esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Second Messengers in Cyanobacteria
### Cyclic Adenosine 3',5'-Monophosphate, cAMP
#### 2.3.2 cAMP-dependent Transcriptional Regulation of Motility  
In _Synechocystis_, the cAMP-receptor protein SYCRP1 is required for motility and appears to impact pili biogenesis [69]. Non-motile _sycrp1_ mutant cells showed a drastic change in the pili phenotype as the length and number of pili were extremely reduced. SYCRP1 exhibits cAMP-dependent binding to _E. coli_ CRP consensus DNA binding sites [70,71]. Additional insights into the signaling pathway that controls motility emerged when it was demonstrated that several SYCRP1-regulated genes were under the control of Hfq, a homolog of a bacterial RNA-binding protein [72]. Notably, a mutant lacking _hfg_ lost motility, which was correlated with the absence of pili on the cell surface. When SYCRP1 is activated it controls cell surface proteins CccS and CccP [73], both of which were also identified as targets of Hfq [69]. Similar to _\(\Delta\)sycrp1_ and _\(\Delta\)hfq_ mutants, a _Synechocystis_ mutant lacking _cccS_ showed an absence of thick pili [73].
## 3 General Spatial Pattern and Meta-Pattern Model
### Spatial Pattern Identification Algorithm  
The new algorithm presented in this article is based on SP identification algorithm used in SPDIAM (phase SP Identification and Evaluation) [2]. First, the area of SP is constructed, using Space Syntax angular segment analysis measures with a certain percentage of maximum values, intersecting them with a grid (size of the cell 200 \(\times\) 200); then, SP is formed, identified, and evaluated.  
The new algorithm further analyzes the form of SP and uses new Space Syntax structure \"Visuality Graph\", together with the new VGA measures, to describe and identify the form of SP. The steps of SP identification algorithm (presented in Figure 7) include: (1) selection of SP economical, ecological, or social orientation (PAttribute.Name = 'Dimension'); (2) selection of the proper measures to describe PatternValue (class Measure); (3) tessellation [38] of CSS area to the grid and the assignment of the calculated syntactic values  
Figure 5: Basic spatial meta-pattern (UML class diagram).  
Figure 6: General SP and meta-pattern model (UML class diagram).  
of the measures to grid cells (classes Structure and Form); (4) construction of SP area by taking 30% of the highest value grid cells (classes Structure and Form); (5) identification of SP using VGA and the relation to geographers' and [PERSON]'s pattern typologies (classes PatternValue and PatternValueMeasure); and (6) evaluation of SP using open data indicators (class Indicator). As for spatial meta-pattern, the algorithm is much simpler, and it contains only 2 steps (Figure 8): (1) construction of spatial meta-pattern area; and (2) identification of spatial meta-pattern using VGA measures.
# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 1 Introduction  
Since December 2019, the world has been plagued with uncertainty and devastation relating to a novel virus, SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2), which causes the coronavirus disease 2019, COVID-19 (Corona Virus Disease 2019). The COVID-19 pandemic has had a profound economic and social impact on most countries. As of 8 May 2020, the virus has claimed almost 300,000 lives and infected almost four million people throughout the world.  
SARS-CoV-2 is not the deadliest virus in contemporary history. Ebola is significantly more deadly, reaching a fatality rate of 50% of infected people. Coronaviruses that cause Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) result in death in approximately 10% and 30% to 40% of cases, respectively. In fact, what makes COVID-19 particularly dangerous is that seeing that it does not immediately ravage the body, it remains active within the host for a longer period of time, therefore increasing the probability of contamination. The virus can attack the upper respiratory airways, often exhausting the host's immune system.  
Researchers around the world were urged to tackle challenges related to this virus, which remains an enigma for the most part. Ongoing intensive works include and are not limited to the sequencing of the virus, screening, related medical treatment, and the necessary vaccine. Our contribution concerns the screening of the virus.  
This research's motivation is to provide a reliable diagnosis system to support the decision-making of medical experts in the detection COVID-19 virus. Since the coronavirus mainly attacks the respiratory system, the diagnosis of chest X-rays has emerged as a viable solution for the detection of COVID-19 infection. In this paper, we contribute to the state-of-the-art by a comprehensive study that compares and combines 16 classifiers to develop a reliable inference system that can detect the COVID-19 virus from chest X-rs with high confidence.
###### Abstract  
Guided bone regeneration (GBR) is a well-documented and widely-used dental surgical procedure for the treatment of various types of alveolar bone defects. The aim of the study was to evaluate the long-term effectiveness of the GBR procedure in correcting small peri-implant bone dehiscence using the xenogeneic deproteinized bovine bone mineral material and a xenogeneic native bilayer collagen membrane. The present study was designed as a retrospective study. Seventy-five bone-level tapered two-piece dental implants Conelog(r), Camlog (Biotechnologies AG, Switzerland) were divided into two groups G1--no bone augmentation (no GBR)--44 implants and G2--bone augmentation (GBR)--31 implants. For both groups, the closed healing protocol with a primary wound closure was used. The incidence of peri-implantits was evaluated, the quantitative assessment of soft tissue thickness was performed using the ultrasound (USG) device, quantitative assessment of marginal bone loss (MBL) was done. The prevalence of peri-implantitis was 5.3%, with a three-fold increase in peri-implantitis comparing the groups without and with bone augmentation (G1 = 2.27%, G2 = 9.7%). The average keratinized gingiva thickness was 1.87 mm and did not differ significantly between groups. No statistically significant differences in MBL between G1 and G2 were found. When dividing patients by gender, no statistically significant differences were observed. When dividing patients by age groups, statistically significant differences were observed between the youngest and oldest groups of patients. Within the limitations of this study, it can be concluded that the use of xenogenic bone and a xenogeneic cellogen membrane in a GBR procedure can be recommended to correct small peri-implant bone dehiscence.  
guided bone regeneration; GBR; implant; soft tissue; marginal bone loss; bone graft Article  
Effect of the Lateral Bone Augmentation Procedure in  
Correcting Peri-Implant Bone Dehiscence Defects: A 7-Years Retrospective Study  
[PERSON]  
1 Department of Dental Surgery, Faculty of Medicine and Dentistry, Medical University of Wroclaw, 50-425 Wroclaw, Poland  
Department of Orthodontics, TU Dresden, 01069 Dresden, Germany  
Correspondence: [EMAIL_ADDRESS]
## 3 Data Analysis and Results
### The Mytilene Aftershock Sequence
(**c**) Interevent time \(T\) evolution with time \(t\) since the main shock. The scarlet line illustrates the \(T_{c}\) value. It is important to mention, that in all estimations the accuracy of the estimated values is in the order of \(\pm\)0.01.
## 4 Discussion
### Performance Comparison of Different Scenarios  
To illustrate the effectiveness of the model design, this model is compared to several other scenarios designed as follows: (1) terminal computing task off-ter, where the terminal only calculates tasks locally; (2) the UAV computing task off-uav, where the terminal only offloads tasks to the UAV for computation; (3) the G-MEC computing task off-GMEC, where the terminal only offloads tasks to the G-MEC for computation; (4) the random offloading off-rand, where the terminal randomly selects any uninstall node for the computing task; (5) the multilevel offloading on the basis of MDP off-MENROM. Figure 5 shows the simulation results.  
As shown in Figure 5, the utility function decreases with the increase of the task size when the model considers the energy level and delay parameters. The utility of the off-MENROM is always the largest. When the task is small, the off-ter utility is close to the off-MENROM utility. Conversely, when the task is large, the off-MEC utility is close to the off-MENROM utility. When the task increases, the system utility function decreases gradually because of the increase in computational cost. The multilevel offloading model can obtain a better utility value compared with other offloading methods, thereby indicating that the multilevel offloading model can save system costs, ensure user QOS, and optimize system resource allocation. When the task is small, the utility value of the terminal local computation is closer to the utility value of the best offloading mode. Meanwhile, when the computing task is large, the utility value of the task offloaded to the G-MEC after the UAV relay is closer to the utility value of the best offloading mode, that is, a larger task is suitable for off-GMEC offloading.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Symbol and Value** \\ \hline Total system bandwidth & \(B=20\) MHz \\ Signal propagation rate & \(v=3\times 10^{8}\) m/s \\ Data size & \(Z=(30,1500)\) Mbits \\ Required CPU cycles per bit & \(k=1000\) cycles/bit \\ UAV altitude & \(H=50\) m \\ Distance between the terminal and UAV & \(d_{u}=1\) km \\ Distance between the UAV and G-MEC & \(d_{uav}=1.2\) km \\ Power of the terminal for offloading & \(P_{uav}=35\) dBm \\ Power of the UAV for offloading & \(P_{uav}=37\) dBm \\ Noise power & \(\sigma=-70\) dBm \\ Energy consumption per CPU cycle for workload execution at the terminal equipment & \(\delta_{u}=3\) J/GHz \\ Energy consumption per CPU cycle for workload execution at the UAV & \(\delta_{uav}=2\) J/GHz \\ Energy consumption per CPU cycle for the workload execution at the G-MEC & \(\delta_{C\text{-MEC}}=1\) J/GHz \\ Operating mechanical power of the UAV & \(P_{uav}=58\) w \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 3 Results and Discussions
### Revisiting GRACE-Derived Non-Tidal DeformationWith GNSS Station PositionTime Series
#### 3.3.2 Degree-1 SHC Effects on the GNSS/GRACE Results  
The degree-1 SHC in the Earth's gravity field (geocenter motion) cannot be observed with satellite-to-satellite tracking. Although GNSS receivers on each GRACE spacecraft do give some sensitivity to their position relative to the Earth's surface, the sensitivity is not yet strong enough to give good degree-1 solutions [26]. Thus, the degree-1 SHC are not provided accurately in GRACE solutions. In the above section, we adopted the degree-1 time series provided by research of [44] to inverse the surface deformations. Here, we add the degree-1 SHCs obtained from the analysis of SLR data into the GRACE solutions and perform the same processing and analyzing program as with the above section. The _WRMS_ reductions are also listed in Figure 10. The results indicate that the difference between SLR geocenter SHCs and the research of [44] geocenter SHCs is not obvious for the _WRMS_ variations of the vertical component. Meanwhile, for the horizontal components, only for the east component, the GRACE solutions corrected with SLR geocenter perform less favorably.
# Data Privacy and Ethical Considerations in Database Management
## 3 Best Practices
### Data Encryption  
In database security, data encryption is essential to protect sensitive information. DBAs can establish efficient protection against unauthorized and potential data access by selectively encrypting critical data and limiting access to encryption keys [35].  
The efficacy of data encryption hinges on adherence to various best practices. First, meticulously evaluating the data types that justify encryption is essential. Sensitive data, including Personally Identifiable Information (PII), Protected Health Information (PHI), and proprietary data must be prioritized. Additionally, DBAs must opt for appropriate encryption algorithms that balance security strength and practical performance considerations. Regarding database encryption, data protection can be implemented across multiple levels, including cell, column, tablespace, and file levels.  
There are two types of Data Encryption Techniques:  
* Symmetric database encryption uses the same secret key for data encryption and decryption as illustrated in Figure 1. It is a simple and effective solution at a low cost. The main problem with this method is keeping the key secret and sharing it securely among authorized users. Examples of symmetric encryption algorithms include 3 DES, AES, DES, QUAD, and RC4.  
* Asymmetric database encryption, also known as public-key encryption, encrypts and decrypts data using public and private keys as shown in Figure 2. A more secure method for protecting sensitive data is to use a public key, which can only be decrypted given an associated private key. By separating public and private keys, asymmetric database encryption unravels the problem of managing key distribution. Examples of asymmetric encryption algorithms include Diffie-Hellman, ECC, El Gamal, DSA and RSA.  
Data encryption is essential for database security, ensuring confidentiality and appropriately maintaining the integrity and availability of information. The Data Encryption Standard (DES) has been fundamental to information security among the several symmetric encryption algorithms. By transforming plain text data into cypher text using a shared secret key, these algorithms ensure that the encrypted data will remain indecipherable without the key in case of unauthorized access to the database [44]. DES is no longer considered safe for new systems or applications requiring high security. The Advanced Encryption Standard (AES) is a symmetric key encryption algorithm that replaces DES and is widely used in modern systems and applications that require a high level of security [45].
## 2 Materials and Methods
### The Automatic System for Water Detection: AUTOWADE  
AUTOWADE (AUTOmatic Water Areas DEtector, [32]) is an automatic processor, which does not need any supervision or user intervention, that performs a systematic daily mapping of water surfaces (both floodwater and permanent water bodies) using Sentinel-1 (S1) data [32]. It was originally designed to respond to the Italian National Civil Protection Department's need of monitoring the water surfaces' extent. Therefore, it works at the national (Italian) scale. The AUTOWADE processor is formed by different components. The main component is obviously water detection, but other components are included, such as the automatic downloading of S1 images (GRD products) and their preprocessing to derive geocoded and calibrated data, the generation of reference and exclusion masks, and the delivery of the data to the end user. All these components are sequentially executed in a fully automated way. The water mapping algorithm implemented in AUTOWADE combines different image processing techniques, such as clustering, edge filtering, automatic thresholding, and region growing; it is applied to copolarized data only. The floodwater detection performed by AUTOWADE using Sentinel-1 SAR data is based on a change detection approach that uses a prefload image acquired under standard conditions and an image of the flood. The approach identifies the modifications of the roughness and the reflectivity of the ground due to the presence of floodwater. When the electromagnetic radiation transmitted by SAR impinges upon a target, the amplitude and phase of the backscattered electromagnetic field depend on the physical (i.e., geometry and roughness) and electrical (i.e., dielectric constant) properties of the target itself. These properties are substantially changed by the presence of floodwater. In particular, the roughness of the surface decreases because floodwater is generally very smooth (in the absence of significant winds). This implies that standing water reflects most of the impinging radiation toward the specular direction and the portion of the radiation that is backscattered toward the radar (which basically represents the SAR measurement) is very low. Then, the presence of floodwater implies a significant decrease in the backscattering with respect to that measured under nonflooded conditions.  
AUTOWADE is scheduled to run twice a day to process both ascending and descending S1 orbits. However, if the end user (Italian National Civil Protection Department) requires a fast processing of S1 data, for instance to quickly obtain information about the extent of a big flood, it is possible to manually run AUTOWADE in anticipation with respect to the schedule. This was perfrmed for the flood that occurred in Sicily due to the Apollo Medicane.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 3 The NDMR Approach
### NDMR Operations
#### 3.2.3 Path Maintenance  
After discovering and establishing two node-disjoint paths between the source and destination nodes, the path maintenance process is used to check and maintain the active paths of the active communication.  
1. _Case 1_: During the active communication between the source and destination nodes, the movement of intermediate nodes that do not lie along an active path does not affect the communication.
2. _Case 2_: If the source node moves during the active communication, it will decide whether to initiate a new path discovery process or not to establish one or two node-disjoint paths to the destination. 1. If the source can communicate with both its neighbors, which belong to the first and second paths, respectively, the movement of the source does not affect the active communication. 2. If the source becomes unreachable with a neighbor lying along an active path, the source will remove the path from its route table and set the remaining path to the first path. An SPREQ is then broadcast by the source to establish the second path to the destination.  
If the source cannot communicate with its neighbors that belong to the first and second paths, the source will re-initiate a new path discovery to establish two new node-disjoint paths to the destination.
3. _Case 3_: If any node along an active path fails or becomes unreachable, the node upstream of the break will send a PFLR message to the source. Upon the message, the source removes the corresponding path from its route table and then initiates the secondary path discovery by broadcasting an SPREQ to establish the new alternate path.
## 7 Using IETF Standards to Realize the Internet of Things
### Reprogrammability  
Finally, many IoT devices will have a long lifetime and will be deployed at locations that are difficult to reach. As such, it is clear that some mechanism will be required to (wirelessly) update IoT devices with new or updated firmware, software, protocols and/or security keys. As an example, the DisSeNT project [154] already provides solutions to wirelessly add new application level components at run-time to embedded devices.  
Providing wireless updates to IoT devices is at the moment quite difficult because it requires additional overhead in terms of the memory footprint of the software. In addition, (multi-hop) transmitting a firmware image to multiple embedded devices quickly depletes the batteries of involved devices. At the moment, very few embedded operating systems have the capability to execute these kinds of updates: additional research and standards will be needed before future-proof IoT networks can be deployed.
## 2 Design and Implementation of the Test
### The Testing Equipment and Sensors  
The equipment and loading method of the test are shown in Figure 3, where Figure 3a shows the test photo, Figure 3b shows the arrangement of sensors, and Figure 3c shows the cap with a variable pile spacing.  
The test was carried out in a self-balancing testing box with dimensions of 1.5 m \(\times\) 1.2 m \(\times\) 1.5 m (length \(\times\) width \(\times\) height). The minimum distance between the pile and the acrylic sidewall was greater than 15\(D\), so the boundary effect could be ignored [24]. When applying the load, the tail of the actuator was against the force transducer, and the force transducer was fixed on the reaction beam, which was rigidly connected to the testing box to form a self-balancing loading device.  
The layout of strain gauges is shown in Figure 3b. The pile was divided into two parts from the middle. Strain gauges were attached to each part along the axial direction. There were 15 pairs of strain gauges on the whole pile, and the first pair was 5 cm above the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Model**} & \multicolumn{2}{c}{**Scale Factor**} & \multicolumn{2}{c}{**Unit**} \\ \cline{2-7}  & **Law** & **Parameter** & **Prototype** & **Actual Value** & **Actual Coefficient** \\ \hline Depth & \(\lambda\) & 0.08 & 13.75 & 1.1 & 0.08 & m \\ Density & \(\eta\) & 1 & 16.4 & 17.2 & 1.05 & t/m\({}^{3}\) \\ File L & \(\lambda\) & 0.08 & 13.75 & 1.10 & 0.08 & m \\ Diameter & \(\lambda\) & 0.08 & 0.5 & 0.04 & 0.08 & m \\ E & \(\frac{l_{M}(1/\lambda)^{45}}{l_{p}}\) & 0.29 & \(25\times 10^{9}\) & \(3.3\times 10^{9}\) & 0.132 & Pa \\ \hline \hline \end{tabular} (E)P and (E)M are the bending stiffnesses of the prototype and model, respectively, \(\lambda\) is the geometric similarity ratio.  
\end{table}
Table 2: Similarity design.  
Figure 3: Equipment and sensors of the test: (**a**) testing photo, (**b**) the arrangement of sensors, and (**c**) the cap with a variable pile spacing.  
mud-line. The strain gauges 0-30 cm below the mud line were densified with a spacing of 5 cm, and the remainder were densified with a spacing of 10 cm. When the epoxy resin was cured, the pile was reclosed with special adhesive. Cable ties were settled every 100 mm along the pile axis to increase the overall strength. The pile end was closed with a pipe plug. The pile surface was brushed with glue and rolled in the sand to simulate the working condition of the bored pile [25].  
Four LVDTs (Linear Variable Differential Transformer) were settled at the side and top of the cap to measure the vertical (No.1 and No.2) and lateral displacements (No.3 and No.4), as shown in Figure 2(b). When the output difference between the two vertical LVDTs was greater than 5% or the lateral displacement was greater than 5 mm, the test was repeated.  
The cap was composed of 4-piece steel parts (Figure 2(c)). The holes were brushed with special adhesive and fastened with bolts to simulate the fixed pile head. Prefabricated parts could be connected between the clamps to simulate different pile spacing.