esa-sceva/satcom-chunk-collection huggingface dataset:
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 3 System Model
### Transmission Power  
WBANs are distributed networks and often a large number of networks have to co-exist close to each other. Each network has its own transmission power and it is practically very hard to maintain cooperation among all the WBANs to avoid co-channel interferences. Traditionally assigned transmission powers are not very optimized, so there is a big room for power optimization in WBANs. These traditional transmission power algorithms can generate tremendous amounts of interferences in the neighboring WBANs [21; 36]. We propose a novel power allocation algorithm to reduce the interference among gateways (which are acting as base stations for all control operations for the individual WBANs). Each gateway is governing various WBANs in its vicinity. The primary convergence criteria for the power optimization is interference minimization.  
The transmission power is calculated based on the desired interference level at the boundary of the WBAN, i.e., edge of a zone. The algorithm is derived from the convex optimization and is based on the illumination problem [37]. To calculate the transmission power, the interference boundary, i.e., perimeter of the zone, is divided into equally spaced linear small k patches. The interference received at any patch should not be greater than \(\mathrm{I_{des}}\), which is known as the interference threshold to avoid any interference to the neighboring WBANs. Equation (12) states the received power/interference level, \(\mathrm{I_{k}}\), at the center of each patch of interference boundary:  
\[\mathrm{I_{k}}=\sum_{j=1}^{m}\mathrm{a_{kj}}\mathrm{P_{T}}, \tag{12}\]  
where m is the number of transmitters (base stations). Since there is only one gateway serving the zone, here m = 1 and \(\mathrm{P_{T}}\) is the optimal transmission power of the transmitter. \(\mathrm{a_{kj}}\) is the propagation losses between transmitter and the receiving path k, where the interference level is calculated. \(\mathrm{a_{kj}}\) is calculated from the path loss and channel model presented in Section 3.3. Since the interference threshold, \(\mathrm{I_{des}}\), is known (it is determined empirically), the algorithm calculates the transmission power meeting this interference criterion for the given gateway. For optimization purposes, the objective function is formulated as shown in Equation (13):  
Minimize:  
\[\mathrm{max_{k=1,\ldots,n}}|\mathrm{log\,I_{k}}-\mathrm{log\,I_{des}}|, \tag{13}\]  
Subject to:  
\[0\leq\mathrm{P_{j}}\leq\mathrm{P_{max}}, \tag{14}\]
## 3 Application of TCP Panels in the Museum of the University of Alicante  
The Museum of the University of Alicante (MUA) is composed of a range of different volumes. Some rooms are semi-buried, and the rooftop is covered by a bed of water. The main room or \"cube\", which is where we will apply the TCP panels, is an extensive, flexible space lined on the inside with bakelised wood boards, measuring \(63\times 22.5\times 9\) m (Figure 3). The configuration is based on passageways throughout its perimeter allowing circulation over three levels for maintenance tasks. The circulation under the roof also takes place through 8 passageways. This buffer space is used for all the building's lighting, air-conditioning and water-evacuation installations (Figure 4). The roof includes 7 giant skylights with orientable aluminium external blades. The perimeter on the ground floor is made of simple glass, with MD panel-based protection.  
The interior enclosure consists of white lacquered MD panels glued with polyurethane cord on timber frames, screwed on a steel tube substructure. The outer enclosure was made up of sandwich type panels with rock-wood insulation, phenolic outer panel, MD interior panel and timber frames on the periphery. The enclosure was damaged because of humidity, solar radiation and thermal differences caused by thermal bridges due to the insulation material's discontinuity [16]. In 2010, the enclosure underwent an energetic rehabilitation. A total of 3 cm of projected polyurethane was applied to the existing skin, and a ventilated facade was generated, taking advantage of the existing metal substructure, with latest generation phenolic panels (Figure 5).  
Figure 3: View of the Museum of the University of Alicante (MUA).  
The MUA's current air conditioning system is an all-air system, with a split heat pump system located inside the conduits in the top part, and primary air is distributed through fiberglass conduits. The air is renewed by means of rooftop inlets, in manholes with drains that avoid the inlets from protruding above the rooftop plane.  
The present study was based on the hypothesis of replacing the MUA flooring with underfloor TCP panels. This would allow replacing the air conditioning system, which we will call Option 1 (OP1), by a radiant floor system based on hot water distribution in winter and cold water in summer, with a fan-coil dehumidification installation, hereon Option 2 (OP2). To do this, prefabricated panels would be used with pieces of latest generation 20 mm-thick porcelain stoneware (Figure 6). Hot or cold water  
Figure 4: Cross section of the MUA, with passageway spaces in the enclosure.  
Figure 5: Constructive details of the MUA. Energy rehabilitation project.  
would be distributed using a 32 mm-outer diameter PPR pipe system, embedded in the flooring in \(180\times 110\) mm pipes. TCP panels, containing 3 mm PPR capillary tube mats, would be connected with flexible hoses and click and cool connections.
## 6 Use Case: Cyber and Physical Attacks against a Water Critical Infrastructure
### Reading/Writing PLC data  
The attacker launches the Kali machine (192.168.66.5) and starts the PostgreSQL and Metasploit. To execute an attack against the victim database, the attacker uses a Modbus auxiliary from Metasploit. At this point, the attacker has two possibilities: read or write against the victim database. The attacker decides to read info from the database by specifying the target IP address (e.g., RHOSTS 192.168.66.6), the target port number (e.g., RPORT 502) and the Modbus data address (e.g., DATA_ADDRESS 1). After executing the attack, the attacker can read the values of the registers.  
In addition, the attacker succeeds to modify the registers in the database. In this simulation, the attacker has added values to the first seven registers, which originates an abnormal situation with temperature values falling outside the threshold.  
The detection of these attacks is performed using rules specifically designed for the Modbus protocol (e.g., read write registers, unauthorized access to the port 502, etc.). Examples of these rules used by our IDS can be found in [44].  
As a result, after the execution of the attack to read and write registers in the database, two logs have been generated by Suricata entitled: Modbus TCP - Unauthorized Read Request to a PLC and Modbus TCP - Unauthorized Write Request to a PLC. Examples of the Suricata messages received by the XL-SIEM are provided as follows:  
* 04/03/2020-09:32:36.325102 [**] [1:111123:1] Modbus TCP-Unauthorized Read Request to a PLC [**] [Classification: Potentially Bad Traffic] [Priority: 2] TCP 192.168.66.5: 39621 \(\longrightarrow\) 192.168.66.6:502
* 04/03/2020-09:43:54.890149 [**] [1:1111007:1] Modbus TCP-Unauthorized Write Request to a PLC [**] [Classification: Potentially Bad Traffic] [Priority: 1] TCP 192.168.66.5: 44919 \(\longrightarrow\) 192.168.66.6:502 Examples of the security events involving PLC communications within the platform are provided bellow:
* Unauthorized Read Request to a PLC; 2020-04-03 09:32:36; xlsiem-server; 192.168.66.5: 39621; 192.168.66.6:502; 5* Unauthorized Write Request to a PLC; 2020-04-03 09:43:54; xlsiem-server; 192.168.66.5: 44919; 192.168.66.6:502; 5  
Each detected event has been assigned a severity of four, meaning that the risk level is medium and should be carefully considered for mitigation.
## 5 The ATLAS MAC Protocol  
The basic aim of the ATLAS protocol is to ensure the medium access control of the network nodes, taking the estimated traffic load status (_i.e._, low, moderate, high and overload) into account. The details of the ATLAS protocol are described in the following subsections.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies
#### 2.2.12. EnOcean  
EnOcean [87] is a proprietary energy harvesting wireless sensor technology designed to be applied in a vast variety of applications such as building automation and control systems, transportation, cold-chain management, environmental monitoring and health monitoring. EnOcean is promoted by EnOcean Alliance to ensure interoperability of EnOcean products among different device vendors. EnOcean wireless technology is specifically optimized to provide solutions for ultra-low power consumption and energy harvesting applications. EnOcean-based devices utilize ultra-low power electronics and micro energy converters to enable wireless communications among battery-free sensors, switches, controllers and gateways. The main purpose of EnOcean's energy harvesting technology is to derive energy from surroundings such as light, motion, pressure and transform them into electrical energy that can be utilized. Recently, EnOcean is ratified as a new international wireless standard by the International Electrotechnical Commission (IEC) as ISO/IEC 14543-3-10 to accelerate the development of energy-optimized wireless sensor networks. Products based on EnOcean are designed to operate without batteries and are engineered to run maintenance-free. EnOcean operates in frequency ranges of 315 MHz, 868.3 MHz and 902 MHz. It uses Amplitude-Shift Keying (ASK) modulation technique, utilizes relatively small data packets (limited to 14 bytes) and supports data rates up to 125 kb/s. This technology is also able to support maximum indoor and outdoor communication ranges of 30 m and 300 m respectively.
## 4 Materials and Methods
### Hs-Spme-Gc-Ms Analysis
#### 4.3.2 GC-MS Conditions  
The GC-MS conditions were as follows: (1) column: DB-5 MS capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m, Agilent, Folsom, CA, USA); (2) shunt ratio: 1:15; (3) carrier gas: He (99.9999%); (4) flow rate: 1.2 mL/min; (5) injector temperature: 250 \({}^{\circ}\)C and detector: 280 \({}^{\circ}\)C; (6) temperature rise condition: 40 \({}^{\circ}\)C for 3.5 min, increasing at 10 \({}^{\circ}\)C/min to 100 \({}^{\circ}\)C, at 7 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C, at 25 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C, hold for 5 min. The MS conditions were as follows: (1): electron impact (EI) ionisation mode: 70 eV; (2): ion source temperature: 230 \({}^{\circ}\)C; (3) quadrupole mass detector: 150 \({}^{\circ}\)C; (4): transfer line temperature: 280 \({}^{\circ}\)C; (5): collection mode: Scan; (6) quality scanning range: \(m/z\) 40-400.  
The mass spectrograms corresponding to each chromatographic peak were compared with NIST05 and NIST05s standard spectrum library (NIST Mass Spectral Database 2.2) to identify the volatile organic compounds in three _C. oleifera_ petals. Each sample was repeated three times. The MassHunter quantitative software (version B.08.00, Agilent Technologies Inc., Santa Clara, CA, USA) was carried out to integrate and correct the chromatographic peaks, and peak area normalization method was applied to determine the relative content of each component after detection by GC-MS. The peak area of each chromatographic peak represented the relative content of the corresponding metabolite.
## 2 Materials and Methods
### Camellia Oleifera Fruit Object Detection Algorithm
#### 2.3.5 Activation Function Optimization  
The activation function is a crucial part of a neural network, responsible for adding nonlinear factors to the output of neurons in the previous layer so that the network model can fit nonlinear functions, thus improving the network model characterization ability. In order to alleviate the accuracy degradation of the backbone network after lightweight improvement and to improve the model generalization ability and detection accuracy, the Gaussian Error Linear Units (GELU) activation function is introduced in this paper [35; 36]. This function introduces the idea of stochastic regularity, a stochastic regular transformation method, which is essentially a fusion of Dropout, Zoneout, and ReLU, as shown in Equation (1).  
\[GELU(x)=xP(X\leq x)=x\Phi(x) \tag{1}\]  
\(\Phi(x)\) is the cumulative probability function of the normal distribution, as shown in Equation (2).  
\[\Phi(x)=\int_{-\infty}^{x}\frac{e^{-(X-\mu)^{2}/2\sigma^{2}}}{\sqrt{2\pi\sigma }}dX\ \ (X\leq x) \tag{2}\]  
where \(X\) is a random variable and \(x\) is a real number, \(\mu\) is 0 and \(\sigma\) is 1 represent the mean and variance of the normal distribution, respectively; \(\Phi(x)\) changes with the current neuron input \(x\), the larger the \(x\) value, the more likely the activation output will be maintained, while the smaller the \(x\), the more likely the activation output will be set to 0. The neuron is regularized with the dropout method so that the weight of each node is not too large, mitigating network overfitting. Since the value of Equation (2) cannot be derived directly by calculation, it can be calculated by the approximation formula of the GELU activation function of the standard normal distribution, as shown in Equation (3).  
\[GELU(x)=0.5x\Big{[}1+\tanh\Big{(}\sqrt{2/\pi}\Big{(}x+0.044715x^{3}\Big{)} \Big{)}\Big{]} \tag{3}\]  
The smooth activation function has better generalization performance and network optimization ability, thus improving the characterization ability of the network model, as can be seen in Figure 6. The ReLU activation function used in the original YOLOv5s network can only achieve nonlinearity when the input is less than 0, and the gradient is 0, which is prone to neuron necrosis. The GELU activation function has basically linear output when the input \(x>0\) is large; when the input \(x<0\) is small, the output is 0; when the input \(x\) tends to 0, the output is nonlinear. From the curve of the derivative function, it can be seen that the GELU function is fully derivable and it is not easy to cause gradient explosion and gradient disappearance, which is good for keeping small negative values and keeping the network gradient flow in a stable state. Based on this, the GELU activation function was introduced into the Shuffle Block in Figure 4a to replace the ReLU in the original module and improve the detection accuracy of the model.
# A Review of Image Inpainting Methods Based on Deep Learning
## 5 Applications
### Object Removal  
Object removal is a relatively widespread application of image inpainting techniques. The task typically involves removing specific objects from a photo and filling the removed area with pixels that make sense based on the surrounding background. The different application scenarios where object removal technology is applied are broadly divided into three main categories: general image object removal, remote sensing image object removal, and image desensitization.
## 2 Results
However, it should be noted that the side wall roughness, sidewall angle, or any imperfections due to fabrication inaccuracy are not considered in the numerical calculations. To enhance visual clarity and prevent overlap of the resonance dips, the spectra are thoughtfully offset along the Y-axis, as illustrated in Figure 2b. The free spectral range (FSR) is numerically determined for each RR, resulting in values of 4.2 nm, 2.8 nm, and 2.1 nm for radii of 50 \(\upmu\)m, 75 \(\upmu\)m, and 100 \(\upmu\)m, respectively. Furthermore, Figure 2c illustrates the FSR of the RR structure as a function of R within the range of 30 \(\upmu\)m to 100 \(\upmu\)m. The plot highlights an inverse relationship between R and FSR. This insightful analysis serves as a valuable guide for selecting an optimal R value for the RR structure, aligning with specific FSR requirements. For an in-depth exploration of the resonant behavior, the normalized H-field distribution within the RR with a radius of 100 \(\upmu\)m is presented in Figure 2d and Figure 2e for the on-resonance and off-resonance states, respectively. These depictions offer valuable insights into the spatial distribution and intensity of the magnetic field, providing a comprehensive understanding of the RR's resonant characteristics.
## 4 Materials and Methods
### Analytical Methods
#### 4.6.4 Determination of TPC, AC, and PPC  
TPC and AC were determined by the Folin-Ciocalteu reagent method and the DPPH radical scavenging method, respectively, as described in detail in [2,4]. Specifically, 0.1 mL sample, 5 mL water, and 1 mL Folin-Ciocalteu reagent were added in 10 mL flasks and left for 30 min in the dark. Then 1 mL of 7.5% \(w/v\) Na\({}_{2}\)CO\({}_{3}\) solution was added, the volume was fixed to 10 mL, and the mixture was left again for another 30 min in the dark. The absorbance was then measured at 725 nm (Jasco V-630 UV-vis spectrophotometer), versus a blank determination. The TPC was expressed as mg gallic acid (GA)/L of wine, with the aid of GA standard curves.  
For AC determination, 3 mL of 137.6 \(\upmu\)M methanolic DPPH solution and various amounts of sample (in the range 0.05-1 mL) were added in test tubes, and the volumes were fixed with methanol to 4 mL. The samples were left for 30 min in the dark and the absorbance was measured at 517 nm, against aqueous methanol solution as blank [2]. The results were expressed as mg ascorbic acid equivalents (mg AA/L) with the aid of standard AA curves.  
Additionally, a spectrophotometric estimation of the PPC was carried out, based on the reaction with ferric ammonium citrate (EBC Method 9.11) [24]. Specifically, 20 mL sample, 15 mL carboxymethyl cellulose (CMC) solution (containing 10 g CMC and 2 g EDTA in 1 1 water), 1 mL 3.5% ferric ammonium citrate solution (containing 16% Fe and prepared right before use), and 1 mL NH\({}_{3}\) solution (1:2 in water) were added under stirring in a 50 mL volumetric flask. The volume was fixed to 50 mL with deionized water. The mixture was left for 10 min and the absorbance was measured at 600 nm (Jasco V-630 UV-vis spectrophotometer). At the same time a blank solution was prepared without the addition of NH\({}_{3}\) (EBC, 1987). The PPC (mg/L) was calculated as the difference in absorbance between sample and blank multiplied by 820.
## 4 Descriptions of Different Topologies of WSNs
### Flat/Unstructured Topology
Second, all communication in directed diffusion is neighbour to neighbour with each node having the capability to perform data aggregation and caching. Unlike SPIN, there is no need to maintain global network topology in directed diffusion. However, directed diffusion may not be applicable to applications (e.g., environmental monitoring) that require continuous data delivery to the BS. This is because the query-driven on-demand data model may not help in this regard. Moreover, matching data to queries might require some extra overhead at the sensor nodes. Rumor routing performs well only when the number of events is small. For a large number of events, the cost of maintaining agents and event tables in each node becomes infeasible if there is not enough interest in these events from the BS. Moreover, the overhead associated with rumor routing is controlled by different parameters used in the algorithm such as time to live (TTL) pertaining to queries and agents. Since the nodes become aware of events through the event agents, the heuristic for defining the route of an event agent highly affects the performance of next-hop selection in rumor routing [56]. Overall, the advantages of flat-based protocols include (a) good quality routes from source to sink, and (b) no topology maintenance overhead. On the other hand, there are few disadvantages:  
* The main way of communication is flooding. However, flooding is an expensive operation that is normally avoided by sensor network routing protocols. * A large number of redundant messages are created and passed. This redundancy consumes processing cycles as well as bandwidth of the network. Due to the redundancy latency increases because of the high contention of wireless communication medium. * Non-uniform energy distribution occurs in flat/unstructured topology. This is the reason why the lifetime of a sensor network decreases. * Sensors are not aware of new members or died members. * Highly unreliable. * High delay.
## 1 Introduction  
Emergency management (EM) increasingly depends on computational and communication systems for coordination, communication, information gathering, training and planning. For example, wireless sensor networks can contribute towards early detection of emergency events [1,2], as well as improved situational awareness during a search and rescue operation, at the level of individual buildings [3] or larger geographical areas [4]. Autonomous systems and particularly autonomous vehicles are also commonly proposed in the EM context. Situational awareness and coordination may be improved with live aerial imagery provided by unmanned aerial vehicles [5] or with an ad hoc infrastructure of wireless robots that reach locations otherwise inaccessible to the first responders [6]. The Internet also plays a significant role, with several web-based EM systems, as well as with the widespread use of social media for the dissemination of information during an emergency, both by the authorities and the public [7,8].  
This increased use of computational and communication systems introduces cyber threats in EM. Cyber attacks can directly cause physical damage or indirectly aggravate a physical incident by impeding the work of first responders. As EM makes use of several private and public communication systems, from satellite communications to wireless sensor networks, cellular networks and the Internet, a security breach in one communication medium can have an impact on all other ones. In fact, the prevalent use of cyber-physical systems means that a cyber attack can even affect the operation of physical devices, such as flood control equipment or safety sensors. At the same time, decisions during an emergency need to be taken and communicated quickly. A cyber attack that would target the integrity of the information could have an immediate effect on the decision making that relies on that information, while a denial of service attack could cut off communication between commanders and first responders. The various EM interdependencies have been categorised by [PERSON] _et al_. into physical, informational, geospatial, policy/procedural and societal ones [9,10].  
Our aim is to illustrate the landscape of the EM-related information security research and identify areas of priority where further work is needed. We have previously discussed the security threats to EM networks and their unique challenges in terms of time-criticality, system interdependencies and the human element [11]. Here, we attempt to cover the broader spectrum of computational and communication threats in relation to the technologies used in the mitigation, preparedness, response and recovery phases. The four phases comprise what is known in EM as the _Comprehensive approach_, originally proposed in 1978 and, although challenged over the years [12], still in use in the United States, the UK and Commonwealth, and several other countries. By following the widely used _Comprehensive approach_ terminology, our aim is to facilitate communication between information security and EM practitioners and researchers.