esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Digital Forensic Readiness Model for Shadow IoT Devices
## 2 Background
### Risks of Shadow IoT Devices and the Requirements for Digital Forensic Readiness  
According to the ISO/IEC 27043 standard [17], the need for digital forensic readiness is four-fold [12; 18]. The risk shadow IoT devices poses to the enterprise network necessitates the need for shadow IoT DFR.  
* Managing gateway security connections: In a situation where a shadow IoT device joins an enterprise network through an external communication mode such as WiFi or Bluetooth, it will be very difficult for the security gateway to control and manage this connection. Should the IoT device be vulnerable in nature, it could pose a gross impending danger to the network if an adversary leverages on a known vulnerability to sniff out and obtain sensitive data from the enterprise network. It then becomes highly imperative for organizations to proactively employ DFR to identify and gather potential digital evidence. Due to the high risk associated with the presence of shadow IoT devices in the enterprise network, neglecting to gather evidence while the device is still within the network may have catastrophic consequences. For example, shadow IoT devices may premeditate attacks or be used to launch attack(s) on the enterprise network. They quickly become untraceable as their lifespan on the network is always short.
* Managing the possibility of cross-contamination of legitimate IoT devices: The possibility of cross-contamination of legitimate IoT devices connected to the enterprise network is virtually inevitable if vulnerable shadow IoT devices are present. This can pose a great danger to the health and availability of the enterprise network. Hence, the accurate identification of shadow IoT devices, and the monitoring and capturing of their activities as potential digital evidence and as a readiness process in case of any harm to the enterprise network cannot be overemphasized.
* Profiling network activities: The readiness process for shadow IoT devices will serve as a measure to assist cybersecurity incident responders, IoT forensic experts and cybercrime investigators in profiling enterprise networks so as to identify and analyze the network activities of shadow IoT devices that violate an organization's security policies.
* Managing digital forensic investigation (DFI) costs: The DFI process is made easy when potential digital evidence is available. As indicated earlier, the lifespan of shadow IoT devices on the organization's network is short. This may have critical cost implications as potential digital evidence (PDE) may become untraceable when DFI is difficult to conduct. At the occurrence of a crime or breech in the organization's policies involving a shadow IoT device, DFI could become tedious because the device might not leave a trace of its activities on the enterprise network.  
The purpose of gathering PDE for shadow IoT devices was discussed in this section. The next section discusses the proposed conceptual model for shadow IoT device forensic readiness to achieve digital forensic readiness.
## 2 Materials and Methods
### Characterization  
The morphology of the Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/carbon composite fibers was characterized by scanning electron microscopy (SEM) using a Sigma VP Carl Zeiss instrument, coupled with an energy dispersive spectroscopy (EDS) system (EDAX, Mahwah, NJ, USA) to investigate the elemental composition of the fibers. Transmission electron microscopy (TEM) images and selected area electron diffraction (SAED) images were recorded using a FEI G2 F30 microscope operated at 300 kV. High-angle annular dark field (HAADF) scanning TEM (STEM) and EDS maps were collected with a FEI G2 Titan 60-300 probe corrected microscope equipped with a Super-X EDS system. The Titan was operated at 200 kV with a convergence angle of 24 mrad and HAADF inner collection angle of 58.5 mrad. X-ray photoelectron spectrometry (XPS) was conducted using a Thermoscientific K-\(\alpha\) instrument equipped with monochromatized Al K\(\alpha\) radiation (1486.7 eV). For all the XPS tests, a spot size of 400 \(\upmu\)m for the X-ray beam was implemented. To confirm the desired crystal structure of the synthesized metaloxide/carbon composite fibers, X-ray diffraction analysis with a 20 range of 20 to 60 degrees was carried out at a scan rate of 1 \({}^{\circ}\)C min\({}^{-1}\). Thermogravimetric analysis (TA instruments Q500) was carried out to obtain the carbon content in the composite fibers.
# A Case Study on the Rehabilitation of a Fire-Damaged Structure
## 5 Rehabilitation Plan  
Based on the results of the visual inspection, material tests, and finite element analysis discussed in the previous sections, the remaining structure needs to be rehabilitated. The scope of the rehabilitation can be summarized as follows:  
* Operation floor structure (slabs, girders and beams): severe or partial damage to the concrete and reinforcing bars, thus retrofit is required.
* Columns and walls: damage to the surface or plastering only, thus finishing work is required without structural retrofit.
* Mezzanine floor structure: damage cannot be identified but no marked damage is inferred, thus inspection is required after removing the debris on the floor.  
The retrofit methods of the damaged girders and beams can be summarized as:  
* Surface repair by epoxy-injection or mortar-grouting.
* Structural retrofit by bonding carbon fiber sheet or by anchoring steel plate around the damaged surface.  
Figure 16: Comparison between load-displacement relations of damaged and undamaged structural elements. (**a**) Girder #2. (**b**) Column #3 (exterior column) and #4 (corner column).
## 3 Experimental Results and Analysis  
To verify the effectiveness of the multisource fusion classification method proposed in the chapter, three tree species datasets in Section 2.2 are still used as experimental data in this chapter, and classical methods are selected for comparison. The training sample and test sample are in Section 2.2. The Softmax classification and linear discriminant analysis (LDA) are used for the classifiers, respectively.
## 5 Results
### Feature Selection
It should be noted that the selection of relevant features was only done in the N-BaloT dataset, since the CIC-DDoS2019 dataset already has a selection of relevant features and was provided by the authors of the dataset, as mentioned in [6]. To further enhance model performance and computational efficiency, the 30 most relevant features were selected based on the results of feature importance evaluations from both Random Forest and XGBoost. Among these, features such as HH_jit_L0_01_mean, H_L0_01_weight, HpHp_L0.1_weight, and MI_dir_L0.1_mean stood out for their strong contribution to distinguishing benign and malicious traffic. The selection was based on the intersection of 31 common features between both algorithms, prioritizing consistency and robustness in predictive capability. This dimensionality reduction approach significantly decreased training time while preserving high accuracy levels, demonstrating its suitability for deployment in resource-constrained IoT environments. It is important to note that this feature selection process  
Figure 5: Most important features selected by XGBoost. was only applied to the N-BaIoT dataset, as the CIC-DDoS2019 dataset already included a predefined set of relevant features curated by its original authors. To address the class imbalance present in the datasets, the Synthetic Minority Over-sampling Technique (SMOTE) algorithm was applied. This technique increased the representation of the minority class without replicating existing instances, which contributed to improving metrics such as recall. However, it is recognized that SMOTE can introduce synthetic instances that do not fully reflect the complexity of real malicious traffic, which could affect the model's generalization. To mitigate this risk, cross-validation and a detailed analysis of performance metrics were employed, with a particular emphasis on the minority class.
## 3 Results
### Experiment 2 (Concentration and Spatial Distribution of BAP)  
As shown in Figure 3, an increase in the amount of BAP used for injections to a dose of 75 g/m\({}^{3}\) did not improve with statistical significance the effects already reached by the standard dosing of 50 g/m\({}^{3}\), though the achieved reductions in %OM and COD slightly increased up to 20% and 24% respectively. On the other hand, reducing the dose of the BAP to 25 g/m\({}^{3}\) did not achieve organic matter variations significantly different from the controls, meaning that the effect of the BAP was blurred when the dose was lowered from 50 to 25 g/m\({}^{3}\). Similarly, a higher spread of the standard  
Figure 4: Box and whisker plots of the variation of the total nitrogen (TN), ammonium (NH\({}_{4}\)), nitrite (NO\({}_{2}\)-), total phosphorus (TP), and loosely-binding phosphorus (Loosely-P), after 45 days of incubation for the different experiments conducted in the River Magro. The box and whisker plot show the minimum value, first quartile, median (solid line), third quartile, and maximum value of the data set. The gray dotted line shows the zero-line for the variations, built-up from the average values for the variable at the beginning of the experiment. Outliers are indicated as black crosses. The codes of samples are described in Table 1. One and two asterisks denote significance at confidence levels of 95% and 99%, respectively. The color of asterisks refers to pairwise comparison between any treatment with either the control (red) or with the standard dose of 50 g/m\({}^{3}\) of BAP treatment shown in experiment 1 (blue).  
50 g/m\({}^{3}\) BAP dose did not show observable reductions of the OM-related variables compared to the control without BAP injections. Instead, both the %OM and BOD\({}_{5}\) in this treatment showed slightly significant increases (\(p\)-values \(\leq\) 0.032) compared to the standard 50 g/m\({}^{3}\) dose. All these results show that the effects of lowering the dose, or to spread it excessively, result in negligible effects of the BAP additions for reducing the organic matter content in the riverine sediments.  
Concerning TN and TP (Figure 4), only the amendments with the highest dose of BAP (75 g/m\({}^{3}\)) achieved significant reductions of TN, up to 20%, compared to the control, whether neither this treatment nor any of the others amended with the BAP achieved a statistically significant difference in its final TP content compared to the control. Regarding the N and P-forms, the only treatment showing statistically significant differences with either the control or the standard BAP dose (50 g/m\({}^{3}\)) was that where the standard dose was spread, where ammonium decreased by 68% (\(p\)-value = 0.001) and the loosely-binding P increased by 13% (\(p\)-value = 0.02) compared to the control, whereas the nitrite increased (\(p\)-value = 0.034) compared to the non-spread standard dose treatment.
## 3 Test and Numerical Model of Dampers  
The hysteretic dampers considered in this study consist of steel strips connected to two external flanges and one internal flange, as shown in Figure 6a. They are built from a steel plate by cutting a number of slits and leaving a number of strips in between and will be called slit-plate damper (SPD) hereafter. The SDPs are installed in the structure in such a way that when it is subjected to horizontal deformations, the internal flange is forced to move with respect to the external flanges in the direction perpendicular to the axis of the steel strips, as shown in Figure 6b, and this causes flexural/shear deformations on the strips. The source of energy dissipation of the SPD is the hysteretic plastic deformation of the steel strips. The cyclic behavior of SPDs were investigated through a lab test whose results are reported in reference [34].  
Figure 7 shows, with solid lines, the force displacement curve, \({}_{s}\)N\({}_{s}\)A\(u\), obtained from one of the tests before the specimen failed. The test results were used to calibrate a numerical model (to characterize the behavior of the SPD), namely, the Giuffre-Menegotto-Pinto model with isotropic strain hardening. It is implemented in Opensess [24] as Uniaxial Material Steel 02. Table 1 indicates the values of the parameters that control this constitutive model. \({}_{s}\)N\({}_{y}\) and \({}_{s}\)_k_ correspond to the yield-axial force and the stiffness, respectively, while the remaining parameters control the hysteretic behavior of the steel device; their meaning is explained in reference [24]. Figure 7 compares the \({}_{s}\)N\({}_{s}\)A\(u\) curves  
Figure 5: Input energy history.  
Figure 6: Slit-plate dampers: (**a**) dimensions (in mm); (**b**) deformed shape.  
obtained experimentally with those predicted by the numerical model (dash lines). A very good agreement is seen.
## 4 Conclusions  
Extraction of cellulose from sago biomass using five different chemical and chemi-mechanical methods were carried out. When considering the chemical composition and microscopic observation, cellulose exhibits high crystallinity and purity. Cellulose extracted from pulping method 1 has proved to be the best cellulose extraction method from sago biomass. Furthermore, this cellulose was subsequently modified to CMSC of DS 0.6. There was a complete loss of crystallinity in CMSC when compared with cellulose and the NMR results confirm that there was expected change in chemical structure as required. The synthesized CMSC is free from heavy metals and toxic substances. The cells cultured on the CMSC samples suggested the biocompatibility of the sample. Considering the aforementioned properties, CMSC has potential a potential application as a pharmaceutical excipient. The developed method could be used to convert sago waste into a value-added product of commercial importance.  
The synthesis and characterization of purified cellulose and CMSC were funded by the Ministry of Higher Education through Fundamental Research Grant Scheme (FRGS) (FRGS/2/2013/SG06/MUSM/02/1). The article-processing charges are also supported by the FRGS grant. The research work carried at School of Pharmacy and School of Science, Monash University, Malaysia.  
Figure 7: **(A)** Micrographs of HEK (Human Embryonic Kidney) cells cultured in presence of CMSC (**a**); Positive control (**b**); Negative control (cytotoxic 5-Fluorouracil) (**c**) 20 \(\upmu\)g of CMSC (**d**) 50 \(\upmu\)g of CMSC (**e**) 100 \(\upmu\)g of CMSC (**f**) 250 \(\upmu\)g of CMSC (**g**) 500 \(\upmu\)g of CMSC (**h**) 1000 \(\upmu\)g of CMSC; (**B**) Bar graph comparing the cell viability of Hela cells that had been cultured on various CMSC concentration (100–1000 \(\upmu\)g/mL).  
**Author Contributions:** [PERSON] and [PERSON] have contributed to the synthesis and the characterization of CMSC from sago biomass; [PERSON] and [PERSON] conducted all the experiments and analyzed the results, [PERSON], and [PERSON] have contributed to pharmaceutical characterization and written the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Attenuating Oxidative Damage with Macelignan in Glutamate-Induced HT22 Hippocampal Cells
## 3 Results  
### Macelignan Identification and Isolation  
The results of the compound analysis are shown in the Supplementary Materials (Figures S1-S3). Compound **1** had the molecular formula C\({}_{20}\) H\({}_{24}\) O\({}_{4}\), as confirmed by high-resolution electrospray ionization mass spectrometry (HRESIIMS) using the [M + H] + ion at \(m/z\) 329.1737. The \({}^{1}\)H-NMR spectroscopic data of compound **1** showed six aromatic protons at \(\delta_{\rm H}\) 6.82 (1H, H-5), 6.72 (1H, H-5\({}^{\prime}\)), 6.65 (1H, H-2), 6.64 (1H, H-6), 6.62 (1H, H-2\({}^{\prime}\)) and 6.59 (1H, H-6\({}^{\prime}\)); one methylene dioxy at \(\delta_{\rm H}\) 5.92 (1H, d, \(J\) = 1.4 Hz, OCH\({}_{2}\)O) and 5.91 (1H, d, \(J\) = 1.4 Hz, OCH\({}_{2}\)O); one hydroxy proton at \(\delta_{\rm H}\) 5.45 (1H, s, 4\({}^{\prime}\)-OH); one methoxy group at \(\delta_{\rm H}\) 3.86 (3H, s, OCH\({}_{3}\)); two methylene protons at \(\delta_{\rm H}\) 2.76 (2H, dd, \(J\) = 13.8, 4.9 Hz, H-7) and 2.26 (2H, m, H-7\({}^{\prime}\)); two methine protons at \(\delta_{\rm H}\) 1.72 (2H, m, H-8 and 8\({}^{\prime}\)); and two methyl protons at \(\delta_{\rm H}\) 0.83 (6H, m, H-9 and 9\({}^{\prime}\)). The \({}^{13}\)C-NMR spectrum of compound **1** revealed the presence of two aromatic rings at \(\delta_{\rm C}\) 147.5 (C-3), 146.3 (C-3\({}^{\prime}\)), 145.5 (C-4), 143.6 (C-4\({}^{\prime}\)), 135.7 (C-1), 133.8 (C-1\({}^{\prime}\)), 121.8 (C-6), 121.7 (C-6\({}^{\prime}\)), 114.0 (C-5\({}^{\prime}\)), 111.5 (C-2\({}^{\prime}\)), 109.4 (C-5), and 108.0 (C-2); one methylene dioxy group at \(\delta_{\rm C}\) 100.7 (OCH\({}_{2}\)O); one methoxy group at \(\delta_{\rm C}\) 55.9 (OCH\({}_{3}\)); two methine carbons at \(\delta_{\rm C}\) 39.4 (C-8) and 39.3 (C-8\({}^{\prime}\)); two methylene carbons at \(\delta_{\rm C}\) 39.1 (C-7) and 38.9 (C-7\({}^{\prime}\)); and two methyl carbons at \(\delta_{\rm C}\) 16.2 (C-9) and 16.1 (C-9\({}^{\prime}\)). By comparing the \({}^{1}\)H-NMR and \({}^{13}\)C-NMR spectroscopic data with those published in the literature [37], the structure of compound **1** was identified to be macelignan (Figure 1a).
## 3 Experimental Results
### Noise on the \(g_{i}(t)\) Versus Noise on the \(h_{k}(t)\)  
In this section, we verify expression (10), which we have shown is the functional link between the power of noise superimposed on functions \(g_{i}(t)\) and the corresponding power of noise superimposed on the \(h_{k}(t)\) estimated by combining the \(g_{i}(t)\). We start with the simulation of the noise sequence superimposed on functions \(g_{i}(t)\) and estimate their variances; we then operate the linear transformation (7) and obtain the corresponding noise sequences affecting the \(h_{k}(t)\) kernels; in this case too we estimate the variance vector \(\left[\sigma^{2}{}_{h}\right]\) characterizing these noise sequences. We thus compare the latter estimates \(\left[\sigma^{2}{}_{h}\right]\) with those obtained by means of Equation (10).  
Figure 3 plots the noise sequences: The first line of Figure 3 reports an example of the noise sequences obtained by truncating at different \(\Delta t_{k}\) the function \(u(t)\), in the absence of input excitations: The value of the noise powers in the four segments, estimated by aver  
aging over 50 trials, is \(\left[\sigma^{2}{}_{g}\right]=\left[\begin{array}{cccc}&0.0098&0.0090&0.0101&0.0099 \end{array}\right]^{T}\). The second row of Figure 3 shows the noise sequences obtained by the transformation (7). Noise powers, estimated from the sequences, are \(\left[\hat{\sigma}^{2}{}_{h}\right]=\left[\begin{array}{cccc}&0.0157&0.0048&0.00 6&0.0002\end{array}\right]^{T}\).  
We then compare these estimates with the corresponding variance values predicted by using equation (10), which gives the estimates of the same \(\left[\sigma^{2}{}_{h(t)}\right]\) starting from the \(\left[\sigma^{2}{}_{g}\right]\): we obtain from (10): \(\left[\hat{\delta}^{2}{}_{h}\right]=\left[\begin{array}{cccc}&0.0154&0.0047& 0.0006&0.0002\end{array}\right]^{T}\).  
The percentage estimate error made adopting (10), evaluated by means of \(\sigma^{2}{}_{i\_ERR\%}=100\)\(\left(\hat{\delta}^{2}{}_{h_{k}}-\hat{\sigma}^{2}{}_{h_{k}}\right)/\hat{ \sigma}^{2}{}_{h_{k}}\), in the case of the above example gives:  
\[\left[\sigma^{2}{}_{ERR\%}\right]=\left[\begin{array}{cccc}&-1.54\%&-1.8 7\%&0.0\%&0.0\%&\end{array}\right]^{T}\]  
The low error values confirm that the variance \(\left[\sigma^{2}{}_{h}\right]\) of the noise superimposed on the kernels \(\left[h(t)\right]\) can be accurately obtained from an estimate of the power of the noise \(\left[\sigma^{2}{}_{g}\right]\) superimposed on the functions \(\left[g(t)\right]\) by adopting Equation (10).
# Citrus Pest Identification Model Based on Improved ShuffleNet
## 3 Results and Discussion
### Impact of the Attention Mechanism on Model Performance  
To assess the effect of integrating the BCBAM attention mechanism on the performance of our model, we conducted comparative experiments under identical conditions. We added SE, CBAM, and BCBAM attention mechanisms to the original ShuffleNet V2 model, labeled Schemes 1, 2, and 3, respectively, as shown in Table 3. The results indicated that Scheme 3, incorporating the BCBAM attention mechanism, achieved superior accuracy, precision, recall, and F1 scores compared with Schemes 1 and 2. Notably, all three schemes had an equal number of parameters. Although the floating-point operations for Schemes 2 and 3 were marginally higher than those for Scheme 1, Scheme 3 demonstrated the best performance across the evaluated metrics, suggesting a slight superiority of the BCBAM attention mechanism module over the SE and CBAM attention mechanisms in our model.
## 4 Experiment Results and Discussion
### Vibration Control Experiments Based on Multiple Frequency Excitations
Compared with the experimental results by only using air flow excitation in Figure 13, the interference amplitude is reduced due to the passive damping effect. In Figure 14, with respect to interference amplitudes, the dominant frequencies are mainly 0.06, 0.12 and 0.18 Hz. Especially, four frequency components appear in Figure 14b. The full frequency band vibration suppression effect below 0.2 Hz is significant, especially at 0.18 Hz in Figure 14b, and the vibration reduction effect can reach 18.63 dB. This result further proves the semi-active vibration suppression effect of dual joints. In the case of multi-frequency excitations, due to the lower excitation frequency of the flexible plate, variable damping is the leading contributor with variable stiffness being the supplementary.
## 2 Channel Model and Signal-to-Noise Ratio
### Effective Signal-Collection Area Model for Non-Imaging Angle-diversity Receivers
#### 2.2.2 Sectored Angle-Diversity Receiver
They are usually made of coloured glass or plastic and their transmission characteristics are constant and substantially independent of the incidence angle, i.e., \(T_{S}(\psi)=T_{0}\) for all wavelengths longer than the cutoff wavelength. Since the silicon device does not respond to wavelengths beyond about 1100 nm, the filter-photodetector combination exhibits a bandpass optical response, whose bandwidth will range from the filter cutoff wavelength to the longest wavelength at which the silicon responds. Although the sectored receiver may currently be regarded as a theoretical structure since no prototype has yet been implemented, we assume that a longpass absorption filter with a 780-nm cutoff wavelength is employed, specifically the Schott RG-780 optical filter [24]. This filter exhibits an almost constant transmission characteristic above 780 nm given by \(T_{0}\) = 0.99, and consequently, the spectral bandwidth resulting from the filter-photodetector combination is about \(\Delta\lambda\) = 320 nm.
## 4 Coexistence Solutions for Sync-FT Operation
### Repicking a Backoff Count  
This feature allows a free-riding link to repick a new backoff count and restart a BO based on this new count immediately after a free-ride transmission (Figure 11); thus, we name it _Repick_. Since the link may have performed a BO before the free-ride transmission and reduced the backoff count by some amount, allowing it to repick a new backoff count will increase its backoff time on average. In the example shown in the figure, the free-riding link (Link 2) with Repick starts a new BO with a backoff count of nine. Without this feature, the link would use the previous count of three (STR case) or one (non-STR case). The newly repicked count value is not affected by whether the free-riding transmission was successful. It follows the current CW value of the link.