esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Compression with a Time Limit
## 4 Lossy Compression
### Test 4  
The dataset \(D\) to be compressed consisted of 1000 images, for a total size of 692.2 MB (691,215,000 bytes). Single runs of the compression tools with the indicated quality setups yielded the following results shown in Table 11:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tool** & **Size of Output in Bytes** & **Time in ms.** &
\begin{tabular}{c} **Quality** \\ **Parameter** \\ \end{tabular} & **SSIM** \\ \hline JPG1 & 55,165,877 & 5096 & 95 & 0.983708 \\ WEBP2 & 34,071,556 & 4251 & 95 & 0.98584 \\ JPEG-NO OPTIMIZE3 & 57,648,359 & 5201 & 95 & 0.983713 \\ JPEG4 & 17,071,808 & 5658 & 51 & 0.9462306 \\ JPEG5 & 12,384,398 & 5702 & 29 & 0.9226299 \\ JPEG6 & 11,053,309 & 5536 & 24 & 0.9121278 \\ JPEG7 & 10,516,324 & 5875 & 22 & 0.9027293 \\ JPEG8 & 9,873,987 & 5781 & 20 & 0.9005430 \\ WEB99 & 4,908,124 & 3260 & 4 & 0.90047801 \\ WEBP10 & 4,688,350 & 3158 & 3 & 0.8955097 \\ JPEG-NO OPTIMIZE11 & 18,071,969 & 5913 & 51 & 0.946195 \\ JPEG-NO OPTIMIZE12 & 13,702,370 & 5926 & 29 & 0.922539 \\ JPEG-NO OPTIMIZE13 & 12,492,529 & 6028 & 24 & 0.912026 \\ JPEG-NO OPTIMIZE14 & 12,018,149 & 6018 & 22 & 0.902617 \\ JPEG-NO OPTIMIZE15 & 11,446,486 & 6135 & 20 & 0.899925 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Test 4.  
Figure 17: Optimal mix.  
In this test, assuming that the quality desired is lower or equal than SSIM 0.8955097, a single setup will be chosen to compress: the WEBP10 setup with a quality of 3.  
This is because it is the best setup among all both in terms of compression and output size. The final output size will be 4,688,350 bytes, and it will take 3158 ms. Therefore, there will not be a mix of two setups that will be able to do better.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 4 Quantitative Performance Comparison via Simulation
### Energy Model  
We adopted the energy model discussed in [70]. Energy depletion through data transmission can be derived using the following equation:  
\[\begin{split}& E_{T}(l,\Delta)=E_{T-el(l)}+E_{T-amp(l,\Delta)}\\ &=\left\{\begin{array}{ll}l*E_{el}+l*\varphi_{fp}*\Delta^{2},& \Delta<\Delta_{th}\\ l*E_{dec}+l*\varphi_{mw}*\Delta^{4},&\Delta>\Delta_{th}\end{array}\right., \end{split} \tag{7}\]  
where the energy consumption due to signal transmission is denoted by \(E_{T}\). \(l\) denotes the length of the packet, and \(\Delta\) represents the distance between the transmitter and receiver. Energy depletion due to circuitry operation for a data packet containing \(l\) bits transmission is given by \(E_{T-el(l)}\). The transmitter energy consumption model due to free-space propagation is denoted by \(\varphi_{fp}\), and for multipath propagation, the model is denoted by \(\varphi_{mw}\). \(\Delta_{th}\) is  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Area & 100 \(\times\) 100 m\({}^{2}\)–400 \(\times\) 400 m\({}^{2}\) \\ Number of sensor nodes & 100 \\ Number of packets sensed by a node in a single round & 100 \\ Initial energy & 1 J \\ \(E_{dec}\) & 50 nJ/bit \\ \(\varepsilon_{fp}\) & 10 pJ/bit \\ \(\varepsilon_{mw}\) & 0.0013 pJ/bit \\ \(\varepsilon_{aggr}\) & 5 nJ/bit \\ Data packet length & 4 KB \\ Hello packet length & 100–150 B \\ Aggregation percentage & 10\% \\ Sensor mobility & Static \\ Carrier frequency & 2.4 GHz \\ UAV altitude & 50 m \\ Node deployment—IoJJHR & Planned \\ Node deployment—NDCMC & Random \\ Node deployment—HEED & Random \\ Sink Placement—HEED & Center of the ROI \\ Antenna type & Omnidirectional \\ MAC protocol & CSMA, TDMA \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
the threshold distance for determining free-space propagation and multipath propagation. The threshold distance can be calculated as follows:  
\[\Delta_{th}=\sqrt{\frac{\varphi_{fp}}{\varphi_{mw}}} \tag{8}\]  
Energy depletion for receiving \(l\) bit of energy can be expressed as follows:  
\[E_{R}(l)=E_{R-el}*l, \tag{9}\]  
where \(E_{R}(l)\) is the energy consumption due to receiving \(l\) bits of data. \(E_{R-el}\) represents the energy consumption due to receiving one-bit data.
# Enhanced FANO Structure Based on Tip-Field-Enhancement Theory
## 3 Experiments and Discussion  
We planned to fabricate a series of FANO structures on the basis of an asymmetric half-ring structure. The substrate was made of quartz with a thickness of 190 um, as shown in Figure 7b. Structure tips were sharpened according to tip-field-enhancement theory, as mentioned above. The width of the widest part was 6.25 times that of the sharpest. A FANO structure without sharpened tips was also fabricated as contrast, as shown in Figure 7a.  
A ZVA 40 with a 330-500 GHz VNA frequency extender, two horn antennas, and a sample holder were used for measurements. The THz wave emitting from the antenna traveled through the sample and was then received by the other antenna. By normalizing the data against a blank quartz sample, the transmission characteristics of the FANO structure could be derived. The THz light spot was confined to the size of the sample, which was 10 \(\times\) 10 mm. The distance between antenna and sample were set within a range of 50 mm according to the antenna gain. Results are shown in Figure 8.  
Due to the dipole resonances at both the high and low side of FANO resonance and loss, \(-3\) dB was not available in the experiment results, as the curve could not reach \(-3\) dB level around FANO resonance. Instead, we used \(-5\) dB bandwidth to characterize its Q factor, which was anticipated to have positive correlation to \(-3\) dB bandwidth. Figure 8 shows that the sharpened structure had a much narrower resonance band than that of a typical structure, while their resonance intensities were at the same level. This result supports the idea that electric charge tend to accumulate at the sharpest points in time-harmonic electromagnetic(EM) fields. Due to the error of thickness and surface-structure shape  
Figure 6: Minimum width of structure has negative correlation with Q factor, while Q factor has negative correlation with resonant intensity. Q factor varies linearly with minimum width, and there seems to be a limit of highest possible Q factor, slightly larger than 120. Non-monotonicity of resonance intensity was probably because resonant intensity was so small that there could be a computational error.  
Figure 7: (**a**) Original and (**b**) enhanced FANO structure under microscope.  
introduced in the fabrication, the Q factor did not match well with the simulation results. The measured thickness was 177 um. However, it could still be found that the sharper the tips are, the higher the Q that could be obtained. It can be seen from Figure 8 that the Q factor of the sharpened model was 1.37 times larger than that of the originally shaped model, while resonant intensity only declined by 1.38 dB, which is quite small. This experimentally proved that charges do accumulate at sharp tips in time-harmonic electromagnetic(EM) fields.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Diffuse Optical Tomography (TD-DOT)  
Diffuse optical tomography (DOT) has been investigated since the early 1990s in CW, FD, and TD. Many investigations of DOT have been conducted in CW and FD, but the advantages of TD over CW and FD attracted researchers. First TD-DOT was reported by [PERSON] and [PERSON] [156] in 1993. They demonstrated the advantage of TD-DOT over CW-DOT by showing a TD-DOT image of a mouse. The TD-DOT image using the early photons revealed the internal organs of a mouse while the CW-DOT image was unable to distinguish the internal organs at all. In the late 1990s, several groups developed more sophisticated multi-channel TD-DOT systems for studies of brain, breast, and muscle imaging [45; 49; 157].  
In the following, various methods for TD-DOT are reviewed first, and the applications of TD-DOT for in vivo studies of brain, breast, and muscle imaging are reviewed.
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 3 Methods
### MSGNet-MLLA-Transformer
#### 3.2.2 Method Framework  
The process of the prediction method proposed in this paper is shown in Figure 2.  
Specifically, this method consists of ten steps.  
**Step 1.** Data collection. Collect weather data and power data in hourly or daily time units to form a power load dataset.  
**Step 2.** Preprocess the collected data, which specifically includes handling missing values, feature selection, normalization, etc.  
**Step 3.** Data embedding. Perform timestamp and position embedding on the preprocessed data to extract the information within it.  
**Step 4.** Use the fast Fourier operation to determine the scale information in the data. This step is achieved through Formulas (1) and (2).  
**Step 5.** Construct multi-scale spatio-temporal graphs: Based on the scales determined in Step 4, a spatio-temporal graph is constructed for each scale according to Formula (3), thereby modeling the data structure within different scales of the data.  
**Step 6.** Construct the MLLA-Transformer encoding layer: To model the long-term sequence dependency relationships existing among data at different scales, the MLLA-Transformer encoding layer is constructed. The specific structure is, in sequence, the multi-head MLLA layer, the normalization layer, the feedforward layer, and the normalization layer. Among them, the multi-head MLLA layer is used to learn the internal relationships within long sequences between data scales.  
**Step 7.** Construct the MLLA-Transformer decoding layer. The specific structure is as follows: masked multi-head MLLA layer, normalization layer, multi-head MLLA attention layer, normalization layer, feedforward layer, and normalization layer. The use of multi-head MLLA helps the model extract the deep global information in the data and calculate the similarity within the sequence without increasing the computational consumption.  
**Step 8.** The amplitudes corresponding to each scale obtained by the FFT are output after being processed by the Softmax layer. They are multiplied pairwise by the outputsthat have passed through the corresponding multi-scale Transformers to obtain the final model result. The specific formula is as follows:  
\[\text{Out}=\sum_{i=1}^{k}Softmax_{F_{f_{i}}}Transformer_{out}^{i} \tag{7}\]  
In the formula, \(Transformer_{out}^{i}\) represents the Transformer output corresponding to the \(i\)-th scale, and \(F_{f_{i}}\) determines the amplitude corresponding to scale \(i\), which is calculated by Formulas (1) and (2).  
**Step 9.** The model is trained and iterated N times, and the training results are verified and evaluated.  
**Step 10.** Test the model and output the final result.
# Sialic Acids: An Important Family of Carbohydrates Overlooked in Environmental Biofilms
## Results and Discussion
### Sialic Acids Are Present in Environmental Biofilms
Secondly, sialic acids are not only produced and used by microorganisms to colonize host surfaces, but are also produced and released in the EPS to protect the biofilm and microbiome from environmental stresses [5; 17; 20; 21]. A recent study on granular sludge has shown that sialic acids, due to their terminal position and large size with nine carbons, were able to protect the granules from enzymatic degradation [5]. Other systems have shown similar behavior, such as the CMP-sialic acid transporter which is upregulated in diatoms under silica-limited conditions. This transporter transfers CMP-sialic acid from the cytosol into Golgi vesicles where glycosyltransferases are activated and produce sialylated glycolipids that can act as defensive molecules. Therefore, this upregulation indicates that diatom cells are coping with starvation by accumulating defensive molecules [20]. Another example are cancer cells, which react to nutrient-deprived conditions by using sialic acids to maintain cell surface glycosylation, through complementary mechanisms [21]. The FTIR analysis shows a sharp peak around 1738 cm\({}^{-1}\) and a shoulder peak at 1730 cm\({}^{-1}\) in the nutrient poor EPS spectrum, which are absent from the nutrient enriched EPS spectrum (Figure 4). The shoulder peak at 1730 cm\({}^{-1}\) has been previously associated to the alpha-keto aldonic acid structure in sialic acids [5]. The presence of this peak in the FTIR analysis of the nutrient poor EPS might indicate additional production of other ulosonic acid variants (e.g., 7 or 8 carbon sugars) at larger quantities potentially induced by nutrient starvation, or increased exposure of the observed nine-carbon sugars. Correlation between FTIR and MS analyses needs to be further investigated. To summarize, changes in nutrient availability did not affect the types and abundances of sialic acids produced by the bacterial community. The conservation of energetically costly production pathways of sialic acids under nutrient starvation highlights their importance as a constituent of EPS and raises questions on their function in environmental biofilms.
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 3 Calculation of Hoop Strain Time-History Curve  
Theoretically, the pipeline leakage case can be both simulated by experiment and numerical simulation, during which the signals can be collected. However, it is impractical to open a hole on a real pipeline to simulate a leakage. Thus, to obtain the key characteristics of hoop strain induced by leakage, the time-history curve needs to be first calculated by numerical simulation. In this work, the method of characteristics is applied to solve the hydraulic transient process of a leakage occurrence.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 4 Security Analysis
### Resistance to Stolen Smart Card Attack  
Stolen card attack points to an attacker who steals the user's smart card and extracts data stored in a smart card. Then, he/she uses these data to impersonate the user whose smart card was stolen. Here, we assume that an attacker already extracts the data (\(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(PID_{i}\), \(ID_{cs}\)) from user \(U_{i}\)'s smart card. In our proposed scheme, if the attacker wants to impersonate user \(U_{i}\), he/she needs to perform the authentication phase. According to the description of Step 1 in Section 3.2., the attacker needs to key in the correct \(ID_{i}\) and \(PW_{i}\) for checking the equation \(h(ID_{i}||PW_{i}||n_{i})?=C_{4}\). However, he/she does not have \(ID_{i}\) and \(PW_{i}\). Therefore, when the attacker initiates an authentication run, he/she cannot pass the check \(h(ID_{i}||PW_{i}||n_{i})?=C_{4}\) in this step, then his/her authentication process will be terminated. The results show that the scheme can resist the attack of stolen smart cards.
## 3 The Proposed Protocol
### The Proposed Scheme  
In this subsection, we will specify the process of authentication and key agreement between the UAV and the ground control station, including the following phases, namely: (i) initialization phase, (ii) registration phase, (iii) UAV-GCS authentication, (iv) Inter-UAV authentication, and (v) the key refresh. Table 2 gives a description of the symbols used in the authentication process. This protocol uses four types of keys: the master key, the refresh key, the session key, and the authentication key.  
* _The master key:_ The master key is generated via the Elliptic Curve Diffie-Hellman Key Exchange (ECDH) protocol, but is not used to encrypt messages, but rather to assist in the generation of session keys and authentication keys.  
Figure 1: System model.  
* _The refreshing key:_ Shared between the UAV and the ground control station and used with the primary key to generate the session key and authentication key. After each generation of the session key and authentication key, a new refresh key is generated by applying a hash function to the current refresh key and deleting the current key and replacing it with the new key. This provides forward secrecy to the protocol; it is assumed that when a node is attacked, information about that node's previous communications will not be compromised. Because attackers cannot generate the old key, the previous refresh key is no longer available at the time of the attack, nor is the master key.
* _The authentication key and the session key:_ The session key is used as the key for the data-to-data encryption algorithm that provides data confidentiality for communication between the ground control station and the UAV and between UAVs. The authentication key uses a separate key for encryption and authentication to guarantee the legitimacy of the identity.
## 3 Singular Spectrum Analysis
### Decomposition  
The first step in the basic SSA algorithm is embedding. In this step, the original time series is converted into a trajectory matrix. For an original time series (\(X=x_{1}\),..., \(x_{N}\)) of length \(N\) with no missing value, a window of length L is chosen (\(2<L<N/2\)) to embed the original time series.  
Then, the original time series \(X\) is mapped into \(L\) lagged vectors, \(X_{i}=x_{i},\ldots,x_{i+L-1}\) for \(i=1,\ldots,K\), where \(K=N-L+1\). Thus, \(T_{X}\) is written as:  
\[T_{i,j}=\left(\begin{array}{cccc}x_{1}&x_{2}&\ldots&x_{L}\\ x_{2}&x_{3}&\ldots&x_{L+1}\\ \ldots&\ldots&&\ldots\\ x_{K}&x_{K+1}&\ldots&x_{N}\end{array}\right).\]  
After the embedding step, SVD is applied to the trajectory matrix \(T_{X}\) and the decomposed trajectory matrices \(T_{i}\) are obtained. \(U_{i}\) for \(1<i<L\) is a \(K_{i}\times L\) orthonormal matrix, \(D_{i}\) for \(1<i<L\) is a diagonal matrix order of \(L\), and \(V_{i}\) for \(1<i<L\) is an \(L\times L\) square orthonormal matrix. In this step, \(T_{X}\) has \(L\) many singular values, which are:  
\[T_{X}=sum_{i=1}^{d}U_{i}\sqrt{\lambda_{i}V_{i}^{T}}. \tag{1}\]  
Then, we can calculate the ratio of each eigenvalue. The ratio of each eigenvalue is the contribution of the matrix \(T_{i}\) to \(T_{X}\).
## Materials and Methods
### Development of ANN Model  
The primary goal for this section of our study was to develop and test different ANN models and determine the model with the best fit that can be used to successfully predict the amount of urea adsorbed. To cater to this purpose, MATLAB R2015b was employed (consisting of the Toolbox of neural network) to create the ANN models. The networks are composed of three layers: an input layer with three nodes, i.e., the 3 process parameters used in this study (temperature, initial concentration, and mass loading), an output layer with one node yielding the final result (adsorbed urea), and a hidden layer with fourteen neurons. Three-layered ANN models were constructed and evaluated to determine the most accurate prediction models for this research. Tangent sigmoid functions are used as transfer functions for the created ANN models' hidden and output layers. The impact of varying the number of neurons in the hidden layer on the final mean square error was analyzed, and the optimal number was chosen. Numerous systems based on various artificial neural network models were created using data collected during a series of experiments. The Levenberg-Marquardt algorithm (TRAINIMM), scaled conjugate gradient, Bayesian regularization (TRAINBR), and quasi-Newton were among the back-propagation techniques evaluated to get the optimum fit.  
The development of the ANN models was based on the dataset obtained from the experimental results of our study. The dataset mentioned above was subdivided into 3 subgroups--training (70% of data), validation (15% of data), and testing (15% of data), followed by a subsequent normalization of these subgroups as per Equation (4).  
\[\mathbf{x_{n,i}}=\frac{\mathbf{x_{i}}-\mathbf{data_{min}}}{(\mathbf{data_{max}}-\mathbf{data_{min }})}*(\mathbf{R_{n,upper}}-\mathbf{R_{n,lower}})+\mathbf{R_{n,lower}} \tag{4}\]  
Here, '\(\mathbf{x_{n,i}}\)' represents the normalized value of '\(\mathbf{x_{i}}\)', '\(\mathbf{data_{min}}\)' denotes the minimum value of data, '\(\mathbf{data_{max}}\)' represents the maximum value of data, '\(\mathbf{R_{n,upper}}\)' represents the normalized upper range, and '\(\mathbf{R_{n,lower}}\)' denotes the normalized lower range. Subdivision of the dataset was performed to negate instances of overtraining the models and, to avoid any computational issues occurring in the ANN models, data normalization was adopted. The epoch was initially set at 1000. Multiple linear regression analysis (MLR) was performed for our dataset to predict the TN and TP. Numerous evaluation techniques were used to comprehend, verify, and evaluate the models' fit and prediction accuracy, including root mean square error, absolute average deviation, R-squared, and adjusted R-squared. These techniques compared the models' predicted values to the observed values of TN and TP. Lastly, the autocorrelation of error with a variation of lag was calculated and minimized for each model, and the mean squared error was calculated as shown in Equation (5).  
\[\mathbf{MSE}=\frac{\mathbf{1}}{\mathbf{n}}\sum_{i=1}^{n}\left(\mathbf{Y_{i}}-\hat{\mathbf{Y}}_{i} \right)^{2} \tag{5}\]