. The TJ-TL collector doping has a significant influence on the voltage modulation capability [73] due to its dependence on the base-collector junction field. With heavy doping of the collector region, the enhanced junction field and FK absorption reduce the output optical power, which reduces the bandwidth. However, heavy doping also increases the sensitivity of FK absorption to voltage, which implies that an increase in the sensitivity of the optical power to \(V_{CB}\) changes and modulation speed. The collector doping density must be carefully considered because of the trade-off between power and modulation sensitivity. The chosen doping density value should ensure sufficient optical power and photon density while improving the sensitivity to voltage modulation. The discovery and application of photon-assisted tunneling have considerably expanded the application prospects of TL in laser modulation. Further, they also confirmed the great potential of TL in the future application of high-speed optoelectronic devices. However, the modulation characteristics of TJ-TL are significantly more complicated than those of conventional TL.
# Biosorption of Heavy Metals by the Bacterial Exopolysaccharideaccharide FucoPol
## 3 Results and Discussion
### Effect of FucoPol Dosage on the Pb\({}^{2+}\)-Binding Ability of FucoPol  
The effect of the FucoPol dosage on the removal of Pb\({}^{2+}\) from aqueous solutions (10 mg/L) was measured in terms of metal removal efficiency (\(M\)) and specific metal uptake (\(q\)), as disclosed on Figure 3.  
As shown in Figure 3, the concentration of FucoPol used as biosorbent influenced the metal uptake (\(q\)), as well as the overall metal removal efficiency (\(M\)). In all experiments, an increase in FucoPol concentration led to an increase in metal removal. These results could be related to the fact that more binding sites were available to adsorb the metal ions in solution [23; 40]. Maximal metal removal (93.9 \(\pm\) 5.3%) was achieved when 5 g/L of FucoPol were used, decreasing when a higher concentration (10 g/L) was tested (66.0 \(\pm\) 8.2%). Thus, it can be speculated that higher concentrations of FucoPol increase the occurrence of polymer-polymer interaction, reducing the availability of binding sites capable of metal sequestration [21]. Furthermore, higher concentrations of FucoPol cause an increase in viscosity [41], which may affect the diffusion of the metals, thus restraining their interaction with less accessible binding sites. Similar results were reported in the literature for other EPS. For example, for the EPS produced by _P. aeruginosa_, the use of an EPS concentration of 100 mg/L resulted in higher Pb removal rather than the higher dosages tested (1000-10000 mg/L) [36]. Also, for the EPS produced by _P. peoriae_ TS/, a maximal Pb\({}^{2+}\) removal of 89% was obtained with 0.5 g/L of EPS, since both  
Figure 2: Specific metal uptake (\(q\), \(\bullet\)) and removal efficiency (\(M\), \(\blacksquare\)) by FucoPol (5 g/L) using different initial Pb\({}^{2+}\) concentrations (temperature 21 \({}^{\circ}\)C, incubation time 24 h).  
lower (0.25 g/L) and higher concentrations (up to 2 g/L) led to a significant reduction in the removal efficiency [23].  
An opposite trend was observed for the metal uptake, in which \(q\) decreased with the increment of the FucoPol's concentration on the dialysis tubing (Figure 3). When the concentration of FucoPol rose from 1 to 10 mg/L, the metal uptake decreased from \(81.3\pm 7.8\) mg/gEPS to \(14.4\pm 1.2\) mg/gEPS. The different profiles of \(M\) and \(q\) shown in Figure 3 are in line with the results achieved for the biosorption of Cu\({}^{2+}\) by the anionic EPS produced by _Bacillus_ sp. F19. Indeed, an increase in EPS dosage from 500 to 1500 mg/L lead to an increase in removal efficiency from 71.2% to 91,2%, and a reduction in metal uptake from 142 to 61 mg/gEPS [34]. Due to the differences in profiles under the same conditions, the subsequent studies were carried out with 5 mg/L of FucoPol, which was the concentration that maximized \(M\).
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 5 Experimental Validation  
To show an actual implementation of MoDaNE approach and its ability to discriminate between different nonlinearities in different media, a few experimental results are presented in this section. The characteristics of the investigated samples are the following:  
* The concrete sample (B06) was in the shape of a cylinder (4 cm diameter and 16 cm length), and was drilled from a casting prepared with 340 kg of cement (CEM II A-L 42.5 R), 957 kg of sand (0-5 mm), 846 kg of gravel (5-15 mm) and 200 kg of water (w/c ratio \(\approx\) 0.59).  
Figure 9: Relative errors in the estimation of the nonlinear parameters for increasing modulus nonlinearity. The signal distortion, defined as the ratio between third harmonic and fundamental amplitudes, increases with increasing nonlinearity. Arrows in the plot indicate the result corresponding to Case 3 of Figure 3.  
* The Berea sandstone sample was a thin cylinder with a diameter of 1 cm and 15 cm long. The size of the grains in this sample is of the order of tens of micrometers.
* The two linear samples (PMMA and copper) were in the shape of cylinders (section of 2.25 cm\({}^{2}\) and length of 14.5 cm). Both samples were intact.  
The experiments were carried out as follows. A waveform generator (Tektronix AFG 3022B) generating monochromatic waves at increasing amplitudes is connected to a linear amplifier (CIPRIAN Model US-TXP-3, 200\(\times\)). Two ultrasonic sensors with a diameter of 2.5 cm and a centre frequency at 55 kHz (MATEST C370-02) were connected one to the amplifier (source) and the other (receiver) to a digital oscilloscope (Lecroy 324A) for data acquisition. The signals, recorded in a short time window in standing wave conditions, are monochromatic waves and treated using the MoDaNE approach described before. Furthermore, the linearity of the system (including transducers and coupling media) was verified before starting the measurements.  
In Figure 10, it is easy to observe that copper and PMMA behave linearly and no variation in \(c\) and \(\alpha\) is present increasing the amplitude of excitation. Differently, the other two samples (consolidated granular media) exhibit high nonlinearity, both for relative variations of velocity and attenuation. However, some differences, most probably due to very different grain structures, can be appreciated: B06 shows a higher nonlinearity in the attenuation coefficient \(\alpha\) with respect to Berea sandstone, while the situation is the opposite when the variation of velocity \(c\) is considered for the two samples.