esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Study Design  
Retrospective non-random, observational, cohort long-term study. The study inclusion criteria were: cone beam computerized tomography (CBCT), flap procedure, two experienced surgeons, date of implant placement between 2000-2010, partially edentulous, healed implantation sites, sufficient keratinized gingiva (>2 mm on either side of incision), fixed partially restoration, informed consent to undergo implant-supported restoration, generally good health, and available periapical radiographs performed with a film holder (Dentsply Rinn, Elgin Rinn Corporation, XCP). The exclusion criteria were gingival grafts; irradiation in the head and neck area; uncontrolled diabetes; pregnant or nursing, substance abuse; immunosuppressed or immunocompromised patients; treatment with oral or IV bisphosphonates; psychiatric therapy; untreated periodontal disease; healed sites (at least 3 months after teeth extraction); severe bruxism or clenching; poor oral hygiene and motivation (full mouth bleeding on probing and full mouth plaque index >25%; current smoking (over 10 cigarettesper day); immediate placement; immediate loading; implant design (tissue level). The primary outcome parameter was fraction of technique preference over the years. The secondary outcome parameters included clinical measures as implant survival, radiological measure--crestal bone loss and gingival index (15). The gingival index is used for the assessment of prevalence and severity of gingivitis. Score 0 = Normal gingiva; Score 1 = Mild inflammation--slight change in color, slight edema. No bleeding on probing; Score 2 = Moderate inflammation--redness, edema, glazing. Bleeding on probing; Score 3 = Severe inflammation--marked redness and edema, ulceration. Tendency toward spontaneous bleeding.  
Additional information that was retrieved from the patients' files included gender, age, implant characteristics (type, length, diameter) and implant location (maxilla/mandible).
# Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN
## 3 System Model and Problem Formulation
### Problem Formulation  
In the proposed D-JERDG, we consider the UAV capabilities of energy replenishment and data collection. The UAV departs from the BS and moves according to a planned flight path. It is responsible for collecting data generated by all cluster heads in the network. After completing a mission cycle, the UAV returns to the BS to recharge and transmit the collected data back to the BS. The flight path consists of the hovering positions and the CH node access sequence. First, to improve data collection efficiency, the UAV needs to serve as many nodes as possible within the maximum flight time \(T\) and collect data. Second, the charging strategy for the target CH node is crucial to minimize the node death rate and flight costs. To ensure that high-energy-consuming nodes and remote area nodes receive energy replenishment while reducing ineffective flight time, the UAV flight distance and speed need to be controlled.  
In summary, the overall objective of D-JERDG is to minimize the UAV flight energy consumption and maximize its throughput while minimizing the node death rate. This problem is a multiobjective optimization problem, and the objective functions can be formulated as follows:  
\[\text{P1}:\min(N_{d},E_{u}^{ave},L_{u}^{total}) \tag{17}\]  
The constraint condition can be formulated as follows:  
\[0\leq\sum_{n=0}^{N}t_{n}\leq T \tag{18}\] \[\Delta d_{u,c\lambda t_{n}^{tar}}<R_{d}\] (19) \[\forall\Delta d_{u,cmj}<R_{c}\] (20) \[N_{d}^{i}=\left\{\begin{array}{l}0,RE_{i}(t){=}0\\ 1,RE_{i}(t){>}0\end{array}\right.\] (21) \[v_{\min}\leq v_{u}(t)\leq v_{\max}\] (22) \[0<\theta_{u}(t)\leq 2\pi\] (23) \[0\leq x_{u}(t)\leq L\] (24) \[0\leq y_{u}(t)\leq L \tag{25}\]  
In objective functions, \(N_{d}^{i}(i=(1,2,\dots,k))\) is denoted as the number of dead nodes, \(L_{u}^{total}\) represents the total flight distance, and \(E_{u}^{ave}\) represents the UAV's average flight energy consumption. In the constraint condition, Formula (18) represents the UAV with a maximum flight time of no more than \(T\). Formula (19) represents the maximum data transmission range of the node into the UAV to establish a communication link with the UAV. Formula (20) indicates that nodes can harvest energy from the UAV within the maximum charging range of the UAV. Formula (21) represents the energy state. If \(RE_{i}(t)=0\), node \(i\) is considered a dead node. If \(RE_{i}(t)<0\), node \(i\) is in a normal state. Formulas (22) and (23) represent the size limit of the UAV's speed and heading angle. Formulas (24) and (25) represent that the UAV's flight range does not extend beyond the two-dimensional area.
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 4 Proposed Multi-Frame Blind Super-Resolution Algorithm
### Preparation and Initial Settings  
The middle frame of LR images is selected as the reference frame. We use the inter-frame information to calculate the relationship between each frame and the reference frame. The initial values of blur kernel, motion estimation and the HR image are set first. Bicubic interpolation is used for the initialization of the estimated HR image. Enhanced correlation coefficient (ECC) optimization [21] is employed for the initialization of motion estimation. The blind image deblurring method [22] is used for the initialization of the blur kernel estimation.
## 3 Results
### Pseudomonas aeruginosa PA01 3981
#### 3.2.2 Combination of Agents  
\(N\)-acetylcysteine 25.0 mg/mL in combination with either 12.5 or 25 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL bromelain displayed the most efficient biofilm dissolution values (BD MAX = 95.5 and 97%, respectively) amongst all combinations in a synergistic manner. The combination of NAC 50 mg/mL with either 6.25 or 12.5 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL bromelain also displayed good dissolution values (BD MAX = 88% and 95.3,) respectively), with CI values indicating good synergy (Figure 2A and Table 2).  
Hence, in this particular strain of bacteria, the combination of bromelain with NAC performed very well at four different combinations (BD MAX 88-97%), as shown in Table 2 (highlighted).  
\begin{table}
\begin{tabular}{c c c c c} \hline _Pseudomonas aeruginosa_ & **N-Acetylcysteine (mg/mL)** & **Bromelain (\(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL)** & **BD MAX (\%)** & **Combination Index (CI)** \\ \hline \multirow{6}{*}{ATCC 27853} & \multirow{2}{*}{15.0} & 12.5 & 73 & 0.063 \\  & & 25.0 & **87** & **0.025** \\ \cline{2-5}  & & 12.5 & \(-\)19 & antagonistic \\  & & 25 & 67 & 0.209 \\ \cline{2-5}  & & 6.25 & **85** & **0.008** \\  & & 12.5 & **80.5** & **0.03** \\ \hline \multirow{6}{*}{PA01 3981} & \multirow{2}{*}{10.0} & 20 & 33 & 0.672 \\  & & 40 & 50 & 0.268 \\ \cline{2-5}  & & 12.5 & 66 & 0.019 \\  & & 25 & 71 & 0.022 \\ \cline{2-5}  & & 12.5 & **95.5** & **0.001** \\ \cline{2-5}  & & 25 & **97** & **0.001** \\ \cline{2-5}  & & 6.25 & **88** & **0.009** \\ \cline{2-5}  & & 12.5 & **95.3** & **0.003** \\ \hline \multirow{6}{*}{ATCC 31461} & \multirow{2}{*}{10.0} & 20,0 & 13 & 7.34 \\  & & 40.0 & 27 & 5.76 \\ \cline{1-1} \cline{2-5}  & & 12.5 & 50 & 0.819 \\ \cline{1-1}  & & 25 & **81** & **0.403** \\ \cline{1-1} \cline{2-5}  & & 12.5 & **80** & **0.357** \\ \cline{1-1}  & & 25.0 & **85** & **0.395** \\ \cline{1-1} \cline{2-5}  & & 6.25 & 21 & 2.195 \\ \cline{1-1}  & & 12.5 & **79** & **0.581** \\ \hline \end{tabular}
\end{table}
Table 2: The biofilm dissolution actions of individual agents and their combinations on the biofilm secreted by _Pseudomonas aeruginosa_ ATCC 31461, PA01 3981, and ATCC 27853. Only values with \(p\) < 0.05 are shown in the tables. Additions of agents with significant effects are highlighted inbold font. Note: BD MAX (%): Maximum Biofilm Dissolution (%); Antagonistic: CI value > 2.0.
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 2 Materials and Methods
### Calibration of Hyperspectral Images  
The acquisition of imaging spectral data under the indoor conditions can be affected by many factors, including instrument parameters and light source. The flat field (FF) model was used to reduce noise and convert the spectral radiance measurements into reflectance. The FF model is developed on the basis of the internal average relative reflectance (IARR) model. There is a region with a certain area and uniform distribution in an image before using this method. The average value of all pixels in the uniform region was calculated, and then the value of each pixel in the image was divided into the average value. The formula is as follows:  
\[R_{j}(i)=(\frac{N_{j}^{a}}{\sum_{i}DN_{j}(i^{a})})DN_{j}(i) \tag{1}\]  
where \(R_{j}(i)\) is the spectral reflectance of the \(i\)th pixel in the jth channel of the sensor; \(N_{j}^{a}\) is the total number of pixels of the \(j\)th channel in the uniform region; \(DN_{j}(i^{a})\) is the digital number (DN) value of the \(i\)th pixel in the \(j\)th channel in the uniform region; and \(DN_{j}(i)\) is the DN value of the \(i\)th pixel in the \(j\)th channel in an image. Here, the white reference panel is treated as the uniform region.
# Fine-Grained High-Utility Dynamic Fingerprinting Extraction for Network Traffic Analysis
## 3 Data Processing
### SSI/TLS Encryption Protocol and Communication Process  
This section analyzes TLS1.2, a Transport Layer Protocol that works over TCP. TLS version 1.2 consists of two layers: the TLS Recording Protocol and the TLS Handshake Protocol. The TLS Recording Protocol works under the TLS Handshake Protocol and provides basic functionality. The TLS Logging Protocol defines four specific protocol formats, which are the Handshake Protocol, the Alert Protocol, the Change Password Specification Protocol, and the Application Data Protocol. The upper-layer TLS handshake protocol is primarily used for negotiation sessions.  
The TLS Handshake Protocol is superior to the TLS Recording Protocol. When the client and server initiate communication for the first time, the two parties agree on the protocol version, encryption algorithm, digital certificate, etc., and complete the following functions. First is the Hello Message exchange and agreement to carry out algorithms, random numbers, and other information on the server, and check the session recovery. Second, the two sides exchange necessary key parameters, allowing the client and server to directly exchange the Hello Message, the server in the algorithm, the random number, and other information on the agreement, and check the session resumption. Third is the exchange of digital certificates and related parameters so that the client and server can authenticate each other. Fourth, the master key is generated from the pre-master key and random numbers are exchanged with each other. Last, it must be ensured that the client and server can verify each other's parameters and that the parameters will not be tampered with by attackers.  
When the client and server need to use TLS Protocols to establish communication, they must follow the TLS Handshake Protocol to establish communication, which can be divided into three phases.
## 5 Experimental Results
### Ablation Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **PESQ** & **CSIG** & **CBAK** & **COVL** & **SSNR** & **STOI** \\ \hline Noisy & 1.97 & 3.34 & 2.44 & 2.63 & 1.68 & 0.916 \\ SECS-U-Net (\(L_{MAE}\)) & 2.69 & 3.96 & 3.35 & 3.32 & 9.59 & 0.942 \\ SECS-U-Net (\(L_{CS}\)) & 2.63 & 3.84 & 3.32 & 3.24 & 9.68 & 0.942 \\ SECS-U-Net (\(L_{MSTFT}\)) & 2.91 & 4.33 & 3.44 & 3.64 & 8.70 & 0.947 \\ SECS-U-Net (\(L_{MAE}+L_{CS}\)) & 2.65 & 3.92 & 3.32 & 3.28 & 9.43 & 0.942 \\ SECS-U-Net(\(L_{MAE}+L_{MSTFT}\)) & 2.90 & 4.31 & 3.47 & 3.63 & 9.13 & 0.947 \\ SECS-U-Net (\(L_{CS}+L_{MSTFT}\)) & 2.84 & 4.22 & 3.45 & 3.54 & 9.51 & 0.947 \\ SECS-U-Net (\(L_{all}\)) & 2.87 & 4.25 & 3.47 & 3.58 & 9.58 & 0.947 \\ SECS-U-Net (\(L_{alpha}\)) & 2.91 & 4.30 & 3.49 & 3.62 & 9.54 & 0.947 \\ SECS-U-Net (\(L_{alpha}\), \(No\ Att\)) & 2.92 & 4.30 & 3.44 & 3.63 & 8.80 & 0.946 \\ SECS-U-Net (\(L_{alpha}\), \(No\ Res\)) & 2.54 & 3.96 & 3.28 & 3.25 & 9.38 & 0.938 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiment of different loss. that when Res_Blocks is not used in the codec, the enhancement quality is degraded, and the PESQ is only 2.54. So, it shows that the Res_Blocks which we constructed are effective in improving the enhancement performance.
## 1 Introduction  
Nowadays, digital and analog filters are both commonly used for analysis and signal processing. Their effectiveness and quick operation allow them to eliminate unwanted noise or signal components. Many of their designs, implementation methods, and programming are well known [1; 2; 3; 4]. In the case of digital filters, there are many different approaches (e.g., time window method or bilinear transformation).  
In this paper, the concept of creating a filtering system--called a network due to the similarity of its construction to one-way (recurrent) neural networks--is presented. Each element in the network is the same, with one high-pass and one low-pass output. The final structure is defined by weights. Their value affects which signal is forwarded to the next layer of the network. A network composed of these non-stationary elements has the ability to shape frequency characteristics without any restraint, and is also characterized by proper accuracy of the filtering process. This allows for the possibility of expansion which enables operation with even the most complex signals.  
Furthermore, it is known that in filters with time-invariant parameters, there are only a few possibilities to shorten the time of the transient state. However, the introduction of time-varying parameters (i.e., the time functions of the time constant and the gain factor) allows a prominent reduction of the settling time without altering their spectral properties.  
This paper proposes a new method of selecting optimal parameters of the functions that vary the filter coefficients, where all necessary values are specified by the chosen algorithm, in this case the Particle Swarm Optimization (PSO) algorithm [5; 6; 7]. Using artificial intelligence methods is popular when it comes to filtration and methods are constantly being developed [8; 9; 10; 11; 12]. This procedure avoidsmany unnecessary calculations and helps to automatize and optimize the research. In order to avoid internal feedbacks, a layered structure was presupposed, which is an improvement compared to the previous concept of the network. This paper is organized into sections. In Section 2, the first-order element is presented in both analog and digital. In Sections 3 and 4 a description of the network structure is discussed. The first part explains the general construction while the second places emphasis on the parameters and the fitness function of the algorithm. Section 5 shows the varying functions used during the studies. Network operation and examples of filtration are shown in Sections 6 and 7. The conclusions summarizing the study are given in Section 8.
## 2 Related Work and State-Of-The-Art Technologies
### Framework Processes  
To support the execution of workflows in the framework, all executables and its dependencies (e.g., solvers, libraries and runtime environments) have to be integrated into different Docker images for later use. Moreover, a model description including all essential commands required to run the executable should be given by the users and automatically converted into a Python script. Additionally, for each executable, appropriate I/O adapters are used for instrumenting the data exchange between the executables. As shown in Figure 3, various I/O adapters, such as file adapter, TCP adapter, HTTP adapter, are offered by the framework. Finally, by using a Dockerfile, an executable, an I/O adapter, a Python script and the relevant dependencies are integrated into one Docker image. Based on the image and Kubernetes, the process management mentioned in the previous section can run Docker containers to perform the executables.  
While this procedure suggests that there is a large overhead in integrating new models and tools into the environment, the benefits of this procedure for the scientists far outweigh the additional effort. Once the integration is done, the scientist itself does not have to care about any of the runtime aspects for performing the workflow task (e.g., setting up a computer so that the application can be run successfully, e.g., sometimes there are libraries missing or a patch of the operating system breaks the application, starting and stopping the application, controlling resource usage). As mentioned in Sections 2.2.1 and 2.2.2, by leveraging Docker container and microservices, programs can be easily and flexibly executed in Docker containers running within different independent simulation nodes on a computing cluster, and the end users do not have to care about on which computing nodes the tasks of the workflow get started. Every aspect of the workflow can be supervised by him using a web browser for accessing the NiFi user interface. With Redis and the I/O adapters provided by the framework, data exchange between executables can be easily instrumented without requiring users to change their executables or implement interfaces and adapters.
## 3 Results and Discussion
### Effect of the Addition of Biochar Materials on Se Uptake from Soil and Its Phytoextraction
[49] confirmed that the addition of activated biochar as soil amendment provoked a higher reduction in metal concentrations owing to its higher porosity that promoted aeration conditions and transported oxygen molecules. Also, its larger specific surface area was considered a habitat for microbial attachment. The strong carbon sequestration capacity and an appropriate C/N ratio was beneficial to the growth and reproduction of microorganisms as an energy source. The greater ion exchange capacity benefited from the presence of surface functional groups. Organic matter adsorption could be also enhanced on the biochar surface, promoting the binding of metals and acting as a bridge between biochar and metals for pollutant immobilization. Indeed, the metal mobility could be also influenced by the transformation of its valence and speciation on the activated biochar surface. Although many previous studies confirmed the potential of activated biochar for metal stabilization, until now there is a lack of experiments trying to combine both approaches to soil remediation. Scarce studies tested some combinations such as [PERSON] et al. [46] who found that hardwood biochar could be used for phytoextraction, limiting As transfer to plants in three soils planted with Miscanthus. [PERSON] et al. [9] tested another combination of biochar made from miscanthus straw and Brassica napus for the phytoextraction of Cd and Zn resulting in low bioaccumulation in plant tissues. Based on these findings, [PERSON] et al. [50] suggested that biochar could also be used to alleviate metal contamination in mine soils before plant colonization.
## 4 ITS Threats Analysis and Classification
### ITS Security Architectures  
In the previous sections, the ITS security requirements, attacks and countermeasures were analyzed, and in the following, we will focus on describing global security architectures for ITS.  
The current research works classify the ITS security system architectures into three main different cryptography-based categories: (1) public key infrastructure (PKI)-based architectures; (2) crypto-based architectures; and (3) ID-based security architectures; as briefly described below.  
* PKI-based security architectures: Public key infrastructure-based architectures rely on asymmetric encryption/decryption algorithms to provide several security services, such as certificates generation, renewal and revocation, signing and issuing, checking and auditing. A certificate provided by a PKI aims to link a public key with the owner's identification and encryption technology. PKI requires the use of a certificate revocation list (CRL) in order to ensure safe and secure management in real network environments. This requirement can be considered as a critical problem and introduces high communication overhead. A detailed list of recent PKI-based security schemes and their evaluation in terms of communication overheads are presented in [84];
* Crypto-based security architectures: Crypto-based approaches are generally based on symmetric and asymmetric security algorithms to provide several security services. For instance, [85] provides a novel ITS security scheme that can provide privacy, data confidentiality and integrity and non-repudiation by using symmetric block cipher algorithm and a certificate-based public key cryptography scheme. The privacy and data confidentiality is ensured by employing the robust block cipher Advanced Encryption Standard (AES) [86];
* ID-based security architectures: In order to cancel the overhead of CRL- and PKI-based approaches, ID-based security architectures can be adopted, in which an encryption algorithm is employed for the generation of pseudonyms. ID-based security method aims to guarantee ID privacy, a precondition for the protection of user safety and privacy. The key point lies in generating irreversible algorithm for pseudonyms based on an ID with firm confirmation that only one pseudonym is available within the same entity to prevent Sybil attacks. Indeed, [87] employs ID-based encryption for pseudonym generation and conducts control of signature and identify authentication through a threshold scheme to satisfy security and privacy requirements. ID-based encryption is also used by [88] to ensure robust and secure V2I, V2V and I2V authentication processes. Finally, ID-based encryption can also be employed to generate the public keys from the entities identifiers [89], reducing thus the overhead of PKI-based solutions.