esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Login and Authentication Phase  
A legal client can submit a login request message to a remote server and obtain various services after being authenticated. The steps of login and authentication are shown in Figure 4 and explained below:  
1. The client inserts the memory device and inputs \(ID_{i}\) and \(PW_{i}\). Then, the client computes \(HPW_{i}=h(PW_{i}||b)\), \(B_{i}^{*}=h(h(ID_{i}||HPW_{i})mod\ m)\). After that, \(B_{i}^{*}\) is compared with \(B_{i}\). If they are equal, the client executes Step 2; otherwise, the process is stopped.
2. The client generates a random integer \(r_{c}\), the current timestamp \(T_{1}\), and then computes \(A_{i}=C_{i}\oplus HPW_{i}\oplus B_{i}\), \(R_{c}=r_{c}A_{i}P\), \(k_{1}=r_{c}A_{i}K_{s}\), and \(H_{i}=E_{k_{1}}(realm||ID_{i}||A_{i}||T_{1})\). Lastly, the client sends the REQUEST message \(\{H_{i},R_{c}\}\) to the server.
3. When the REQUEST message is received, the server obtains the data \(\{realm||ID_{i}||A_{i}||T_{1}\}\) by decrypting \(H_{i}\) with \(k_{2}(k_{s}R_{c})\). Then, the server verifies the validity of \(T_{1}\). If \(T_{1}\) is valid, \(A_{i}^{*}=h(ID_{i}||k_{s})\) is computed and \(A_{i}^{*}=A_{i}\) checks whether the validity holds or not. If it holds, then the server executes Step 4; otherwise, it stops the process.  
Figure 4: Login and authentication phase of our proposed scheme.  
Figure 3: Registration phase of our proposed scheme.  
**Step 4:**: The server generates a random integer \(r_{s}\) and timestamp \(T_{2}\), and then computes \(R_{s}=r_{s}\cdot P\), \(J_{i}=r_{s}R_{c}\), \(SK=h(J_{i}||T_{1}||T_{2})\), and \(L_{i}=E_{k_{2}}(ID_{i}||R_{s}||J_{i}||T_{2}||realm)\). Finally, the server sends the ACCEPT message \(\{L_{i}\}\) to the client.
**Step 5:**: On receiving the ACCEPT message, the client obtains \(\{ID_{i}||R_{s}||J_{i}||T_{2}||realm\}\) by decrypting \(L_{i}\) with \(k_{1}\). Then, the client verifies the validity of \(T_{2}\), and if it is valid, the client computes \(J_{i}^{\prime}=r_{c}A_{i}R_{s}\) and checks whether \(J_{i}^{\prime}=J_{i}\) holds or not. If it holds, the client computes the session key \(SK=h(J_{i}^{\prime}||T_{1}||T_{2})\); otherwise, the client stops the process.
## 2 Experimental Methods and Materials
### Raman Spectroscopy  
Three handheld Raman spectroscopy instruments were employed for the collection of Raman spectra. These are briefly detailed below:  
* CBEx handheld Raman spectrometer from Snowy Range (Laramie, WY, USA), equipped with 785 nm excitation laser wavelength, with laser power of 70 mW on the sample, was used to collected spectra over the 400-2300 cm\({}^{-1}\) range with 12-14 cm\({}^{-1}\) spectral resolution. The acquisition time was 2 s for vials-mode analysis;
* CBEx handheld Raman spectrometer (Snowy Range) operating at 1064 nm, with laser power 300 mW on the sample, was employed to collect data in the 400 to 2300 cm\({}^{-1}\) range with 12-14 cm\({}^{-1}\) spectral resolution. The acquisition time was 15 s for vial-mode analysis; and
* Resolve (Cobalt Light System, Oxfordshire, UK now part of Agilent) handheld SORS system, equipped with an 830 nm excitation laser wavelength, with laser power of 450 mW on the sample, was employed to collect spectra in the 350-2000 cm\({}^{-1}\) range with 3 cm\({}^{-1}\) spectral resolution. The acquisition time was less than 2 min for vial-mode, and 2 min for through-barrier analysis.  
Raman spectra were collected in vial-mode using all three instruments. For these measurements, the scans were performed by placing the glass vial containing each sample into the sample holder housed within the spectrometer, where the light is focused into the sample. Five technical replicates were collected from each sample. This resulted in 105 spectra being collected for each series of mixtures: EVOO-sunflower oil; EVOO-pomace olive oil; EVOO-refined olive oil--with each containing 21 samples from 0-100% mixtures in 5% steps. Prior to Raman measurements, the oil samples were gently vortexed to ensure complete homogenization and left to rest for 5 min in order to free any trapped air bubbles. For the SORS instrument, the vial mode was performed by placing a vial containing the oil into the instrument and so only a single spatial offset Raman measurement was made.  
The final series of experiments have been performed through the container. SORS spectra were collected with the handheld Resolve device with the through-barrier measurement setting and the standard SORS nose cone adapter following the manufacturer's recommended protocol. In order to keep the distance of the laser nozzle and sample bottle consistent, a wooden holder was designed and constructed, and this is shown in Figure S1. Four technical replicates were analysed from each mixture in these series. Again, prior to Raman measurements, the oil samples were gently vortexed to ensure complete homogenization and left to rest for 5 min in order to free any trapped air bubbles.
## 2 Materials and Methods
### Air Dose Rate Survey over Downstream Floodplains  
During sediment and river water sampling, it was noted that the air dose rate was much higher over the floodplains compared with the land surface around the sampling site, reflecting radiocesium accumulation on the floodplains. Radiocesium contamination was investigated by measuring the air dose rate distribution over the floodplain and \({}^{137}\)Cs depth profile in downstream areas. The floodplain at the Kinoene Bridge (D2 site) on the Odaka River and cross sectional view along the bridge are shown in Figure 3.  
The Kinoene Bridge is 124 m long with floodplains of 30 m wide on the right side (B-C) and 35 m wide on the left side (D-F), and the river channel is 10 m wide. The air dose rate along the riverbed was surveyed on 27 September 2016 by using a mobile dosimeter GeoGamma-220 manufactured by Geosense Co. Ltd. Japan. This dosimeter measures the air dose rate with a 2\({}^{\prime\prime}\) diameter \(\times\) 2\({}^{\prime\prime}\) long NaI(Tl) scintillator, and Global Positioning System data at 1 s intervals, and records integrated gamma-ray spectral data. The air dose rate measurements were conducted by holding the GeoGamma-220 1 m above the ground and walking over the slope face and the floodplain along A-C and D-F in Figure 3.  
Soil core sampling to a depth 30 cm were performed at the S1 and S2 sites over the floodplain shown in Figure 3. Soil core samples were separated every 5 cm from the surface and \({}^{137}\)Cs concentration was measured.  
Figure 3: (**a**) Horizontal view around the Kinoene Bridge (D2 site) on the Odaka River. Air dose rates were measured along A–C and D–F over the floodplain. Core samples were taken at S1 and S2 sites. (**b**) Cross sectional view along the A–F direction of the Odaka River.
# Estimating CPT Parameters at Unsampled Locations Based on Kriging Interpolation Method
###### Abstract  
The cone penetrometer test (CPT) has been widely used in geotechnical investigations. However, how to use the limited CPT data to reasonably predict the soil parameters of the unsampled regions remains a challenge. In the present study, we adopted the Kriging method to obtain the CPT data of an unsampled location in Adelaide, South Australia, based on the collected CPT data from six soundings around this location. Interpolation results showed that the trend of the estimated parameters is consistent with the trend of parameters of the surrounding points. From the Kriging interpolation result, we further carried out axial bearing capacity calculation of a precast concrete pile using the CPT-based direct method to verify the reliability of the method. The calculated bearing capacity of the pile is 99.6 kN which is very close to the true value of 102.8 kN. Our results demonstrated the effectiveness of the Kriging method in considering the soil spatial variability and predicting soil parameters, which is quite suitable for the application in engineering practice.  
CPT; Kriging interpolation method; spatial variability; pile axial bearing capacity
# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
## 4 Discussion and Limitations
### Error Analysis and Model Performance  
Error analysis indicated that RNN underperformed primarily during sudden, highly nonlinear maneuvers--especially at transition points in DLC tests.  
Key findings from error analysis include:  
* Underestimation of LTR during rapid steering reversals.
* Lag effects due to insufficient time window size for capturing delayed load transfer dynamics.
* Outliers when wheel lift occurred at very low lateral acceleration magnitudes (indicating model overfitting to typical cases).  
Future improvements could involve adopting sequence models with longer memory (e.g., bidirectional LSTMs) or attention-based architectures.
## 2 Basic Principles of Code Tracking  
Figure 1 shows the diagram of a DLL for pseudo-random noise (PRN) code tracking in the GNSS receiver, where the pseudo range measurement is derived. In this model, a non-coherent early-late loop (NELP) is considered, where an early minus late power discriminator is used.  
This work focuses on the performance of code tracking. For the convenience of illustration, assume that the carrier frequency synchronization is perfect. Thus, the input baseband signal of Figure 1 is given as:  
\[r(t)=\sqrt{P}e^{i\theta}d(t-\tau_{0})c(t-\tau_{0})+n(t)+\iota(t) \tag{1}\]  
where \(\theta\) is the phase difference between local carrier and the received carrier, \(P\) is the relative signal power, \(d(t)\) is the binary-coded navigation data, \(c(t)\) is the direct sequence spread spectrum (DSSS) waveform assigned to the exact satellite, and \(\tau_{0}\) is the true value of time of arrival (TOA), which is regarded to be unchanged within the integration duration \(T\).  
Also consider the existence of white Gaussian noise (WGN) \(n(t)\) and non-white Gaussian interference \(\iota(t)\). The complex envelope of the WGN and interference are represented as a stochastic process, satisfying wide-sense stationary, zero-mean, and circularly symmetry, which are independent of the signal, and statistically uncorrelated with each other [11]. The double-sided spectral density of WGN is denoted as \(N_{0}\). The power spectral density (PSD) of the interference is denoted as \(CiG\iota(f)\), with \(Gi(f)\) normalized as \(\int_{-\sigma}^{+\infty}G_{\iota}(f)=1\). To make the model clear and concise, multipath is not currently shown in Equation (1), while the multipath effects will be discussed in the following sections.  
Further, the spreading waveform \(c(t)\) is defined as:  
\[c(t)=\sum_{i=-\infty}^{+\infty}a(i)\varphi(t-iT_{c}) \tag{2}\]  
where \(a(i)\) is the PRN spreading sequence, with the value of \(\pm 1\). \(\varphi(t)\) is the waveform of the current spreading code chip, with the pulse width of \(T_{c}\).  
Figure 1: Block Diagram of Non-coherent Code Tracking Loop, where the discriminator model is early minus late power.  
Then, as shown in Figure 1, the processing of baseband digital signal in the receiver front-end can be equivalent to a transition function \(H(f)\). Here, the assumption of low-pass filter with rectangular amplitude-frequency response is used [11]:  
\[H(f)=\left\{\begin{array}{ll}1,&|f|<\beta_{r}/2\\ 0,&\text{else}\end{array}\right. \tag{3}\]  
where in the double-sideband bandwidth is \(\beta_{r}\). After the filter, the received signal is correlated with three branches of the local despreading signals, with the code phase delay of early, prompt and late respectively. To support various receiving strategies, the local despreading code in the model is described in the next section.
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 3 Methodology
### Algorithm Implementation
#### 3.4.4 Offline Training and Online Predicting  
When the feature engineering process is done, the features are fed to the input layer. To eliminate the effect of index dimension and quantity of data, all features are normalized using the Min-Max Normalization method shown in Equation (14).  
\[x_{norm}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{14}\]  
where \(x_{min}\) and \(x_{max}\) represent the minimum and maximum of a certain feature, respectively.  
Each neuron in the input layer is connected to each LSTM cell in the hidden layer. Note that its number should match the dimension of the input data. In this paper, the activation functions of the input gate, the output gate and the forget gate are selected as the standard logistics sigmoid function shown in Equation (15)  
\[\sigma_{i}(x)=\sigma_{f}(x)=\sigma_{o}(x)=\frac{1}{1+e^{-x}}. \tag{15}\]  
In the recurrent hidden layer, as described in Section 3.3, the \"meaningful parts\" are abstracted from the input time series. The parameters are updated using the Adam optimization algorithm and the BPTT algorithm. These \"higher-level features\" are then combined in the output layer. The output layer is also termed as the dense layer to produce the predicted train speeds of future time steps. As a regression problem, the output layer has only one neuron. In this paper, the optimization of the dense layer can be considered as a linear regression problem, i.e., the activation function of the output layer is set as a linear function. The loss function in this paper adopts the Mean Absolute Error (MAE) loss  
\[L(y,\hat{y})=\frac{1}{l}\sum_{i=1}^{l}|y_{i}-\hat{y}_{i}|. \tag{16}\]  
The offline training phase ends when the error of the algorithm outputs meets the preset threshold. The set of parameters (such as the weight matrices and the biases vectors) can be obtained, which approximately describes the regression function \(h(\cdot)\) in Equation (5).  
In the online predicting phase, the actual data gathered by VOBC are fed to the trained LSTM network. After extracting the raw features, the proposed data-driven algorithm can output the prediction of the next time step, whose functionality can serve as the train dynamic model. In addition, with the feature engineering process, the train speeds for next \(n\) time steps can be obtained.
## 2 System Model and Design Goals
### System Model  
We illustrate an overall architecture of fog computing-enhanced LBS query in Figure 1, which includes a set of mobile users, a set of fog nodes \(\{id_{1},id_{2},\ldots,id_{n}\}\) and an LBS provider.  
* LBS provider: the LBS provider acts as a profit company, providing the location-based services for the registered mobile users. With the advantages of fog computing, the LBS provider prefers to outsource its LBS data containing service content and the corresponding geographic location to appropriate fog nodes based on the geographic location distance, which can provide the low-latency LBS for mobile users.
* Fog nodes \(\{id_{1},id_{2},\ldots,id_{n}\}\): with the pay-as-you-use way, each fog node \(id_{j}\) stores the LBS data from the LBS provider and provides the fine-grained query services for mobile users.
* Mobile users: a mobile user who acts as a registered member of the LBS provider, sends a query that contains the searching content, current location information and searching spatial range, to a local fog node for requesting the nearby POIs satisfying both the given searching content and spatial range.  
Similar to the most common assumption in research literature (see [27; 28]), fog nodes are considered honest-but-curious, which honestly follow the underlying scheme, but are curious about  
Figure 1: System model under consideration.  
the privacy of mobile users and the LBS provider. In the LBS applications, the LBS provider or mobile users would not collude with fog nodes to obtain others' privacy. This is because if the LBS provider agree to collude with a fog node to obtain the content of a user' request, then this node can obtain some LBS data considered as provider's privacy from the LBS searching. For example, if a fog node obtain a mobile user's request (e.g., a hospital within 500 m) from the collusion attack, then it can obtain the real information of the matched LBS data, i.e., it knows that the service content of all matched LBS data is the hospital and the corresponding location is within 500 m of the current location. In other words, once the privacy of mobile users is disclosed, the privacy of the LBS provider will also be disclosed. Therefore, the LBS provider would not collude with fog nodes. Similarly, mobile users would also not collude with fog nodes. Note that, since the privacy preservation is our focus, some active attacks are beyond the scope of this work and will be discussed in the future.
## 2 GEO Grid ASTER EVI Products
### Vegetation Index Module
#### 2.2.2 Evi2--Two-Band Evi (\(Evi_{p}\))  
Recently, another two-band EVI, or EVI2, was proposed, which had been optimized to produce equivalent index values with the original three-band EVI for good observations, _i.e._, good quality pixels that contain no cloud or snow and are atmospherically-corrected over low aerosol quantity [16]. This EVI2 can also be computed with the ASTER red and NIR bands:  
\[EVI_{P}=2.5\cdot\frac{\rho_{ASTER,NIR}-\rho_{ASTER,red}}{\rho_{ASTER,NIR}+ 2.4\cdot\rho_{ASTER,red}+1} \tag{7}\]
## 2 Materials and Methods  
### Microorganisms, Media, and Self-Developed Bioreactor  
_C. necator_ H16 (CGMCC 1.7092) was used in this study and purchased from the China General Microbial Culture Management and Preservation Center. For inoculum preparation, 1 part of \(-\)80 \({}^{\circ}\)C glycerol stored in _C. necator_ medium was inoculated in 25 mL of seed medium (yeast extract 10 g/L, peptone 10 g/L, meat paste 5 g/L, (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) 2 g/L) for 12-32 h. Three test tubes containing 5 mL of seed culture solution were washed twice with sterile water and used as inoculum. For the preparation of secondary seed liquid, the inoculum was poured into an autotrophic medium (KH\({}_{2}\)PO  
Figure 1: CO\({}_{2}\) fixation flow chart of microbial fermentation for SCP production.  
2.4 g/L, Na\({}_{2}\)HPO\({}_{4}\) 2.5 g/L, (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) 2.0 g/L, MgSO\({}_{4}\)-7H\({}_{2}\)O 0.5 g/L, NaHCO\({}_{3}\) 0.5 g/L, ferric ammonium citrate 0.1 g/L, and trace element mixture 1 mL/L. Trace elements H\({}_{3}\)BO\({}_{3}\) 600 mg, CoCl\({}_{2}\)\(\cdot\)H\({}_{2}\)O 400 g, ZnSO\({}_{4}\)\(\cdot\)H\({}_{2}\)O 200 mg, MnCl\({}_{2}\)-4H\({}_{2}\)O 60 mg, NaMoO\({}_{4}\)\(\cdot\)2H\({}_{2}\)O 60 mg, CuSO\({}_{4}\)\(\cdot\)5H\({}_{2}\)O 20 mg). A gas mixture of H\({}_{2}\):O\({}_{2}\):CO\({}_{2}\) = 7:2:1 [39] was introduced and incubated in a full-temperature shaking incubator at 30 \({}^{\circ}\)C until the optical density of 620 nm (OD\({}_{620}\)) \(\approx\) 2 was reached.  
Carbon dioxide was the carbon source. (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) (2.0 g/L) and supplemented ammonia (3 mol/L) were used as the nitrogen sources. The other components of the medium were autotrophic media (KH\({}_{2}\)PO\({}_{4}\) 2.4 g/L, Na\({}_{2}\)HPO\({}_{4}\) 2.5 g/L, (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) 2.0 g/L, MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O 0.5 g/L, NaHCO\({}_{3}\) 0.5 g/L, ferric ammonium citrate 0.1 g/L, and trace element mixture 1 mL/L. Trace elements H\({}_{3}\)BO\({}_{3}\) 600 mg, CoCl\({}_{2}\)\(\cdot\)H\({}_{2}\)O 400 g, ZnSO\({}_{4}\)\(\cdot\)H\({}_{2}\)O 200 mg, MnCl\({}_{2}\)\(\cdot\)4H\({}_{2}\)O 60 mg, NaMoO\({}_{4}\)\(\cdot\)2H\({}_{2}\)O 60 mg, CuSO\({}_{4}\)\(\cdot\)5H\({}_{2}\)O 20 mg). Ammonia (2 mol/L) adjusted the pH to about 6.8.  
In a 5 L self-developed bioreactor, fed-batch and continuous cultures were maintained at a working volume of 3 L. A bioreactor (BLBIO-5 GJ-2, BLBIO, Shanghai, China) equipped with temperature, pH (Easy Ferm plus PHI Arc 120, HAMILTON, Bonaduz, Switzerland), dissolved oxygen (VisiFerm DO Arc 120 H2, HAMILTON), and biomass sensors (Dencyte RS485 120, HAMILTON, Bonaduz, Switzerland) was used. This was a self-developed bioreactor with a gas exchanger and fermenter body. Sensors for pH, temperature, optical density (OD), and dissolved oxygen (DO) and a sampling port are all included in the fermenter's main body. The gas exchange device is separated into two layers, with material being able to be placed in the upper and lower layers. The section on fermentation in a self-developed bioreactor discusses process control.
# A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty
## 2 Theorical Background
### Affine Arithmetic  
Affine arithmetic is a numerical computation model for interval methods that overcomes one of the main obstacles of traditional interval arithmetic (the dependency problem). That is, standard interval arithmetic procedures tend to overestimate the range of a function (\(f\)) defined over intervals, and with an increase of the magnitude of the problem as \(f\) becomes more complex. AA is a self-validated numerical computation model that relies on first order correlations between computed and input quantities. This allows the computation of the numerical error of a given quantity, something that is very useful when lower and upper bounds of a given function are analyzed. [PERSON] and [PERSON] [62] present the main concepts of AA. We follow their notation and definitions in this paper.  
In AA, a quantity (or ideal variable) \(x\) is represented by a first order polynomial (i.e., an affine form) \(\hat{x}\) given in Equation (1). In this equation, \(x_{0}\) represents the central value.  
_Noise symbols_\(\varepsilon_{n}\) represent independent components of the total uncertainty of variable \(x\), whose values are unknown but restricted to the interval \([-1,+1]\). Finally, \(x_{n}\) are the _partial deviations_ associated with each \(\varepsilon_{n}\) term. These terms represent the magnitude of the uncertainty of the corresponding \(\varepsilon_{n}\) component:  
\[\hat{x}=x_{0}+x_{1}\varepsilon_{1}+x_{2}\varepsilon_{2}\cdots x_{n}\varepsilon _{n}=x_{0}+\sum_{1}^{n}x_{n}\varepsilon_{n} \tag{1}\]  
For instance, Equation (2) corresponds to the AA representation (\(\hat{P}_{g}\)) of the output of a generation unit \(P_{g}\) while considering both the uncertainty of wind generation and demand. In this equation, \(P_{g0}\) is the central value of the generation variable \(\hat{P}_{g}\), whereas \(P_{g,n}\) are the deviations of the variable due to demand forecasting errors (\(\varepsilon_{n}\)), and \(P_{g,w}\) are the deviations of the variable caused by forecasting errors of wind generation (\(\varepsilon_{w}\)):  
\[\hat{P}_{g}=P_{g0}+\sum_{n=1}^{N}P_{g,n}\cdot\varepsilon_{n}+\sum_{w=1}^{N}P_ {g,w}\cdot\varepsilon_{w}. \tag{2}\]  
It is worth noting that affine forms enable the computation of interval bounds. Therefore, a quantity or variable \(x\) represented in the affine form of Equation (1) is bounded in the interval \(x\in[x_{0}-r_{x},x_{0}+r_{x}]\), where the _total deviation_\(r_{x}\) is defined as \(r_{x}=\sum_{i=1}^{n}|x_{i}|\).