esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Small-Signal Modeling of the Distributed WEDES System
### Derivation of Transfer Functions  
Since the design parameters and equilibrium operation point of all WEDES battery modules are the same under steady-state operation (when SOC balancing is achieved), the derivation of transfer functions of all battery modules follows the same procedure. The detailed derivation of \(r\)th battery module is discussed as follows:
## 2 System Model Network
### Energy Harvesting (EH)  
The signal is transferred from the S to the R in the first-half _T_/2 and can be formulated as  
\[y_{r}=h(x_{s}+\mu_{s})+n_{r} \tag{1}\]  
The received RF signal at the input of the energy harvesting unit can be calculated as  
\[y_{h,r}=\sqrt{\rho}\times y_{r}=\sqrt{\rho}hx_{s}+\sqrt{\rho}h\mu_{s}+\sqrt{ \rho}n_{r} \tag{2}\]  
In this equation, \(x_{s}\) is the energy-transmitted signal with \(\mathrm{E}\left(\left|x_{s}\right|^{2}\right)=P_{s}\), \(n_{r}\) is the zero-mean additive white Gaussian noise (AWGN) with variance \(N_{0}\), and \(\mu_{s}\) denotes the distortion error caused by hardware impairment at the source node, which is modeled as a zero-mean Gaussian random variable with variance \(P_{s}\sigma_{1}{}^{2}\) with \(\mathrm{E}\left(\left|\mu_{s}\right|^{2}\right)=P_{s}\sigma_{1}{}^{2}\). Here \(\mathrm{E}\{.\}\) denotes the expectation operation.  
From Equation (2), the harvested energy at R in the first interval _T_/2 can be formulated as the following  
\[E_{h}=\eta\rho P_{s}|h|^{2}(T/2) \tag{3}\]  
Therefore, the transmitted power at R can be calculated as  
\[P_{r}=\frac{E_{h}}{T/2}=\frac{\eta\rho P_{s}|h|^{2}(T/2)}{T/2}=\eta\rho P_{s} |h|^{2} \tag{4}\]  
where \(\eta\) is denoted the energy conversion efficiency of the proposed system.
# _Applied Sciences_
###### Abstract  
Deep learning (DL)-based algorithms have demonstrated remarkable results in potentially improving the performance and the efficiency of healthcare applications. Since the data typically needs to leave the healthcare facility for performing model training and inference, e.g., in a cloud based solution, privacy concerns have been raised. As a result, the demand for privacy-preserving techniques that enable DL model training and inference on secured data has significantly grown. We propose an image obfuscation algorithm that combines a variational autoencoder (VAE) with random non-bijective pixel intensity mapping to protect the content of medical images, which are subsequently employed in the development of DL-based solutions. A binary classifier is trained on secured coronary angiographic frames to evaluate the utility of obfuscated images in the context of model training. Two possible attack configurations are considered to assess the security level against artificial intelligence (AI)-based reconstruction attempts. Similarity metrics are employed to quantify the security against human perception (structural similarity index measure and peak signal-to-noise-ratio). Furthermore, expert readers performed a visual assessment to determine to what extent the reconstructed images are protected against human perception. The proposed algorithm successfully enables DL model training on obfuscated images with no significant computational overhead while ensuring protection against human eye perception and AI-based reconstruction attacks. Regardless of the threat actor's prior knowledge of the target content, the coronary vessels cannot be entirely recovered through an AI-based attack. Although a drop in accuracy can be observed when the classifier is trained on obfuscated images, the performance is deemed satisfactory in the context of a privacy-accuracy trade-off.  
image obfuscation; deep learning; medical imaging; privacy preserving classification +
Footnote †: journal: _Applied Sciences_
## Materials and Methods
### Bottom-Up Synthesis of Carbon Dots  
Four different types of carbon dots were prepared by microwave-assisted hydrothermolysis (750-800 W, 10 min; ETHOS 1600 equipped with PRO 24 rotor system, EMLS, Leutkirch, Germany), using three different carbon sources (glycerol, G; citric acid, CA; lemon juice, L) and two different nitrogenous precursor compounds (urea, U; EDEA, EA). At the example of GEA-type CDs, i.e., CDs obtained from glycerol and EDEA, the impact of varying mass ratios of carbon to nitrogen source materials (corresponding to molar C/N ratios of about 6.7 to 12.9) on PL properties was studied.  
_GEA-type CDs:_ Synthesis from glycerol and 2,2-(ethylenedioxy)-bis-(ethylamine) (EDEA) as published elsewhere [34]. In brief, 5 mL of glycerol (68.4 mmol) and 2 mL of phosphate buffered saline (PBS) were mixed with 2, 3, 4, or 5 mL of EDEA (13.7-34.2 mmol), respectively (cf. above). After transferring the solution to a 25 mL round-bottom flask, the latter was connected to a reflux condenser and placed inside the microwave. Hydrothermolysis was conducted using the above conditions. After cooling (30 min) the obtained dispersion of solid thermolysis products was centrifuged at 20,000 rpm (10 min, 4 \({}^{\circ}\)C). The supernatant was filtered through a 200 nm filter and subjected to dialysis against deionized water for three days using an 1 kDa membrane. The resulting dispersion of carbon dots was then lyophilized, re-dispersed in DI water to arrive at a concentration of 400 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) (10 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) for AFM) and stored at 4 \({}^{\circ}\)C.  
_LEA-type CDs:_ Following an approach for synthesis of CDs from orange juice [35] but using cleared lemon juice [36] (5 mL) instead, EDEA (3 mL) as a nitrogen source both co-dissolved in 2 mL PBS (10 mM). Lemon juice was prepared by squeezing halved lemons using household lemon press. Filtration and subsequent centrifugation (5000 rpm, 15 min) yielded a clear extract which was stored at 4 \({}^{\circ}\)C until further use. Reaction conditions and work-up procedure were as described above.  
_LU-type CDs:_ Experimental procedure as described for LEA-CDs however using 10 mL of lemon juice and 0.5 g urea [37] as nitrogenous precursor instead of EDEA.  
_CAU-type CDs:_ Citric acid (1.0 g) and different amounts of urea (0.5, 1.0 g) were dissolved in 10 mL of ultrapure water and subjected to microwave-assisted co-thermolysis, subsequent isolation, and purification as described above. Since addition of the higher amount of urea (1.0 g) resulted in strongly agglomerated chunks, only CAU-type CDs obtained from citric acid to urea mass ratios of 2:1 were used in this study.
## 2 Materials and Methods  
### Patient Selection  
The study was prospectively performed on patients who underwent endoscopic reduction of medial orbital wall fractures in the Maxillofacial Surgery Unit of the University Federico II of Naples from January 2019 to February 2022. The study was conducted following the principles of the Declaration of Helsinki. Written informed consent was obtained from each patient after submission along with the approval of the Ethics Committee of the University, with protocol number 370/2019. Patients with the following inclusion criteria were enrolled:  
* Patients age > 18 years old;
* Patients with a medial orbital wall fracture, shown by coronal and axial CT scans, not involving the inferomedial angle of the orbit;
* Patients with combined orbital walls fractures, without the involvement of zygomatic complex;
* Patients with fractured bony fragments displaced;
* Patients who did not require urgent surgery;
* Patients treated only by endoscopic approach;
* Patients with functional defects (diplopia, eye motility limitation, enophthalmos).  
A total of 48 patients with a diagnosis of orbital medial wall fracture were considered: 13 of them were excluded because of the inferomedial angle involvement; 3 were not selected due to the absence of functional defects and no surgical indications. One was excluded due to a previous skin location. A total of 31 patients were included in the study according to the above inclusion criteria. The 31 patients were divided in two groups: (A) 14 patients treated between January 2016 and November 2018, when only anterior ethmoidectomy was performed; (B) 17 patients treated between December 2018 and December 2021, after the introduction of anteroposterior ethmoidectomy in the surgical procedures. For all of the 31 patients, the same 3D digital workflow and analysis was performed. The primary purpose was to prove that the anteroposterior ethmoidectomy surgical procedure improved the view of the operative field, obtaining a better reduction of the fracture, particularly in the posterior portion. The second purpose was to demonstrate that this procedure does not reduce the stability of the medial orbital wall over time. Furthermore, the most recent retrospective studies on this topic in the literature from 2010 were analyzed to obtain a brief review.
## 2 Materials and Methods
### Measuring Pressure in Heel Area  
To measure the burden of its own weight, we used three force sensing resistors (FSR 402, Interlink Electronics) which are attached to the calcaneal area of the feet with double tape. Determination of the location of each sensor was performed using an unloaded foot scan around the area that was estimated to receive a large burden when standing, as shown in Figure 2a. For this purpose, sensor 2 is placed at CH (0.15 FL) [29] and sensors 1 and 3 are placed arbitrarily at the lateral heel (LH) and medial heel (MH), in line above sensor 2, under the boundary between the heel and mid foot area (0.31 FL) [30], as shown in Figure 4.  
Figure 3: (**a**) Flowchart evaluation of n% insole foot area; (**b**) Table of 3D coordinates (x, y, and z axis) generated from AutoCAD with the visualization of the increment of x- and y-axis.  
The FSR is a polymer thick-film device which exhibits a decrease in resistance with an increase in the force applied to the active surface. Its force sensitivity is optimized for use in human touch control of electronic devices. FSRs are not a load cell or strain gauge, though they have similar properties. The FSR 402 has active area of 0.5\({}^{\prime\prime}\) (12.7 mm) diameter, the nominal thickness of 0.018\({}^{\prime\prime}\) (0.46 mm), force sensitivity range of 100 g to 10 kg, and pressure sensitivity range of 1.5 to 150 psi. The relationship between the load L (grams) and the voltage V (volts) can be expressed by polynomial regression, as presented in Equation (3). Each sensor is connected to one resistor. The output voltage of the FSR sensor is read by the Arduino MEGA 2560 microcontroller using 10 pin analog input bits [31]. Then, the voltage is sent to DAQ software LabVIEW via serial USB to be converted into load using Equation (3).  
\[\text{L}=927.7\ \text{V}^{3}-1643.9\ \text{V}^{2}+1083.5\ \text{V}-31.02 \tag{3}\]  
Each patient was requested to stand upright using the appropriate test shoe size for each shoe insole area. The outside material is made of Microcell Puff EVA foam and the insole made of Poron cushioning [32]. Initially, each patient is requested to wear the shoe with 0% A, and three pressure datapoints from CH, MH and LH areas are recorded. A similar procedure is carried out for the 25% A, 50% A and 100% A, and the results are compared to the value of PMCP which is measured using an alogometer. The patient will feel pain if the pressure in the heel region is greater than the PMCP.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 4 Proposed Method  
This section presents the details of the proposed method, and Figure 2 illustrates its main processing procedures. On the senders' side, to begin with, a chaotic matrix is generated, which acts as the CS measurement matrix. At the same time, a concealing matrix is generated according to the critical sections of the original signal. Next, CS is processed in order to accomplish data compression, encryption, and critical information concealment through a single step. Last, the encrypted and concealed signal is masked by a chaotic matrix for the preparation of transmission through an open or insecure channel. On the receivers' side, reconstruction procedures are processed by restricted-authorized receivers and full-authorized receivers separately. Restricted-authorized receivers who merely possess key A can obtain signals with critical sections concealed. Full-authorizedreceivers who possess both key A and key H can achieve the entire original signals that contain critical information. In addition, it should be noted that eavesdroppers could obtain little useful information, even if they manage to catch the processed signals by some malicious means.  
As shown in Figure 2, there are four roles in the proposed method.  
* Senders process data compression, encryption, and concealment procedures based on CS theory and then mask the processed data to be transmitted. A typical sender in the proposed method could be a sensor within BBNs, which is possibly mobile electronic equipment linked to local area networks (LANs) or wide area networks (WANs).
* Receivers with restricted authorization only possess key A, that is to say, they could merely reconstruct the portions of the original signals without critical information.
* Receivers with full authorization possess both key A and key H, in other words, they could realize the original signals that contain critical information after the reconstruction procedure.  
Characteristically, receivers in the proposed method could be sinks or fusion nodes within BBNs or servers that may be deployed in data centers, etc.
* Eavesdroppers may listen to the transmission channels for the purpose of catching available information. Attackers between senders and receivers, for example, are likely to intercept network flows and obtain all or just part of transmitted data.  
The details of operations are as follows.
## 3 Results and Discussion
### Superhydrophobic Property and Environmental Durability
Figure 8b shows the variation of CAs after ultrasonic washing the superhydrophobic HNTs samples with 60 min. These values were all above 150\({}^{\circ}\) at different ultrasonic times, indicating that the loaded PDA layers had strong adhesive properties, just like the mussel byssuses. The outdoor durability tests were carried out by exposing the superhydrophobic HNTs samples to sunlight, and the CAs results were recorded at 24 h interval as presented in Figure 8c. Apparently, all of the values remained at about 150\({}^{\circ}\) after solar radiation, suggesting that the samples had excellent stability to endure the harsh outdoor environment. To sum up, diverse superhydrophobic surfaces have been successfully constructed on various substrates. However, many of them had poor environment durability, especially in ultrasonic washing, organic solvents, and strong acidic/alkaline solutions, which limited their practical applications [51; 52]. In this study, this bio-inspired modification method can create a stable and robust superhydrophobic structure on HNTs surfaces, which can conquer various harsh conditions. Figure 6: Micrographs of 3 \(\mu\)L deionized water droplets on the plane surfaces of **(a)** P-HNTs; **(b)** PDA-HNTs; **(c)** VM/PDA-HNTs; **(d)** ODA-HNTs; and **(e)** VM/ODA-HNTs; 10 \(\mu\)L deionized water droplets on the incline surfaces of **(f)** VM/ODA-HNTs.
## 3 The Proposed SP-IoUAV Model
### Privacy-Preserving Ensemble  
The privacy-preserving ensembles in the proposed model are crucial components suggested for securing sensitive data during intrusion detection and prevention processes. In this collaborative approach, the IDS can take advantage of the collective intelligence of UAVs while maintaining data privacy. The ensemble integrates machine learning models from various UAVs while maintaining the privacy of individual data points. A brief description of each technique follows.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 7 Conclusions  
### Summary of the Review's Key Findings  
This comprehensive review of Dense Video Captioning (DVC) has unveiled several pivotal findings that encapsulate the present state of the field. Firstly, DVC marks a substantial leap in video understanding, effectively bridging the gap between video recognition and natural language generation. The evolution of DVC techniques has been remarkable, transitioning from early methods that heavily relied on hand-crafted features and template-based generation to recent approaches that harness the power of deep learning, particularly transformer-based architectures, resulting in significant performance enhancements.  
Secondly, the availability of diverse and large-scale datasets has played a crucial role in propelling advancements in DVC. Datasets like ActivityNet Captions, YouTube2 Text, and Charades have provided essential annotations for training and evaluating DVC models, enabling researchers to benchmark their methods against state-of-the-art performances. These datasets encompass a broad spectrum of video content and include temporal annotations for precise event localization within videos, which is vital for generating accurate captions.  
Thirdly, the integration of multimodal information has emerged as a prominent trend in DVC research. By amalgamating visual, auditory, and textual cues, models can gain a deeper understanding of video content and generate more informative and contextually relevant captions. This multimodal fusion can occur at various stages, including early fusion (combining features prior to encoding), late fusion (combining predictions after decoding), and hybrid approaches that utilize both strategies.  
Lastly, the review underscores the challenges and limitations inherent in current DVC techniques. These encompass the complexity of video content, which may feature multiple overlapping events and background noise, as well as the inherent ambiguity in natural language, leading to multiple plausible captions for the same video segment. Furthermore, the computational demands of deep learning models and the necessity for substantial amounts of labeled data remain significant obstacles to the widespread adoption of DVC technology.
## 2 Materials and Methods
### Variability-Based Sampling and Bisection Method  
The extracted boundaries from image segmentation correspond to a uniformly distributed data set that shows the edge profile. The data can be used for the Gaussian process model. However, the number of data points in the set is proportional to the number of hyperparameters to be fitted, and thus an efficient sampling of the data set is required to reduce the computational cost and improve the convergence rate. To determine the sampling locations along the profile \(y(x)\), the local variability, \(\sigma_{y}^{2}\), is defined to calculate the probability of sampling at each location, \(\Pr(x)\), as follows [24,25]:  
\[\Pr(x)\propto\sigma_{y}^{2}(x)=\frac{1}{n_{x}-1}\sum_{x^{\prime}\in R(x)} \biggl{(}\frac{y(x^{\prime})-\overline{y}(x)}{(x^{\prime}-x)}\biggr{)}^{2}, \tag{4}\]  
in which \(x^{\prime}\) denotes a location within the region around \(x\), \(\overline{y}\) denotes the local mean of the profile values in the region \(R(x)\), and \(n_{x}\) denotes the number of measurement locations in the region. The numerator, \((y(x^{\prime})-\overline{y}(x))\), measures the geometrical distance between \(x\) and \(x^{\prime}\) and the denominator, \((x^{\prime}-x)\), makes \(x^{\prime}\) near the \(x\) more influential to the variability. \(1/(n_{x}-1)\) averages the calculated variability. The intent behind the variability-based sampling is that the regions in which the profile varies more frequently should include more sampling locations, and thus the details within the region can be captured properly. The probability distribution for sampling as defined in Equation (4) is used, and there is a higher probability of selecting the sampling locations with higher local variability as the initial measurements.  
In addition to the variability-based sampling, we also employ an iterative bisection algorithm as follows: if a certain region between two selected measurement locations exhibits a high deviation between the estimated mean and the mean from realizations, then we add an additional measurement point in the middle of the region to improve the match. The process is repeated until the deviations in the entire region are lower than the threshold value. The bisection method ensures that every detail that might be missed in the variability-based sampling is included in the sampling domain.
## 2 Materials and Methods
### CAE Artificial Neural Network  
ANNs excel in handling very complex and highly non-linear relationships among different materials' (i.e., aluminum alloys') parameters, making them well-suited for modeling the intricate interactions between different alloying elements, processing conditions, and resulting material properties. By leveraging ANNs, researchers and practicing engineers can predict the mechanical, thermal, and corrosive properties of aluminum alloys, aiding in the design of alloys with tailored characteristics for specific applications.  
The basic mathematical model, which suits ANNs, presented in this article is derived from the observed and/or measured data and represents a special class of mathematical models, so-called _empirical mathematical models_ in contrast to _abstract mathematical models_[19]. A detailed explanation is given in [5; 20]. This proposed model can produce different kinds of information while describing the phenomenon under consideration. These are the following:  
* CAE ANN predicts unknown (\(k\)-th) output parameter \(\hat{y}\) as a mean value by using the general formula [5]: \[\hat{y}_{k}=\sum_{n=1}^{N}A_{n}\cdot y_{nk},\] (1) where \(A_{n}\) and \(a_{i}\) are determined as: \[A_{n}=\frac{a_{n}}{\sum_{i=1}^{N}a_{i}}\] (2) \[a_{n}=\frac{exp\left(-\sum_{l=1}^{D}\frac{\left(n_{l}-x_{nl} \right)^{2}}{2w_{nl}^{2}}\right)}{\left(2\pi\right)^{D}/2\pi\
u_{l=1}^{D}w_{nl}}\] (3)  
In the equations above \(y_{nk}\) is the \(k\)-th output parameter of the \(n\)-th model vector (sample) from the database of the observed phenomenon. Similarly, \(x_{nl}\) is the \(l\)-th input parameter of the \(n\)-th model vector from the same database, and \(D\) is the number of input parameters in the observed phenomenon, which are taken into account in the prediction. Different values of \(w_{nl}\) correspond to the \(l\)-th input parameter for each model vector from the database (the analyses in this article all employed the parameters \(w_{min}\) = 0.15 and \(w_{max}\) = 0.25). In this case, \(w_{min}\) and \(w_{max}\) refer to values that change linearly along sections of the problem space in the directions of each (\(l\)-th) input parameter and are described in detail in the appendix of the article [20]. The details of the entire procedure are described in more detail in [5]. From the above equations, we can see the structure of the CAN ANN, which, compared to the BP (Backpropagation) ANN, has a known structure for all the considered problems.  
Figure 2: The microstructure of the extruded round profile of 6082 aluminum alloy: on the edge of extruded profile (**a**) and in the center of the extruded profile (**b**).  
* The local standard error (also, standard deviation) of the prediction is calculated by using the following formula [35]: \[\hat{E}_{\
u k}=\sqrt{\sum_{n=1}^{N}A_{n}(\hat{y}_{k}-y_{nk})^{2}}.\] (4)
* An estimation of the reliability of the predicted mean value based on data density is calculated by using the following formula [5]: \[\hat{\rho}=\frac{1}{N}\sum_{i=1}^{N}a_{i}\] (5)