esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Framework for Automatic Detection of Wound Infection with Artificial Intelligence
## 4 Discussion
With such a simulation, the use of wound images increased the specificity of detection of wound complications and surgeon confidence, but worsened the sensitivity of detection of wound complications. Given the importance of image quality and experience with remote evaluation, some strategies should be implemented, such as participant training, standardization of patient photographic techniques, and synchronization of definitions of wound features [30]. In our study, the photographs were taken by surgeons during direct outpatient visits, which eliminated misdiagnosis and provided accurate data for further analysis. Our next goal is to design one application that will provide knowledge about self-care and real-time feedback from surgical professionals. Every uploaded wound photograph will be evaluated by the algorithms implemented in the server, and the output with abnormal wound features will be assessed by surgical professionals before sending alarm messages to the patient or caregiver. Although previous studies utilized CPU or GPU in mobile phones to perform wound analysis, the image resolution should be compromised to fit the limited performance of mobile phones [31; 32]. Moreover, it is difficult for mobile phones to run deep-learning algorithms. Therefore, to allow for a more complex and computationally demanding analysis of high-resolution photographs taken by modern smartphone cameras, we chose to do data mining in the server because the mobile internet is becoming popular, with fast internet speed. Although the recent improvements of deep learning have been significant, there still exist challenges to its application to medical imaging/wound images. A well-annotated large medical dataset is needed and considered as an important key for clinical application, because most of the notable achievements of deep learning are majorly dependent on large datasets. In the future, surgical academic communities should continuously collect the wound images during the perioperative periods that would foster research on the detection of wound infections. This study has some limitations. The study was performed on Asian subjects, which may limit its generalizability to patients of different nationalities or ethnicities. In addition, more patients participating in this study provide diverse quality of wound photographs, which contain inter-person variation because different mobile phone cameras have different color space characteristics or noise levels. This may cause a potential concern interfering with our algorithm. To overcome these limitations, color calibration is routinely adjusted by referenced color card to minimize these confounding factors. However, it may cause extra work for the patients or caregivers to take photographs with a color card. Finally, a \"test\" set in conjunction with a validation dataset should be considered to evaluate the performance of the final model. We will collect more wound images to reflect it.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 3 Fog Generator Model
### Model Architecture  
The proposed Fog Generator Model is built upon the GridNet framework [8], which was originally designed for image dehazing. In this study, we adapt the GridNet architec  
Figure 1: GridHazeNet: attention-based multi-scale network architecture.  
ture for the inverse task of fog synthesis. The model comprises three primary components: the GridNet backbone, a transmission estimation network, and fog parameter layers.  
The network takes a haze-free clear image \(J(x)\) as input and generates a synthetic foggy image \(I(x)\) by simulating atmospheric scattering effects. This process is guided by learned fog features and a predicted transmission map.  
The fog synthesis process is modeled as follows:  
\[I(x)=J(x)\cdot t(x)+A\cdot(1-t(x))+\alpha F(x) \tag{1}\]  
In this formulation, \(J(x)\) denotes the scene radiance, representing the haze-free ground truth image, while \(I(x)\) is the synthesized foggy image generated by the model. The transmission map is represented by \(t(x)\), and \(A\) denotes the global atmospheric light. The learned fog feature map \(F(x)\), extracted through the GridNet backbone, captures complex fog structures, and \(\alpha\) is a blending factor (set to 0.1 in our experiments) that controls the fog density.  
This extended formulation builds upon the conventional atmospheric scattering model by integrating learned, data-driven fog features alongside the traditional transmission and atmospheric light components. As a result, it enhances both the realism and diversity of synthetic fog patterns beyond what heuristic methods can achieve.
## 4 Discussion
The remaining ions from the charging phase in the acid near the electrode surface (\(Pb^{2+}\)) might increase the double-layer capacitance but impede the charge-transfer process in the discharge direction. \(\tau_{neg}\) measured after cycling dropped below 0.01 s for all test cells, when the aging test finished. At the same time, \(R_{ablmic}\) was twice as high as at the beginning. The values of \(R_{ct,neg}\) increased continuously but were slower than \(R_{oblmic}\). The ohmic resistance depends on the cross-section area of the grid, on the contact area between the grid and the active mass, on the average length and conductivity of the current path through the active mass, and finally on the contact surface between the active mass and electrolyte and the ionic conductivity of the electrolyte. It is assumed that only the average length of the current path through the active mas and conductivity of the active mass changed during aging, because of the accumulation of highly resistive lead sulfate crystals in the active mass. The charge-transfer resistance instead depends on the electro-chemically active surface area of the electrode and the current density [25]. The active surface area decreased as well because of the sulfate crystals; however, with decreasing surface area the local current density increased, which in turn reduced \(R_{ct,neg}\) again. No clear differences in the capacity decay between _hard AK3.4_ and _soft AK3.4_ could be observed here. However, based on the available data, it cannot be excluded that the test cells show a greater FCE in _soft AK3.4_. _TC1_ most likely had a defect, and _TC2_ did not reach the end of the test. In the fitted data, clear differences between the tests are visible. Although all curves change linearly over capacity decay, the variation of the slopes makes it necessary to measure the impedance after the same pre-history, preferably after cycling. For every single application, it is necessary to verify whether this is possible. An algorithm can search for frequently appearing patterns during operation to find good timing for measuring meaningful impedance values. However, the individual impedance change over capacity decay should be established for proper _SoH_ estimation. Hence, capacity tests are indispensable but could be performed less often. Last but not least, the verification of time invariance has to be performed, for which at least two impedance measurements at different frequencies are compulsory. An algorithm for _SoH_ estimation does not necessarily have to fit an EEC to an impedance spectrum, as a linear correlation is present already for single impedance values. However, the distinction between \(R_{olmic}\) and \(R_{ct,neg}\) is only possible with the fitting of an EEC and can contain more distinct information about the type of aging. This will be analyzed in the future with impedance spectra recorded during corrosion tests. It is expected that only \(R_{olmic}\) and neither \(R_{ct,neg}\) nor \(\tau_{neg}\) are affected by corrosion.
## 1 Introduction
FPGAs' characteristics suit EC requirements like (i) processing of data streams at lower latency, (ii) adaptability to any algorithm due to their reconfigurable architecture exploiting spatial and temporal parallelism at a finer granularity, and (iii) thermal stability reducing cooling cost [38]. FPGA-based edge devices have proven their resilience to physical and side-channel attacks. FPGAs' inherent ability to process tasks in parallel and flexibility in handling diverse workloads can match AI and ML algorithms' computational and processing needs. FPGAs allow greater flexibility in what the processor does, they are very useful in building AI accelerators [39]. The FPGA-based edge reduces the response time by 1.62\(\times\) for the object application and 1.14\(\times\) for the face application compared to CPU-based edge offloading in general [40]. [PERSON] et al. presented a novel approach to secure FPGA-based edge devices using a lightweight hardware-assisted chaos-based stream cipher for protecting FPGA bitstreams [41]. Regarding security, IP protection techniques implemented on FPGA have better flexibility and require no extra resource overhead compared to those implemented on a traditional custom circuit. [PERSON] et al. implemented a hierarchical decision-making approach combined with an ANN model as a hardware-accelerated framework on the FPGA for real-time detection of network intrusions [42]. An Oscillator Collapse (OC-PUF) designed to utilize manufacturing variations in FPGAs that generate unique responses to input challenges was tested on Altera DE2-115 FPGA boards, achieving an inter-chip Hamming distance of 46.7% [43]. FPGAs can run several lightweight cryptographic protocols simultaneously, in addition to advantages like optimal chip area, speed, and power consumption [44]. Silicon physically unclonable functions (PUFs) implemented on FPGA platforms are flexible, secure, cost-effective, and offer a quick turnaround. FPGA-based PUFs are diverse and effective in IP protection [45], RFIDs [46], secured key generation [47], and remote activation [48]. The remainder of this paper is divided into the following sections. Section 2 discusses the basic edge IoT architecture and key components of the edge ecosystem across three distinct layers of cloud, edge, and devices. Section 3 comprehensively discusses security, privacy challenges, associated countermeasures, and defense mechanisms deployed in an edge paradigm. Section 4 provides the implementation details of PUFs for device-specific authentication schemes in hardware security, digging into the reasons responsible for authentication and trust challenges, access control, and root causes of edge computing security threats, and also proposes future research directions. Finally, we conclude this paper in Section 5.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 3 Results and Discussion  
### gbXML Import Results  
#### 3.1.1 Residential case in Design Builder  
As it is possible to see in Figure 5, all the thermal zones established in the Revit file of the residential building were correctly imported into Design Builder. Furthermore, the library of materials was successfully transferred and a new template called \"gbXML\" was generated.  
However, although the model was perfectly constructed in Revit, it had some problems in Design Builder. First, the model dimensions in the BEM software were not the same as in BIM. In Design Builder, the building was 1.70% smaller than in Revit and, consequently, the volumes of the thermal zones were also smaller. Another problem was found concerning the model surfaces. In the north facade, there were errors in recognizing the walls and the roof and the first floor was not constructed accurately. In addition, some of the walls were imported without the construction assignment.  
Figure 4: IFC files imported into Open BIM Analytical Model.
## 2 Materials and Methods
### Installation Modifications  
The test engine was modified to run diesel/LPG dual fuel. A second injector housing was placed on the diesel engine cylinder head and the Magneti Marelli brand GDI injector was installed. LPG and diesel injector locations are shown in Figure 1. In order to control GDI injector, the angular piston position data which was obtained from an encoder coupled to the crankshaft was used. The ECU controlled the LPG injection timing and duration by evaluating the angular position data. To measure the instantaneous mass consumption of diesel and LPG fuels, electronic scales with precise measuring capability were used. A nitrogen cylinder with 200 bar operating pressure and pressure regulator were used to adjust LPG injection pressure. The liquid phase LPG that accumulates at the bottom of the LPG tank is transmitted to the GDI injector through a high-pressure fuel line. Thus, LPG injection in the liquid phase is provided via GDI injector.  
An Opkon brand PRI 50 model incremental optic rotary encoder coupled to the crankshaft was used for determining the angular piston position. As shown in Figure 2, the encoder has three output channels, A, B, and Z. Channel B leads Channel A by 90 degrees phase shift. Channel Z produces a pulse in every full turn of the encoder. A and B channels are used for rotation direction and position information while Z channels are used for rpm measurement. Direction sensing is determined by generating separate pulse trains for CW and CCW direction. A pulse train is generated by checking for falling edges on A or B pulses when other pulses are high. For position measurement, one pulse train adds to a count register and the other subtracts from a count register. Thus, a precise position detection was ensured, and the LPG injector was controlled with an appropriate timing. The GDI injector was triggered either by the injection of LPG into the cylinder or just after that ignition of the diesel fuel injected into the cylinder.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Engine Specifications**} & \multicolumn{3}{c}{**Alternator Specifications**} \\ \hline Manufacturer & Katana & Manufacturer & Katana \\ Engine Type & Km 178 Fe & Model & KD 4500 E \\ Diameter \(\times\) Stroke & 78 \(\times\) 62 & Maximum & 2.4 kVA \\  & & Power & 6.3 kVA \\ Cylinder Volume & 296 cm\({}^{3}\) & Power & 6.3 kVA \\ Maximum Output Power & 7.6 hp & Phase & 1 \\ Continuous Output Power & 0.6 hp & Voltage & 230 VAC \\ Engine Speed & 3000 rpm & Frequency & 50 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Generator specifications.  
Figure 1: LPG and diesel injector locations.
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
## 4 Carrier Sense Non-Orthogonal Multiple Access (CSNOMA)
### Reactive/Proactive Mode  
Although the optimal carrier sensing range was discussed earlier, in reality it is very difficult to always maintain the optimal value. As the communication nodes move the distance between the transmitter and the receiver changes, and the amount of wireless interference received from other nodes also varied. It is also difficult to determine the density of nodes in the network. This is not just a CSNOMA problem. In the CSMA regime, it is not easy to maintain an optimal sensing range. There are problems that arise when the sensing range is not an optimal setting.  
Suppose that the typical receiver and the typical transmitter with a short carrier sensing range are located in the network. Any nodes near the typical receiver, but not in the carrier sensing range of the typical transmitter, can attempt to transmit. Other transmit nodes around the typical receiver interfere with transmission from the typical transmitter. This situation is called the hidden node problem. As explained earlier, CSNOMA deliberately creates this situation. However, it is not necessary if the hidden node problem occurs. If CSNOMA tries to send additional nodes, it aggravates the situation. IC could be applied to resolve the hidden node problem, and not to promote other silent nodes to activate (Figure 8) like in [38]. We define this mode of operation as reactive mode. IC is applied opportunistically and CSNOMA does not make the situation on purpose.  
On the other hand, if the carrier sensing range is relatively larger than the communication distance, other transmitting nodes that are far enough away from the typical receiver and do not cause significant interference are also within the sensing range of the typical transmitter and do not transmit. This situation is called the open node problem. In this case, only few nodes have chances to transmit, thus it is desirable to force the silent nodes to wake up and apply interference cancellation (Figure 9). We define this mode as the proactive mode, which is the operation of CSNOMA has been described earlier.  
Figure 8: Example for reactive mode operation of CSNOMA. Solution of hidden node problem.  
Figure 7: CSNOMA frame structure.  
To make CSNOMA operate in reactive mode, disable RTS/CTS handshaking. CSNOMA controls neighboring nodes by performing hierarchical modulation on the CTS frame, so handshaking is not necessary in reactive mode where neighboring nodes do not need to be controlled. As the receiver implementing CSNOMA can still perform IC, it performs IC for hidden node transmission.  
As receivers must have IC-related functions, hardware complexity and computation cost increase. Furthermore, as the message decoding process is complicated due to IC, energy consumption increases. However, when the device is running out of energy, it can disable IC-related functions. Then, the device operates in IEEE 802.11 DCF mode, and computation cost and energy consumption can be reduced.
# A Novel Approach Proposal for Estimation of Ultimate Being Capacity Based on Pile Loading Test Data
## 2 Materials and Methods
### Method
#### 2.2.1 Direct Standard Penetration Test Methods for Determining Ultimate Pile  
Bearing Capacity  
The Standard Penetration Test, which is widely used in the world, is a test that determines soil density/consistency as well as helping the calculation of pile bearing capacity. The empirical correlation suggestions of some researchers, whose suggestions are widely used in this study, are given below.  
The Calculation of pile tip (\(q_{b}\)) and surface resistance (\(q_{u}\)) is done in the Meyerhof method according to Equation (2). It is a valid method only for coarse-grained soils [3]. In the equation, \(N_{b}\) is the average of the SPT N values between 10D above and 5D below the pile base. It is determined as \(n_{s}=1\), \(k=0.012\), \(m=0.12\) for bored piles; and as \(n_{s}=2\), \(k=0.04\), \(m=0.4\) and by using the equations \(q_{b}\) (\(MPa\)) = \(k\cdot N_{b}\), \(q_{s}\) (\(kPa\)) = \(n_{s}\cdot N_{s}\) for driven piles.  
\[m\cdot N_{b}\leq(L/D) \tag{2}\]  
The Decourt method calculation formula is given in Equations (3a) and (3b) [5]. For fine-grained soils: \(\alpha\) value is 1.0, \(k_{b}\) value is taken as 0.10 for driven piles and 0.08 for bored piles. In coarse-grained soils: \(\alpha\) = 0.5-0.6 (average value was taken in the study), \(k_{b}=0.325\), (\(N_{b}\))\({}_{60}\): Average of the corrected SPT N values around the pile base, and (\(N_{s}\))\({}_{60}\): is the average of the corrected SPT N values along the relevant layer to be calculated.  
\[q_{b}(MPa)=k_{b}\cdot\left(N_{b}\right)_{60} \tag{3a}\] \[q_{s}(kPa)=\alpha\cdot\left\{2,8\cdot\left(N_{s}\right)_{60}+10\right\} \tag{3b}\]  
The correlations valid for the Bazaraa and Kurkur methods for bored piles are expressed in Equations (4a) and (4b) [4]. The \(N_{b}\) value in these equations is the average of the SPT-N values between 1D above and 3.75D below the pile base with the condition of \(N_{b}\leq 50\). \(N_{s}\): It is the average of the SPT N values along the relevant layer to be calculated. \(n_{b}\): 0.06-0.2 (average value was taken in the study); \(n_{s}\): 2-4 (average value was taken in the study).  
\[q_{b}(MPa)=n_{b}\cdot N_{b} \tag{4a}\] \[q_{s}(kPa)=n_{s}\cdot N_{s} \tag{4b}\]
## 4 Comparison with Literature Review  
In this study, optimal dosages of 5% CaCO\({}_{3}\), 0.75% nano-CaCO\({}_{3}\) and 2.0% GFCSs (or 1.5% GFCSs when combined with nano-CaCO\({}_{3}\)) were identified to enhance soil bearing capacity and reduce consolidation settlement. Nano-CaCO\({}_{3}\) demonstrated superior stabilization at lower dosages compared with conventional CaCO\({}_{3}\), due to its higher chemical reactivity and better microstructural integration. The flexible fiber reinforcement system (GFCSs) improved soil tensile strength and load distribution, thereby reducing deformation and increasing structural durability. The combined application of nano-CaCO\({}_{3}\) and GFCSs produced a synergistic effect, improving soil performance more effectively while allowing for reduced additive amounts. Additionally, a minimum curing period of 14 days was found to be necessary to complete the hydration and stabilization reactions. A reduction in  
Figure 14: qu-s/D relationship for various curing durations.  
Figure 15: For various curing durations: (**a**) bearing capacity values, (**b**) bearing capacity ratios (BCRs).  
consolidation settlement by up to 6.8 times was achieved, indicating a significant decrease in deformation risk, especially in problematic clay soils.  
These findings align with previous studies reporting that nano-silica additions in the range of 0.7-3% improve soil microstructure, decrease compressibility and increase unconfined compressive strength [22; 23; 24; 25; 26; 27; 28; 29; 30]. Carbon nanotubes applied at 1-3% dosage have been reported to significantly enhance soil compressive strength; due to being approximately 100 times stronger and 6 times lighter than steel, they can improve soil strength by up to 100% [31; 32]. Similarly, the use of nano-bentonite and nano-laponite in proportions between 1 and 10% has been documented to reduce liquefaction potential and increase strength [33; 34; 35; 36]. Moreover, the addition of 1.5% nano-clay (montmorillonite) has been shown to notably improve unconfined compressive strength and California Bearing Ratio (CBR) values [37].  
Notably, this study contributes to the literature by demonstrating that the combined use of nano-CaCO\({}_{3}\) and GFCSs not only enhances compressive strength but also improves tensile strength and load distribution, providing more comprehensive soil performance enhancement. The synergistic effect observed notably reduces consolidation settlement, an aspect rarely emphasized in previous research. The emphasis on curing time is consistent with prior studies highlighting the necessity of a sufficient duration for the completion of stabilization reactions involving nanomaterials [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37].  
In conclusion, this research supports the effectiveness of nanomaterials in soil improvement and offers a novel and comprehensive perspective through the multifunctional enhancements achieved by the combination of nano-CaCO\({}_{3}\) and GFCSs.