esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sensitivity of Virtual Constellation Performance to Initial L8 Cloud Cover  
The virtual constellation model was trained and tested using a data corpus where each record corresponds to a labeled (NDVI) pixel with the predictor features explained above. The source images were compiled from L8, S2A, and ASTER imagery from the 2015 to 2018 time period. Since each pixel is used as a training record, rather than a vector representing the entire image (common practice in deep convolutional neural networks, for example), the virtual constellation avoids overfitting to any particular prediction feature, including month. However, in order to determine the effective limit on initial cloud cover in the target L8 image that can be reconstructed using the virtual constellation, we tested the method's sensitivity to percent cloud cover by artificially obscuring increasing percentages of pixels from the target L8 image and executing the virtual constellation process. Table 3 shows the performance of the virtual constellation under different percentages of synthetic data loss. January 2016 was selected for the sensitivity analysis because it had 100% visibility and was therefore a good candidate for validation using synthetic data loss due to clouds.  
Figure 9: Estimated (random forest and linear regression) vs. observed NDVI.  
Based on the analysis of the January 2016 image, the performance of the trained virtual constellation in reconstructing NDVI data in an L8 image was resilient to the initial cloud cover of the base L8 image. The R\({}^{2}\) and RMSE values for predictions across the entire range of artificial cloud cover percentages were all greater than 0.81 and 0.0094. Thus, the results indicate that virtual constellation performs almost equally well regardless of season (Figure 9) or severity of cloud coverage in the target L8 image (Table 3).
## 3 Materials and Methods
### First Step  
The identification of features (i.e., wavelengths) statistically explaining the sample property has been successfully addressed by considering the regression coefficients coming out from the model cross-validation to which the CVA method was applied. Once a problematic wavelength is identified, this is eliminated from the calculation loop, and another run is performed (each new run uses a new randomized set of calibration samples). This loop continues until no more wavelengths are identified as problematic. After this phase, a study was performed on the recorded RPD values, rewinding back to the RPD maximum value and to the corresponding selected wavelengths subset. Finally, 50 Monte Carlo runs were performed using the subset of selected wavelengths; in this manner, the RPD was statistically assessed at its maximum measured value (both its mean and standard deviation of the mean).  
After the selection of the optimal wavelengths using the CVA method, the EXTCV was used to verify the model performance of prediction. To assess this, the usual parameters were used: the false positive rate (FPR) and the false negative rate (FNR).  
FPR is the ratio between wrongly predicted samples as inoculated and overall healthy samples in EXTCV.  
FNR is the ratio between wrongly predicted samples as healthy and overall inoculated samples in EXTCV.  
While the FPR represents the fruits that could be discarded as inoculated despite being healthy, the FNR is a very critical parameter representing a risk; indeed, FNR represents the fruits that could arrive to consumers being, unfortunately, inoculated.  
The two algorithms were coded as M = PCR or PLS.  
In addition, to improve the prediction capability of the models, some pre-treatments of the spectra were considered:  
* raw spectrum or standardized spectrum (i.e., by centering around the mean value and dividing by the standard deviation), coded as OPTN = 0 or OPTN = 6, respectively;
* application of a 0 th, 1 st, or 2 nd-degree derivative (coded as D = 0 (none), 1 (first derivative), or 2 (second derivative)) (i.e., using the Savitzky-Golay algorithm [52] with a width of 15 points and a polynomial order of two).
* The identification and removal of outliers were not considered.
* The final result was coded as M-D-OPTN, i.e., model name, derivative degree, and specific pre-treatment considered.  
This represented the first step (see inner Monte Carlo loop in Figure 1).
## 4 Discussion
### Variables  
* There is no fixed rule for identifying sites that applies to any dam in any region, but it is necessary to know the geographic context to understand which variables may have greater relevance. The two cases of Halabiyeh and GERD dams have, in fact, shown how the potential sites have different characteristics based, among others, on the construction materials of the buildings (mudbrick vs. wood), and the shape of each site (rounded tell vs. irregular flatland). It is therefore essential, when carrying out this analysis, to be well acquainted with the distinctive architectural element(s) of the local ancient culture, the settlement and urban patterns, as well as any other visible physical element that is typical of that region (e.g., abandoned meanders).
* The two types of satellite images that were used in this study provide different levels of detail and types of information. In the case of COSMO-SkyMed, the possibility of modulating the spatial resolution is certainly an extremely advantageous feature, compared to the non-modifiable 10 m resolution baseline of Sentinel-2. The latter, however, is a good trade-off for a large variety of archaeological artefacts.
* The integrated use of COSMO-SkyMed and Sentinel-2 well suits the need for a rich temporal acquisition of satellite images. Indeed, if on the one hand Sentinel-2 guarantees a high acquisition frequency (except for the cloud cover as a random factor), on the other hand, COSMO-SkyMed allows for a remarkable tasking agility. We can also consider both the satellite assets to find observation solutions even in cases of tight deadlines and schedules. This makes COSMO-SkyMed and Sentinel-2 data more suitable than the open access images that are provided by Google Earth or other visualization platforms and used alone.
## 3 Experiments
### 5G Delay Experiments
#### 3.4.2 Results  
The measurements confirm the qualitative finding recorded in the design notes: \"Data size increased. Total delay followed a nonlinear trend, particularly at lower network speeds where the transmission time became dominant factor\".  
In practical terms:  
* Small telemetry packets (<10 kB) incur only the 35-40 ms network round trip and can be pushed at the full Thing5 peak rate limit without perceptible lag.
* Moderate uploads (1-10 MB) remain sub-second on links >500 Mbps but stretch into the 1-10 s range if the cell throttles to 100 Mbps.
* Bulk transfers (100 MB) are viable only when the link holds at several hundred Mbps; otherwise the gateway is occupied for many seconds and the modern climbs into its 14-19 W power envelope (see Section 3.3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Setup** & **Idle Power (W)** & **Active Use Power (W)** & **Load Power (W)** \\ \hline Raspberry Pi + 5G Module & 4.3–6.4 W & 9.5–14.6 W & 14–19 W \\ \hline \hline \end{tabular}
\end{table}
Table 6: Raspberry pi with Waveshare SIM8200 EA-M2 5G module power consumption.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bandwidth** & **Active Use Power (W)** & **Load Power (W)** \\ \hline
100 Mbps & 9.5 & 14.0 \\
500 Mbps & 12.0 & 16.2 \\
1 Gbps & 14.6 & 19.1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Raspberry pi with Waveshare SIM8200 EA-M2 5G module power consumption.  
Accordingly, the deployment plan schedules high-volume firmware or data dumps during strong signal periods and leaves the regular sensor feed well below 1 MB per transaction, ensuring end-to-end delays of <50 ms under normal conditions while keeping energy consumption in check (Figure 11).  
Figure 11: 5G transmission delay vs. data size.  
Figure 10: Throughput mattered: at 100 Mbps the gateway held to 9.5 W (Active)/14 W (Load); raising the link to 500 Mbps pushed those figures to 12 W/16.2 W; and saturating a near-gigabit channel drove consumption to 14.6 W/19.1 W. In other words, every 400 Mbps increment costs roughly 2-2.5 W of extra power.
# Securing IoT Devices against Differential-Linear (DL) Attack Used on Serpent Algorithm
## 2 Problem Statement  
The Serpent algorithm is one of the most common algorithms required to be installed on IoT devices. The main concern is a new tool called _DLCT_ used to attack Serpent by intruders in order to discover secret encryption keys used to secure data stored in IoT devices. The process of using _DLCT_ to find the key is called a DL attack [6; 7]. Once an attacker cracks and discovers Serpent's key, all data encrypted with Serpent in IoT devices can be easily accessible to attackers. This attack can result in the exploitation of entire IoT devices and their users. Essentially, it is simple for an attacker to attack a cryptographic algorithm such as Serpent, since it has a low number of output bits (4-bit) found on the S-Boxes [8]. A differential-linear attack can harm the entire security of IoT devices if it is not appropriately considered. Little has been conducted to secure the Serpent algorithm against DL attacks. This study focuses on securing Serpent from DL attacks by constructing a new additional function called Blocker, using three magic numbers, and developing new 32-bit output S-Boxes so that _DLCT_ will be cumbersome for building _DLCT_ using the S-Boxes of the Serpent algorithm. It has been analyzed that DL attack starts with _DLCT_. Therefore, by blocking the construction of _DLCT_, it is believed that a DL attack will be impossible.  
Additionally, a newly generated function called Blocker is introduced in this study. A Blocker Function uses a 32-bit output value from S-Box as \(state32 hold\) and delivers a new value, \(statehold\) value, as an output. A Blocker Function also turns \(P\) value, \(M\) value, and \(Q\) value into unfactorizable polynomials. Random numbers and XOR operators are utilized for complexity and confusion in order to prevent reverse engineering for attackers. The XOR operator and \(rand()\) change the values of the variables inside a Blocker Function. The random numbers and XOR operators also provide a problematic input range when invaders reverse a Blocker Function to calculate the exact information utilized in that situation. The values of \(M\), \(P\), and \(Q\) are also continually maintained as unfactorizable polynomial variables, which are non-linear and hard to reverse, in order to construct _DLCT_ using any machine or computer. The random numbers and XOR operators include inventing hidden, unseen, and unchangeable variables for intruders. A Blocker Function produces a unique 32-bit S-Box suitable for the new Magic Serpent Algorithm. A Blocker Function distracts the attacker since it comprises many mathematical random numbers and XOR operators. Additionally, most mathematical XOR operators and random numbers are irreversible. For more mathematical characteristics of a Blocker Function and C++ explanations, refer to Section 4 and Figure 1.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 3 Logistics Warehouse Object Detection
### Object-Detection Test  
#### 3.3.1 First Training Detection Model  
The first training dataset consists of 273 photographs of actual logistics warehouse scenarios, including 50 images of goods, 50 images of staff and 173 images of staff and goods.  
The 273 images were fed into the network for training to obtain the detection model. Three test videos were randomly searched for in a logistics scene. To validate the dataset's dependability, each video contained frames of staff, goods and the background. The results of the first logistics warehouse object-detection test are shown in Figure 11.  
The trained model encountered challenges when detecting a target that was a relatively small part of the whole-image bounding box. Along with a continuous video stream, it tended to lose the tracking frame when the target was obscured. There were also many cases of false positives--for example, the detection for non-goods objects as targets. In cases where goods and workers were obscured from each other, the detection bounding boxes often appeared for only one of the two elements.  
In labelling, the authentic-detection bounding box of the workers was defined as the smallest rectangular box that could enclose worker's clothing, hair, etc. However, it was difficult for the detection model to enclose the entire area of the worker when parts of the worker were concealed. When it came to detecting goods, the model often struggled to detect the individual items based on their features, especially in scenarios where multiple goods were stacked each other.  
To address the issues above, another dataset was established. The second training incorporated 790 photos from a fixed scene (i.e., the logistics warehouses of Company S)  
Figure 11: Test results for the three videos.  
Figure 10: Train/Validation loss–Epoch curve and [EMAIL_ADDRESS]-Epoch curve.  
as the dataset for the detection of parcels and workers. It changed the situation of the relatively small size of datasets and a large number of diverse scenes.
## 4 Discussion
### Dependence of the Kaiser Effect on the Prescribed Stress  
Figure 10a demonstrates that when \(\sigma_{\rm A}=0.5\sigma_{\rm c}\), i.e., the prescribed stress is located in the region of the compaction stage to the elastic stage, AEs appear extensively if the stress is larger than \(\sigma_{\rm A}\), and the Kaiser effect is obvious. When \(FR>1.0\) in the re-loading stage, the Kaiser effect occurs, and the loading strain rate poses a minor impact on the Kaiser effect. Once \(\sigma_{\rm A}\) arrives at \(0.6\sigma_{\rm c}\), i.e., in the region of the elastic stage and the stable crack expansion stage, the appearance of the Kaiser effect is strongly related to the loading strain rate. When \(FR<1.0\) and \(l_{\rm sr}<10^{-4}\) s\({}^{-1}\), the Felicity effect appears in the re-loading stage and the Kaiser effect disappears. When \(FR>1.0\) and \(l_{\rm sr}\geq 10^{-4}\) s\({}^{-1}\), the Kaiser effect appears over the re-loading. When \(\sigma_{\rm A}\geq 0.7\sigma_{\rm c}\), i.e., ranging from the stable crack growth stage to the unstable cracking stage, for \(FR<1.0\), substantial AE events happen at the low loading stress level. That is to say, the Kaiser effect vanishes, accompanied with the occurrence of the Felicity effect. The loading strain rate has nearly no impact on the Kaiser effect. Therefore, the Kaiser effect and the Felicity effect are greatly influenced by the prescribed stress (\(\sigma_{\rm A}\)). For the same type of rock, different \(\sigma_{\rm A}\) under the same loading path result in different damage degrees of rock, and thus, different AE characteristics. Overall, the mechanical properties of the rock degrade as \(\sigma_{\rm A}\) increases. As the internal damage degree gradually increases, more AEs are generated earlier. Consequently, as the Kaiser effect breaks down, the Felicity effect emerges.
# Roles for Exosomes in the Pathogenesis, Drug Delivery and Therapy of Psoriasis
## 3 The Immunological Function of Exosomes That Are or Are Not Associated with Soriasis
### Non-Immune Cell-Derived Exosomes
#### 3.2.2 Adipocyte-Derived Exosomes  
Metabolic syndrome and obesity are the main complications of psoriasis. Obesity can increase the risk of psoriasis, while many obesity-related proinflammatory factors are involved in the pathogenesis of psoriasis [73]. The adipose tissue was found to regulate immune cell function/metabolism in distant tissues by secreting exosomes containing bioactive molecules [74]. Moreover, adipose tissue from obese mice released more lipid contents via exosomes than that from lean mice [75]. A previous study also revealed that exosomes released by adipose tissue carried adipokines, such as adiponectin, IL-6, monocyte chemoattractant protein-1 and resistir [76]. Many studies have shown that adipose tissue-derived exosomes can enhance inflammatory reactions. For instance, [PERSON] et al. found that adipose tissue-derived exosomes from obese mice induced M1 macrophage polarization through pro-inflammatory miR-155, thus exacerbating intestinal inflammation [46]. Similar studies showed that miR-221-3p-containing exosomes from the adipose tissue of obese mice induced inflammatory responses in perivascular adipose tissue [77]. Additionally, TNF-\(\alpha\)-induced adipocyte-derived exosomes increased leukocyte attachment [78]. However, the role of adipose tissue or adipocyte-derived exosomes in the pathogenesis of psoriasis has not been fully elucidated. More studies are needed to clarify their implications in psoriasis.
# Multiband and Lossless Compression of Hyperspectral Images
## 3 Lossless Multiband Compression for Hyperspectral Images (LMBHI)  
Hyperspectral images present two typologies of correlations:  
* _inter-band correlation_;
* _intra-band correlation_.  
In particular, contiguous bands are strongly correlated (inter-band correlation) and the pixels are generally correlated, since, for instance, two adjacent pixels map adjacent areas, possibly composed of the same material, _etc._ (intra-band correlation). Such characterizations are exploited by the compression strategies, in order to optimize the redundancy among the third dimension. The main aim of our approach, which we denoted as _Lossless MultiBand compression for Hyperspectral Images_ (_LMBHI_), is to exploit the correlation with a predictive coding model.  
In detail, for each pixel, \(X\), of the input hyperspectral image, LMBHI performs the prediction of the current pixel, \(\hat{X}\), by selecting the appropriate prediction context of \(X\) (three-dimensional or a bi-dimensional contexts).  
All the pixels that belong to the first band are predicted by using a bi-dimensional predictive structure: the 2-D Linearized Median Predictor (2-D LMP) [21], which exploits only the intra-band correlation, since the first band has no reference bands. The other pixels are predicted by using a new three-dimensional predictive approach, which uses a prediction context composed of the neighboring pixels of \(X\) and its reference pixels in the previous bands.  
Once the prediction step is computed, the prediction error \(e\) (defined in Equation (1)) is modeled and coded.  
\[e=\left\lfloor X-\hat{X}\right\rfloor \tag{1}\]
## 4 Online Recognition Based on the Hidden Markov Method  
### Hidden Markov Online Orientation Recognition Model  
The mapping relationship between the characteristic parameter vectors of the acoustic emission signal, the vibration signal, and the grinding direction is often not linear, and accurate identification of the grinding direction cannot be achieved by solely relying on kurtosis and skewness. In order to improve the identification accuracy, based on manual monitoring experience, the Hidden Markov Method (HMM), which can solve the modeling problem of time series statistics of stochastic processes in complex systems, is used to identify online the monitoring signal samples and realize online orientation by intelligent means to improve the accuracy of the recognition results. A Hidden Markov Model [14; 15; 16] is represented by \((S,O,\pi,A,B)\). \(S\) is the hidden state of the model, the hidden state has Markov properties, cannot be directly observed, the number of hidden states is \(N\), \(N\) hidden states in the model is defined. Then, the hidden state at time \(t\) is expressed by the Formula (11).  
\[q_{t}\in[S_{1},\cdots,S_{N}] \tag{11}\]  
\(O\) is an observable state in the model associated with an implicit state, which can be directly observed. The observed data at time \(t\) is recorded in Formula (12).  
\[O_{t}=(V_{1},V_{2},\cdots,V_{M}) \tag{12}\]  
In Formula (13), \(V_{1},V_{2},\cdots,V_{M}\) is the observed data of the 1 st to \(M\) dimensions at time \(t\), respectively, \(O_{t}\) is the observation data matrix at time \(t\). \(\pi\) is the initial state probability matrix of the model, which describes the state transition probability distribution of the implicit state at the initial moment, and \(\pi=[\pi_{i}]\) represents the probability that the initial state is \(i\). \(A=\big{[}a_{ij}\big{]}\big{(}a_{ij}\big{)}\) transition probability from state \(i\) to state \(j\) is an implicit state transition probability matrix in the model, which describes the transition probability between each state in the model. The transition matrix \(A\) of the hidden state at time \(t\) is denoted as \(S_{u}\) (there are \(u\) kinds of possible hidden states at time \(t\)), then the probability that the hidden state is \(S_{v}\) at time \(t+1\) is denoted in Formula (13).  
\[A=\{a_{uv}\} \tag{13}\]  
In Formula (14), \(a_{uv}\) is the probability that the hidden state at time \(t\) is \(S_{u}\) and the hidden state at time \((t+1)\) is \(S_{v}\), \(u\
eq v\), \(u\geq 1\), \(v\geq 1\). \(B=[b_{i}(k)]\big{(}b_{i}(k)\big{)}\) is the observed state transition probability matrix that the current state is \(i\) and the probability that the observed value is \(k\) in the model.
# Numerical Simulation of Interaction between Plasma and Azithromycin Based on Molecular Dynamics
## 3 Results Analysis
### Oxidation Reaction  
Due to the participation of active-oxygen groups, many oxidation reactions occurred, and now the common oxidation reactions in the simulation are listed. The functional group (-OH) shown in the molecular structure in Figure 6 is called the alcohol group. An oxidation reaction can make the alcohol group form a ketone or aldehyde group if a hydrogen atom is on the attached carbon.  
Figure 5: Partial reaction path display, a: ozone dehydrogenation; b: hydroxyl dehydrogenation; c: oxygen atom dehydrogenation.  
As shown in the molecular structure of azithromycin in Figure 7, three alcohol groups can be oxidized. Figure 8 shows the process of the alcohol group being corrupted. Under the action of oxygen atoms, H\({}_{1}\)-C\({}_{2}\) is broken, O\({}_{3}\)-H\({}_{4}\) is broken, and the fractured hydrogen atoms and oxygen atom form a water molecule and simultaneously form a C\({}_{2}\)=O\({}_{4}\) double bond.  
Figure 6: Structural display of ketone and aldehyde.  
Figure 7: The oxidizable hydroxyl group in the azithromycin molecule.  
As mentioned in the dehydrogenation reaction above, a lone electron will be left after the carbon atom is dehydrogenated. In the simulation, it is found that this lone electron will combine with a nearby free hydroxyl group or with an oxygen atom to form a C-O bond, which can be further oxidized to form an aldehyde. As shown in Figure 9, the oxygen atom acts on the C\({}_{1}\)-H\({}_{2}\) bond to break the bond and abstract the hydrogen atom to form the hydroxyl group 5. Then, the free hydroxyl group O\({}_{3}\)-H\({}_{7}\) binds to C\({}_{1}\). The free oxygen atom continues to oxidize the hydroxyl group, the C\({}_{1}\)-H\({}_{6}\) bond is broken, the O\({}_{3}\)-H\({}_{7}\) bond is broken. Then, they shed hydrogen atoms and the oxygen atom forms a water molecule and simultaneously forms the C\({}_{1}\)=O\({}_{3}\) double bond.  
In the actual reaction, due to the large number of hydrogen atoms abstracted from carbon atoms, free hydroxyl groups will be combined with carbon atoms which are in diverse positions and maybe be oxidized. The above only shows one situation.