esa-sceva/satcom-chunk-collection huggingface dataset:
# On Smartphone Power Consumption in Acoustic Environment Monitoring Applications
## 4 Power Optimization Techniques  
Our experimental results show that in background audio monitoring applications, the major impact on smartphone power consumption is caused by the core audio recording functionality. Therefore, in applications that perform continuous microphone sensing, optimization of digital audio signal processing algorithms often does not provide any noticeable battery-life savings.  
The power-saving strategies that can be used in such scenarios are application specific. In most cases, the only viable power-saving approach is to perform the sound capture at certain moments (i.e., rely on non-continuous audio recording). For example, such an approach has been explored in [19], where the sound capture was triggered by events initially recognized by the accelerometer.  
On the other hand, in TV audience measurement applications (see, for example, [9; 10]), the audio recording can be performed periodically at short time intervals (e.g., 10-20 s) followed by longer intervals of microphone inactivity (e.g., 20-90 s). The interval of inactivity can be adaptively selected to match the TV watching profile of the individual user; this concept is schematically illustrated in Figure 8. Our experiments indicate that the TV audience measurement applications, which rely on such an adaptive mechanism, may reduce the average power consumption by a factor of 2-3 without significant impact on the accuracy of the measurement results.  
The interval of inactivity may also be varied adaptively based on the audio features extracted during previous microphone sensing periods. For example, in noise exposure monitoring applications [1; 3; 4; 5], the interval of microphone inactivity could be inversely proportional to the mean and variance of sound pressure level estimates obtained during previous sound capturing intervals. Nonetheless, it should be noted that in some applications, such as healthcare applications [6; 7; 8], it is vital to perform continuous audio recording and signal processing; in this case, the only practical solution is to use smartphone models with low or moderate power consumption in audio recording mode.  
Figure 8: Adaptation of intervals between audio capturing events to the TV viewing profile of a particular user in the TV audience measurement application (real-world example).
# Camouflage Backdoor Attack against Pedestrian Detection
## 3 Methodology
### Concepts and Attack Goal  
To describe the process of a camouflage backdoor attack, we define the following several conceptual objects involved in one attack, as shown in Table 1. The goal of the camouflage backdoor attack is to create a highly deceptive effect through images, to the extent that it remains imperceptible to the human eye. Our approach assumes that adversaries can manipulate only a subset of legitimate images to create a poisoned training dataset, without control over other training components like the model structure. This type of attack can occur in various scenarios where the training process is not fully controlled, such as when using third-party data, computing platforms, or models [29; 30].  
Figure 1: The main pipeline of the camouflage backdoor attack. Our attack method consists of three main stages. In the first stage (poisoning stage), we extract pedestrian images from the dataset and embed triggers within them. Subsequently, by applying image scaling techniques, we blend attack images with benign images to generate nearly imperceptible poisoned images. In the second stage (training stage), we train pedestrian detection using a combination of the generated poisoned images and the remaining benign images to obtain a poisoning detection model. In the final stage (inference stage), the adversary has the capability to activate embedded backdoors in order to bypass pedestrian detection (for instance, in our example, the ‘person’ with a specific trigger escapes the detection of the pedestrian detection model) by adding trigger patterns.  
Let \(\mathcal{D}\) represent the benign images, \(\mathcal{D}=\{(\mathbf{x}_{i},\mathbf{a}_{i})\}_{i=1}^{N}\), where \(\mathbf{x}_{i}\in\mathcal{X}\) is the image of a pedestrian and \(\mathbf{a}_{i}\in\mathcal{A}\) is the ground-truth annotation of the pedestrian \(\mathbf{x}_{i}\). For each annotation \(\mathbf{a}_{i}\), we have \(\mathbf{a}_{i}=[c_{i},\mathbf{x}_{i},\mathbf{y}_{i},w_{i},h_{i}]\), where \(c_{i}\) denotes the 'person' class to which the object within the bounding box is assigned, \((\hat{x}_{i},\hat{y}_{i})\) is the center coordinates of the pedestrian in the image, \(w_{i}\) is the width of the bounding box, and \(h_{i}\) is the height of the bounding box. In pedestrian detection tasks, given the dataset \(\mathcal{D}\), users can utilize dataset \(\mathcal{D}\) to train their pedestrian detector \(f_{\mathbf{w}}:\mathcal{X}\rightarrow\mathcal{A}\) by \(\min_{\mathbf{w}}\sum_{(\mathbf{x},\mathbf{a})\in\mathcal{D}}\mathcal{L}(f(\mathbf{x}),\mathbf{a})\), where \(\mathcal{L}\) is the loss function. In contrast to the standard pedestrian detection task, our objective during the inference stage is to enable adversaries to circumvent pedestrian detection by adding trigger patterns with any pedestrian image \(x^{\prime}\).
# Target Classification Using Frontal Images Measured by 77 GHz FMCW Radar through DCNN
## 4 Radar Signal Processing  
The parameters of the FMCW radar were set as follows. The number of chirps per frame was 64, the frame period was 200 msec, and the total measurement duration was 5 s, resulting in total chirps recorded of 64 \(\times\) 25 = 1600 for each target measurement. Each chirp produced a target frontal image. However, we extracted only 2 chirps randomly per frame because the images are very similar within the frame, as the time duration is only a couple of microseconds. Therefore, the total extracted images per target kind were 2 \(\times\) 25 \(\times\) 50 = 2500.  
The received FMCW signals were processed to construct the radar frontal images. The physical 192 channels are distributed to have a nonlinear array as shown in Figure 2. As channels exist in 31 \(\times\) 33 dimensions, a total of 1023 (31 \(\times\) 33) virtual receiving channels were regarded to have their own down-converted signals in the time domain. The time-domain signal was transformed to the frequency domain through Fourier transformation, which serves as a range profile as presented in Figure 4 as an example. In the range profile, a target is detected based on the simple threshold method. However, as the target occupies several bins in the range profile, the determination of the threshold is critical as it affects the quality of the radar frontal image. The complex value (phasor) of the individual point detected in the range profile from each channel was extracted and 2-dimensional Fourier-transformed was executed to construct the frontal image. Accordingly, when there are multiple points detected in the range profile, the total image should be synthesized by overlapping several frontal images. This process is demonstrated in Figure 4.  
When the threshold is too high, very few points will contribute to producing the image, as in Figure 5a. In this case, the threshold was 3/5 of the peak. As the threshold decreases, many points in the range profile contribute to a single frontal image, which can cause noise issues. The impact on the low threshold is presented in Figure 5b. The figure indicates that several points should be involved in the construction of a target's detailed characteristics. We heuristically set the threshold at a 1/3 of the peak because it provides reasonable characteristics for frontal images. The measured frontal images for each target are presented in Figure 6.  
Figure 4: The process of the frontal image synthesizing from multiple target points in the range profile.  
Figure 5: Measured radar frontal image depending on threshold: (**a**) 3/5 of the peak threshold; (**b**) 1/3 of the peak threshold.  
Figure 6: \(Cont\).  
Figure 6: Measured range profiles and frontal images: (**a**) sedan; (**b**) truck; (**c**) van; (**d**) tree; (**e**) pole; and (**f**) human.
# Green and Efficient Processing of Wood with Supercritical CO2: A Review
## 3 Wood Impregnation  
Wood impregnation is a process to force wood modifying agents--preservatives, fire retardant, and dye--into wood structures by increasing pressure in the vessel [49]. Wood impregnation with scCO\({}_{2}\) is more efficient compared with conventional wood impregnation [50; 51; 52; 53]. Applications of wood impregnation with scCO\({}_{2}\) that are summarized in this section include wood preservation with biocides, wood inflaming retarding, wood dyeing, and wood acetylation. An overview of wood impregnation application with scCO\({}_{2}\) shows in Table 2.  
The liquid permeability of wood can be improved with scCO\({}_{2}\) treatment, which is one of reasons for impregnation efficiency improving under this method. Wood liquid permeability is a physical property that describes the fluid through the connected voids of wood structure under a static or capillary pressure gradient [54]. The high permeability of wood can increases the migration rate of liquid in wood [50]. This was first demonstrated in 1995 using scCO\({}_{2}\)--either alone or with entrainment (methanol)--and was used to improve the permeability of Douglas-fir (_Psuedotsuga menzisei_ ([PERSON]) heartwood samples [55]. The high-pressure difference of scCO\({}_{2}\) treatment will destroy the wood structure and form new water and other liquid transfer channels, thus increasing the liquid flow in the wood. [PERSON] et al. [56] pretreated sugi (_Cryptomeria japonica_ D. Don) with scCO\({}_{2}\) and showed that pits were broken in the pretreated sample with a scanning electron microscope (SEM), which created new water and other liquid transfer channels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Liquid** & **Gas** & **Supercritical Fluid** \\ \hline Density/g\(\cdot\)cm\({}^{-3}\) & 0.6–1.6 & 0.006–0.02 & 0.2–0.5 \\ Viscosity/10\({}^{-4}\) g\(\cdot\)cm\({}^{-3}\cdot\)s\({}^{-1}\) & 20–300 & 1–3 & 1–3 \\ Diffusivity/ cm\({}^{2}\cdot\)s\({}^{-1}\) & (0.2–2) \(\times\) 10\({}^{-3}\) & 0.1–0.4 & 0.7 \(\times\) 10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dissolving and transfer characteristics of supercritical fluid compared with liquid and gas.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application** & **Environment Benefits with** & **Efficiency with scCO\({}_{2}\)** & **References** \\  & **scCO\({}_{2}\)** & **The higher insecticide retention value** & \\ Wood preservation with biocide & & Better protection & [50; 57; 58; 59] \\  & & Fast treatment & \\  & & Fire-resistant additives (non-polar & \\ Flame retardancy of wood & No VOCs emission & compound) have appreciable solubility & [60] \\  & No metal components & in scCO\({}_{2}\) & \\  & Non-toxic solvent & Facilitating dye uptake at moderate temperatures & \\ Wood dyeing & & Improving recycling rates of both CO\({}_{2}\) & [25; 61] \\  & & & and dyes \\ Wood acetylation & & Acetylation reagent penetrate into the core of wood & [62] \\ \hline \hline \end{tabular}
\end{table}
Table 2: An overview of wood impregnation application with scCO\({}_{2}\).
# Combining Higher-Order Statistics and Array Techniques to Pick Low-Energy P-Seismic Arrivals
## 3 Methodology
### Multi-Channel Stage
If the onsets are all grouped in the same cluster, we accept them in the case \(N_{i}\geq 3\); otherwise, they are discarded. If more than one cluster is present, we denote with \(n_{1}\) and \(n_{2}\) the numbers of onsets present in the two most populated clusters, \(C_{1}\) and \(C_{2}\). We accept the onsets of \(C_{1}\) (and reject the other) if all the conditions of Equation (7) are met (// indicates the integer division). \[ \begin{split} n_{1}&\geq 3\\ n_{1}&\geq N_{i}//2\\ n_{1}&\geq 2n_{2}.\end{split}  \tag{7}\]  
In this case, we claim that \(C_{1}\) represents a \"predominant cluster\". If any of the conditions of Equation (7) are not met, no predominant cluster can be defined, indicating very spread arrivals (and, thus, large uncertainty in the solutions). In this case, all onsets of the array are rejected.
## 3 Materials and Methods
### Feature Extraction Methods
#### 3.3.2 Frequency Domain  
Frequency domain methods transform the EEG signals from the time domain to the frequency domain for analysis. The acquired spectrum is typically decomposed into five sub-bands, delta, theta, alpha, beta, and gamma [4], for feature extraction and the subsequent analysis of EEG signals. The functions implemented from this domain in the present work are the spectral entropy, power ratio, power spectral density, and fast Fourier transform. This domain provides insights into the power spectral density or energy distribution across various frequency bands. Each frequency band is associated with different cognitive states and emotional processes.
## 4 Functionality Demonstration of the Mission Design Configurations in the ISL Network  
Figure 6 shows a simplified test bed configuration used to demonstrate SS transmission, 1 PPS time stamping, and frequency reconfiguration between two satellite payloads in an ISL network. Currently, the ISL network operates as a half-duplex system. Also, the satellites operate in two modes for the transmission and reception of SS signals. In Figure 6, Sat \(A\) is in transmission mode and Sat \(B\) is in reception mode. In addition to the system design configuration in Figure 4, a delay pulse generator was incorporated at the receiving payload to simulate propagation delay (\(\delta T\)) due to the _TWVC_ and _TEC_ in orbit. As a result, the system functionality of deducing atmospheric water vapor and total electron content delay was demonstrated as explained in Section 4.3. Additionally, 30 dB attenuators were used instead of power amplifiers to decrease the transmitted signal power in order to prevent receiving SDR damage or interference from other communication networks. A personal computer (PC) was utilized on one end of the network to serve as the generic satellite bus system for monitoring and acquiring processed data.
## 1 Introduction
However, the solar radiation intensity has not been taken into account in recent prediction models [21; 22; 31], which has resulted in there being very limited physiological information in the currently available continuous OCO-2 SIF products. The aims of this study were: (1) to investigate whether the solar radiation intensity, approximated by the cosine value of the solar zenith angle at noon, can provide good physiological information of SIF; (2) to generate global, continuous TanSat SIF products with a spatial resolution of 0.05\({}^{\circ}\); and (3) to investigate the accuracy of spatially continuous TanSat SIF products.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 4 TSN Standards for Automotive Communications
### Timing and Synchronization  
The IEEE 802.1 AS-2011 standard of the AVB family provides synchronization with microsecond accuracy and handles loss of the active grandmaster electing a new one. However, the handover procedure provided in IEEE 802.1 AS-2011 is not very fast and might cause time jumps at some nodes. For this reason, its revision, called IEEE 802.1 AS-2020, supports multiple synchronized grandmasters and multiple synchronization paths to enable seamless low-latency handover and a quick recovery of time synchronization in failure modes.
## 4 Proposed Framework
### Preliminaries
#### 4.1.2 Notations  
In the remaining part of this paper, we adopt the following notation related to the cryptographic operations.  
* \(H(m)\) (Hashed message): A message \(m\) hashed by a hash function \(H\), such as SHA-256, SHA3, RIPMD, Merkle-tree, etc.
* \(\{m\}_{k}\) (Symmetric-key encryption): A message \(m\) encrypted by the key \(k\) using a symmetric cryptographic system, such as AES, 3-DES, etc.
* \(\{m\}_{k_{a}}\) (Message encrypted with a public key): A message \(m\) encrypted with the public key \(k_{a}\) using an asymmetric cryptographic system, such as RSA, El-Gamal, etc.
* \(\{m\}_{k_{a}^{-1}}\) (A signed message): A message \(m\) signed with the private key \(k_{a}^{-1}\) using an asymmetric cryptographic system, such as RSA, DSA, El-Gamal, etc.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 5 Conclusions
### Contextual Adaptation of Model Parameters  
The model parameters in this study were empirically determined, ensuring objectivity and reliability. While certain geographic parameters (e.g., slope, catchment area, and land use) are region-specific and may limit direct applicability to other areas, the dataset used remains highly relevant for Nanchang, serving as a useful reference for urban flood modeling in this region. If applying this methodology elsewhere, sensitivity analyses should be adapted to local conditions to ensure model accuracy and reliability. This highlights the need for context-specific parameter calibration in hydrological modeling to improve adaptability across different urban settings.
## 3 Results and Discussion  
### Modification of the Graphite Felt Electrode for Amide-Coupling  
The graphite materials were used as the substrate to sustain direct supplementation of electrons to the bacteria [15; 16]. Electron uptake from extracellular sources is mainly dependent on direct cell contact via a biofilm on the cathode surface [17]. Modification of cathode materials by decorating with functional groups and increasing surface hydrophilicity might benefit the adhesion and growth of bacterial biofilm [1]. Generally, negatively charged functional groups on the bacterial surface are responsible for the electrostatic binding of cationic groups [18]. However, the surface charge of pristine carbon-related materials is neutral [12]. To generate a positively charged electrode surface, aminopolyosaccharide chitosan polymer was used to coat the graphite felt surface by immersion. In addition, to induce covalent attachment of bacteria to the electrode surface, the chitosan-coated graphite felt was further modified with carbodiimide compound to provide robust amide functionality [19]. The possible chemical pathway of amide-coupling between chitosan and bacteria through the carbodiimide cross-linker is described in Figure 3 [20].  
To estimate bacterial adhesion to the modified electrodes, the zeta-potential of \(R\). _sphaeroides_ and powdered graphite felt electrodes were measured in a neutral environment (0.1 M sodium phosphate, pH 7.0) (Table 1). The zeta-potentials of both \(R\). _sphaeroides_ (\(-31.88\)\(\pm\) 3.31 mV) and bare graphite felt (\(-10.13\)\(\pm\) 2.72 mV) were negative. Meanwhile, the zeta-potential of the graphite felt surface was shifted to positive range by the modification with chitosan and the chitosan/carbodiimide compound. This suggests that the modified graphite felt may form a more rigid biofilm of \(R\). _sphaeroides_ than bare graphite felt due to improved bacterial adhesion [21].  
Figure 3: Reaction of the carbodiimide compound (EDC) with chitosan free amines (1,2). The intermediate is stabilized by the formation of sulfo-NHS (3) which undergoes nucleophilic substitution to form an amide bond with the carboxyl groups on the bacteria (4).
## 3 Results and Discussions
In the case of the influence of the temperature on the AC and RI, we remark that, when the temperature decreases, the curves of AC shift to the higher terahertz frequencies and the peak intensities of AC are reduced (Figure 3a). Indeed, our calculations show that the temperature induces a variation of the electron and hole effective masses and of the band gap energy of \(GaAs\), leading to a decrease in energies of the uncorrelated electron-hole-ionized donor, and consequently the energy transition (\(E_{fi}\)) decreases. Also we remark that, as the AC situation, the RI (Figure 3b) deeply depend on the temperature; the peaks of RI move to the lower energies region when the temperature increases. We will finish our investigation with a very important study which is the comparison between the optical responses of the impurity (\(D^{0}\)), the exciton (\(X\)) and the (\(D^{+},X\)) complex. This study is very helpful for the identification of the optical peaks for different materials. To do that, we have followed the same procedures taken to calculate the energies of (\(D^{+},X\)) complex, by choosing the wave functions given in background theory section (expressions (15)-(18)) corresponding to the exciton and the impurity respectively. In Figure 4a, the absorption coefficients (AC) against the incident photon energy, of the exciton, the impurity and the complex are presented. As we can see, the optical peaks of (\(D^{+},X\)) complex are located between the exciton peaks (high energy region) and donor impurity (low energy region) i.e the interaction of the donor with the exciton, causes a shift toward THz low frequencies compared to the exciton and a shift to THz higher frequencies compared to the impurity. This behavior is explained by the transition energy difference (\(E_{fe}=E_{1p}-E_{1s}\)) where: \(E_{fe}^{D^{0}}<E_{fe}^{(D^{+},X)}<E_{fe}^{X}\). Figure 4b indicates the variation of the refractive index of \(X\), (\(D^{+},X\)) and impurity as a function of the energy of incident photon. We notice that the RI behaves in the same way ((\(D^{+},X\)) complex RI peaks are located between those of exciton and donor impurity) as in the case of AC (Figure 4a).
## 2 Materials and Methods
### Preparation of Test Materials and Control Solutions  
EEP extracts were prepared based on the method outlined by [PERSON] et al. [25] and [PERSON] and [PERSON] [32]. The study used the same variety of Josephine pineapple (_Ananas comosus_ L. _var. comosus cv. Josephine_) and Navel orange (_Citrus sinesis_), both obtained from the same vendor. A mixture of fruit peels was prepared, consisting of 30 g of orange peels and 45 g of pineapple peels mixed in a 6:4 ratio. Then, EEP extract was prepared in triplicate by combining 75 g of fruit peels, 25 g of molasses and 250 mL of water in a 3:1:10 ratio, kept in airtight containers. The containers were stored in the same location at room temperature. The containers were opened for 10 s every day to release the trapped gas. The fermentation was carried out for 3 and 6 months. Each sample from different fermentation periods was prepared three times on different days to create three biological triplicates.  
At 3 months of fermentation, 3 months of EEP (3 M EEP) extract was sterile-filtered using a 0.22 \(\upmu\)m pore-size syringe and further diluted to a concentration of 100%, 50%, 25%, 12.5%, 6.25%, 3.13%, 1.57% and 0.78% with culture medium using two-fold serial dilution method. The same dilution method was repeated for EEP fermented for 6 months (6 M EEP). For the cytotoxicity test, different concentrations of EEP were freshly prepared just before conducting the test.  
The comparison group used in this experiment was 2.5% NaOCl, which is the gold standard for endodontic irrigants. It was prepared by diluting 5.25% NaOCl with distilled water to achieve the final concentration of 2.5% NaOCl. Different positive control was used for different cytotoxic tests conducted in this study. Briefly, 100% reduced Alamar Blue reagent and 10% dimethyl sulfoxide (DMSO) were used for the Alamar Blue and Live and Dead Cell assay, respectively. Culture media containing cells was used as an untreated negative control for both cytotoxic assays.