esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmented Composite Optical Parametric Amplification
## 2 Theory
In the case \(\Delta k=0\), and for a vanishing idler wave at the input (\(A_{2}(0)=0\)), from the last equations one can easily see that both signal and idler increase initially exponentially in a parallel way as  
\[\left|A_{1}\left(z\right)\right|\approx\left|A_{2}\left(z\right)\right|\approx \frac{\left|A_{1}\left(0\right)\right|}{2}\exp\left[\Omega z\right] \tag{5}\]  
In this work, we are interested in an optimization of the signal intensity amplification factor \(a\), defined as the ratio of the intensity of the signal wave (\(A_{1}\)) taken at distance \(z\) to its intensity at the entrance of the crystal  
\[a=\frac{\left|A_{1}\left(z\right)\right|^{2}}{\left|A_{1}\left(0\right)\right| ^{2}}=\frac{I_{1}\left(z\right)}{I_{1}\left(0\right)}. \tag{6}\]  
The above argumentation, in connection with Equation (5), indicates that, when the phase-matching condition is satisfied (\(\Delta k=0\)), the OPA process is the most efficient. However, this is not entirely true, because the solutions (5) are derived only in the limit of the undepleted pump approximation. If one considers the depleted pump regime, the nonlinear Equation (1) has solutions in terms of Jacobi elliptic functions [21, 22]. These, like trigonometric functions, are periodic functions, and thus the energy transfer oscillates back and forth between pump field and signal field. The chirped quasi-phase-matching techniques [5, 6, 7, 8, 9], in addition to the improvement of the bandwidth, eliminate the problem of back conversion and can be used even in the case of the depleted pump regime. Here, in analogy with the technique of composite pulses from quantum physics [11, 12, 13, 23, 24, 25, 26, 27, 28], we propose to use segmented composite crystals for OPA. We note that the composite pulse analogy was already used in nonlinear optics, but for the sum frequency generation or second harmonic generation (SHG) [29, 30, 31]. In these cases, in the undepleted pump approximation the differential equations governing the spatial dynamics have a SU(2) symmetry [32, 33], which is exactly the same symmetry possessed by quantum systems with two states [27, 28]. Therefore, the mapping between two-state quantum systems and SFG in the undepleted pump regime is complete and one can use the known analytic solutions from quantum physics to find robust solutions in nonlinear optics [29, 30, 31]. In the case of OPA, there is no SU(2) symmetry and it is thus not possible to exploit known composite pulses analytic solutions. Instead, we are going to derive numerically solutions that achieve broadband amplification bandwidth together with high amplification in the depleted pump regime.
# Control of DC Motors to Guide Unmanned Underwater Vehicles
## 3 Results
### Comparison of Estimation Accuracy  
This section compiles the estimation error performance for each case investigated. Table 1 displays coefficient estimation accuracy. Little differences can be discerned between the ability of recursive least squares-based estimation of parameters with respect to mean and standard deviation of estimation errors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Estimates 1**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Mean, \(\mu\)**} & \multicolumn{2}{c}{**Standard Deviation, \(\sigma\)**} \\ \hline  & \(\mu_{\hat{a}_{1}}\) & \(0.19537\) & \(\sigma_{\hat{a}_{1}}\) & \(0.1753\) \\ Indirect self-tuner without process zero & \(\mu_{\hat{a}_{2}}\) & \(-0.02096\) & \(\sigma_{\hat{a}_{2}}\) & \(0.12801\) \\ cancellation (minimum phase plant) & \(\mu_{\hat{b}_{1}}\) & \(0.019307\) & \(\sigma_{\hat{b}_{1}}\) & \(0.01283\) \\  & \(\mu_{\hat{b}_{2}}\) & \(-0.02905\) & \(\sigma_{\hat{b}_{2}}\) & \(0.02060\) \\  & \(\mu_{\hat{a}_{1}}\) & \(-0.00957\) & \(\sigma_{\hat{a}_{1}}\) & \(0.16784\) \\ Indirect self-tuner without process zero & \(\mu_{\hat{a}_{2}}\) & \(0.051825\) & \(\sigma_{\hat{a}_{2}}\) & \(0.37542\) \\ cancellation (non-minimum phase plant) & \(\mu_{\hat{b}_{1}}\) & \(-0.22845\) & \(\sigma_{\hat{b}_{1}}\) & \(1.1204\) \\  & \(\mu_{\hat{b}_{2}}\) & \(0.42768\) & \(\sigma_{\hat{b}_{2}}\) & \(1.9589\) \\  & \(\mu_{\hat{b}_{1}}\) & \(0.001976\) & \(\sigma_{\hat{b}_{1}}\) & \(0.06214\) \\ Direct self-tuner with filtering (all process zeros cancelled) & \(\mu_{\hat{b}_{1}}\) & \(-0.03805\) & \(\sigma_{\hat{b}_{1}}\) & \(0.66009\) \\  & \(\mu_{\hat{b}_{0}}\) & \(-0.03052\) & \(\sigma_{\hat{b}_{1}}\) & \(0.6132\) \\  & \(\mu_{\hat{b}_{0}}\) & \(0.039206\) & \(\sigma_{\hat{b}_{1}}\) & \(0.68119\) \\  & \(\mu_{\hat{b}_{1}}\) & \(-0.03256\) & \(\sigma_{\hat{a}_{1}}\) & \(0.20615\) \\ Deterministic artificial intelligence & \(\mu_{\hat{a}_{2}}\) & \(0.019014\) & \(\sigma_{\hat{a}_{2}}\) & \(0.10641\) \\  & \(\mu_{\hat{b}_{1}}\) & \(0.19678\) & \(\sigma_{\hat{b}_{1}}\) & \(0.00019\) \\  & \(\mu_{\hat{b}_{2}}\) & \(0.1974\) & \(\sigma_{\hat{b}_{2}}\) & \(0.00627\) \\ \hline \hline \end{tabular}  
* Estimation error comparison of proposed deterministic artificial intelligence (D.A.I.) method to formerly compared methods in [11].  
\end{table}
Table 1: Coefficient estimation accuracy of all four methods investigated.  
Figure 8: Deterministic artificial intelligence (where timesteps appear on the abscissa). (**a**) Command trajectory tracking by deterministic artificial intelligence with noteworthy comparable initial overshoot, but seemingly eliminating overshoot and settling at each switch of the square wave. (**b**) Coefficient estimation illustrating rapid convergence towards the correct coefficient value with continued convergence at very small scale with time passage.
## 3 Methodology
### Motivations and Deployment Considerations
#### 3.2.2 Separate Authentication Communication Scheme  
Adopting a separate authentication communication scheme in VANETs offers significant advantages over traditional approaches. The system becomes more resilient to attacks and unauthorized access by decoupling the authentication process from other communication protocols. This separation of authentication ensures the integrity and authenticity of participating vehicles and infrastructure units, fostering a secure and trusted communication environment.
# Assessment of Hongtu-1 Multi-Static X-Band SAR Constellation Interferometry
## 2 Materials and Methods
### Co-Registration of Multi-Static SLCs to Monostatic Reference  
The three multi-static SLCs were co-registered to the monostatic reference SLC. In the first step, a lookup table between the bistatic image and the monostatic reference was calculated, considering the individual orbit data and the terrain height, as available from the 1\" Copernicus DEM [25]. This lookup table was then used to resample the reference image to the bistatic image. Then, the offsets between the resampled reference and the bistatic image were estimated and used to determine a polynomial offset refinement. The refinement polynomial was used to update the co-registration lookup table. For the co-registered SLCs, we again estimated the offsets using matching techniques to check the co-registration quality. The standard deviation of these offsets was below 0.01 SLC pixels, confirming very high co-registration quality.
# Runway Pavement Structural Analysis Using Remote Laser Doppler Vibrometers
## 3 Compensation Methods
### Vibration Compensation
#### 3.2.1 Experimental Validation  
The goal of this validation experiment is to assess the reliability of the vibration compensation algorithm and refine it based on the resultant data. The experimental setting consisted of two indoor-scaled cement concrete slabs of 2.5 m \(\times\) 2.5 m \(\times\) 0.26 m. The Heavy Weight Deflectometer (HWD) was utilized to simulate the dynamic loads of an aircraft on the concrete slabs. Two sets of RLDVs were divided into control and experimental groups to measure the vibration at the identical point on the slab during HWD operation. Given the RLDV's capability to measure with nanometer-level precision, one was selected for the control group and affixed to a long beam at an elevated position. The ends of these beams were secured to a steel frame outside of the experimental site to minimize vibrational influence. The RLDV and accelerometer of the experimental group were affixed onto the fixed-end beams and used to measure the ground vibration and the vibration of the fixed-end beams, respectively. The fixed-end beams of specified dimensions were securely attached both ends to the concrete pairs of 0.25 m in height. The accelerometer features a measuring range of 50 g and a sensitivity of 100 mV/g. The experiments were conducted 30 times under respective loads of 80 kN, 100 kN, 120 kN, 140 kN, and 160 kN. Field testing is depicted in Figure 5. To ease installation, the RLDV optical head was chosen for these experiments.  
Figure 4: Vibration experiment data collection results. (**a**) Condition 1: the distance between the RLDV and the observation point is 15 m; (**b**) Condition 2: the distance between the RLDV and the observation point is 25 m; (**c**) Condition 3: the distance between the RLDV and the observation point is 35 m; (**d**) Condition 4: the distance between RLDV and the observation point is 35 m and with reflector.
# Analysis of ICS and SCADA Systems Attacks Using Honeypots
###### Abstract  
Supervisory control and data acquisition (SCADA) attacks have increased due to the digital transformation of many industrial control systems (ICS). Operational technology (OT) operators should use the defense-in-depth concept to secure their operations from cyber attacks and reduce the surface that can be attacked. Layers of security, such as firewalls, endpoint solutions, honeypots, etc., should be used to secure traditional IT systems. The three main goals of IT cybersecurity are confidentiality, integrity, and availability (CIA), but these three goals have different levels of importance in the operational technology (OT) industry. Availability comes before confidentiality and integrity because of the criticality of business in OT. One of the layers of security in both IT and OT is honeypots. SCADA honeypots are used as a layer of security to mitigate attacks, known attackers' techniques, and network and system weaknesses that attackers may use, and to mitigate these vulnerabilities. In this paper, we use SCADA honeypots for early detection of potential malicious tampering within a SCADA device network, and to determine threats against ICS/SCADA networks. An analysis of SCADA honeypots gives us the ability to know which protocols are most commonly attacked, and attackers' behaviors, locations, and goals. We use an ICS/SCADA honeypot called Conpot, which simulates real ICS/SCADA systems with some ICS protocols and ICS/SCADA PLCs.  
critical infrastructure; cyber security; Conpot; defense in depth (DiD); honeynet; operational technology (OT); industrial control systems (ICS); incident response; PLC; SCADA +
Footnote †: journal:  
0023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 Materials and Methods
### Kinetic Assessments  
The kinetics of biomethane production, as an invaluable tool to enhance the understanding of the AD process were investigated using three major models of modified gompertz model [41], cone model [42] and exponential (first order) model [9]. These models are able to elucidate the dynamics of biomethane production during AD processes [43]. The cone model and the exponential model adequately describe the kinetics of biomethane production based on the solubilisation (hydrolysis) rate constant. However, if biomethane production is characterized by a more traditional bacterial growth curve with a sigmoidal profile, the modified gompertz model is considered appropriate [9; 41; 42]. The selection of the correct kinetic model for reliable biomethane potential prediction is not a trivial enterprise, as the precision of the models differs with the operating conditions imposed during the AD process and the nature of substrate digested [43].  
The modified gompertz model, cone model and exponential (first order) model are presented in Equation (3) [41], Equation (4) [42] and Equation (5) [9], respectively, as follows:  
\[B_{t}=B_{\text{max}}\times\exp\biggl{(}-\exp\biggl{[}\frac{2.7183\times R_{m}}{ B_{\text{max}}}(\lambda-t)+1\biggr{]}\biggr{)} \tag{3}\]  
\[B_{t}=\frac{B_{\text{max}}}{1+(kt)^{-n}} \tag{4}\]  
\[B_{t}=B_{\text{max}}\Bigl{(}1-e^{(-k(t-\lambda))}\Bigr{)} \tag{5}\]  
In Equations (3)-(5), \(B_{t}\) is the cumulative biomethane potential in mL/gVS\({}_{\text{added}}\) at time \(t\) in days, \(B_{max}\) is the maximum possible cumulative biomethane potential produced after an infinite number of days in mL/gVS\({}_{\text{added}}\), \(k\) is the biomethane production rate constant in d\({}^{-1}\), \(R_{m}\) is the maximum biomethane production rate in mL/g-VS\({}_{\text{added}}\)-d and \(\lambda\) and \(n\) represent the initial delay period in the degradation process in days and the shape factor, respectively, and are both dimensionless parameters.  
The kinetic model parameters were determined using the nonlinear least squares regression tool in Matlab computing package (Version R2015a, the Math Works Inc., Natick, MA, USA). The suitability of the three alternative models listed above in predicting the biomethane potential was statistically assessed via a combined comparison of the root mean square errors (RMSE) and the coefficient of determination values (R\({}^{2}\)) of the different biomethane production kinetic models. Generally, the preferred model presents the lowest value of RMSE and highest value of R\({}^{2}\). The relation for the root means square errors (RMSE) is expressed as follows:  
\[\text{RMSE}=\left(\frac{1}{n}\sum_{i=1}^{n}(P_{i}-Y_{i})^{2}\right)^{1/2} \tag{6}\]  
where \(n\) is the number of data pairs, \(P_{i}\) is the biomethane potential predicted by the models in mL/gVS\({}_{\text{added}}\) on the \(i\)th day and \(Y_{i}\) is the measured biomethane potential in mL/gVS\({}_{\text{added}}\) on the \(i\)th day.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Analysis and Discussion
#### 4.3.3 Language Individuality Not Fully Used  
From the technical history of the three languages, it can be seen that the most-significant performance improvement has been due to the evolution of acoustic models developed in common languages, such as the DNN-HMM, Transformer, etc. Even for the techniques developed for low-resource language speech recognition, such as transfer learning and pre-training, they are not specific to Uyghur or Kazakh. At present, we have not found much improvement being attributed to a \"special design\" for the three languages (using morphemes as LM units could be seen as a special treatment for agglutinative languages, but subword units are also known in English ASR).  
This observation should be interpreted from two perspectives. On the positive side, this indicates that it is indeed possible to implement low-resource language speech recognition by improving the general speech recognition technology; at least the most-effective techniques at present can be applied to various languages with no difference. This brings hope to overcome human language barriers: it is not necessary for us to study 7000+ languages one by one, and we can only select some representative languages to develop the general technologies and, then, generalize them to other languages. On the negative side, this means that the language individualities of Uyghur, Kazakh, and Kyrgyz have not been seriously considered in the current research. For instance, we know that people write words in the way that they want to pronounce them, due to a simple letter-to-phoneme mapping. However, pronunciation may vary significantly due to accents or personal habits, leading to uncertainty in word forms. This has caused serious problems in language modeling, but little has been done to resolve it. Other language individualities such as harmony rules, vowel weakening patterns, and phonological constraints have not been well treated in the present research.
## 4 Methodology
### Proposed Model
#### 4.1.2 Transformer Module  
The Transformer module facilitates self-attention and context modeling within the network, enabling long-range dependency capture and feature refinement. It consists of multi-head attention mechanisms and positional encodings to capture global context information. The Transformer module is mathematically represented as:  
\[\text{MultiHeadAttention}(Q,K,V)=\text{Concat}(\text{head}_{1},\dots,\text{ head}_{h})W^{O} \tag{5}\]  
\[\text{where head}_{i}=\text{Attention}(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}) \tag{6}\]  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{7}\]  
\[\text{Output}=\text{LayerNorm}(X+\text{MultiHeadAttention}(X)) \tag{8}\]  
where \(Q\), \(K\), and \(V\) represent the query, key, and value matrices, respectively. \(W_{i}^{Q}\), \(W_{i}^{K}\), and \(W_{i}^{V}\) denote the learnable weights for the \(i\)-th head of the multi-head attention mechanism. \(W^{O}\) represents the output weight matrix. The Transformer module enhances feature representations by capturing long-range dependencies and contextual relationships, leading to improved vessel segmentation accuracy.  
Feature refinement in segmentation tasks. The module gathers long-range dependencies and global context in the network using positional encodings and multi-head attention methods. Different areas of the input feature maps are attended to by each attention head, enabling parallel processing and effective acquisition of pertinent data at various scales. By calculating attention weights based on the similarity between the query and key vectors, the attention mechanism allows the network to suppress unimportant features and noise, allowing it to concentrate on informative regions. The Transformer module improves information flow throughout the network and guarantees stable training by implementing residual connections and layer normalization.
## 4 Experimental Results
### Defect Shape Mapping Based on C-scan Imaging
The time difference of defects 1, 2, and 3 in the two directions are shown in Table 2. The preceding equation was used to calculate the size of the defects. The estimated size of the defects was slightly different from the actual size (15 mm). The width of the defects could be calculated according to the scanning path, which is shown in Table 2. Unfortunately, the estimated width was much bigger than the actual width of the defects. However, with the augmentation of the actual defect widths, the estimated width increased. From what has been discussed, we can draw the conclusion that the proposed method provides us with an effective tool to estimate the defect's dimension. To evaluate the performance of the methods in two directions, the results of the C-scan from the two directions were compared. Table 3 shows the maximum and minimum amplitudes of the defects in different directions. Obviously, the maximum amplitude of the defect in the direction of magnetic flux was larger than the maximum amplitude in the direction of the excitation current, and the minimum amplitude of the defect in the direction of magnetic flux was smaller than the minimum amplitude in the direction of the excitation current. In other words, defect recognition in the direction of magnetic flux was better than defect recognition in the direction of the excitation current. Therefore, defect shape mapping was only carried out in the direction of magnetic induction flux. \begin{table}
\begin{tabular}{c c c c c} \hline
**Direction** & **Item** & **Defect 1** & **Defect 2** & **Defect 3** \\ \hline \multirow{3}{*}{Magnetic induction flux} & Time difference (ms) & 220 & 260 & 260 \\  & Length (mm) & 13.2 & 15.6 & 15.6 \\  & Width (mm) & 7.5 & 6.5 & 5 \\ \hline \multirow{3}{*}{Exciting current} & Time difference (ms) & 250 & 250 & 290 \\  & Length (mm) & 15 & 15 & 17.4 \\ \cline{1-1}  & Width (mm) & 7 & 5.5 & 4.5 \\ \hline \end{tabular}
\end{table} 
Table 2: Estimated length and width. Figure 13: C-scan outline images of defect 3 in the direction of the exciting current.
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 3 Existing Contention-Based Random-Access Mechanism  
When a UE is switched on or awakens, it initially synchronizes with the DL channels by reading the primary synchronization signal (PSS) and secondary synchronization signal (SSS) from the eNB. The UE separates the Master Information Block (MIB) at that point, which contains data on the DL and UL carriers' configuration so that the eNB receives data from the Sender Information Block (SIB). All RA parameters are included in this SIB, such as the number of available RA slots, RA preamble classes, and preamble setup. Subsequently, UEs generate CB-RA transmission attempts in order to decode the SIB. For association initialization in a 5G network, the CB-RA conducts four main phases. Figure 3 shows a CB-RA procedure in a 5G network.
# Cross-Camera Erased Feature Learning for Unsupervised Person Re-Identification
## 4 Results
### Experimental Details Analysis  
The effect of the parameters \(t\) in loss function \(L_{G}\). We perform experiments on two datasets (Market-1501 and DukeMTMC-reID), and used Rank-1 accuracy as the evaluation index. If the value of \(t\) is too small, CEFL may choose a negative sample which has the same identity of the query image. The wrong selection of negative sample can reduce the classification ability of CEFL. The performance of the model can be reduced if the value of \(t\) is too large, as shown in Figure 5. Setting appropriate values is more helpful to find negative samples with different identity. Experiments show that the CEFL network performance better than others when \(t\) is set to 10, as shown in Figure 5.  
The effect of the parameters \(k\) in loss function \(L_{B}\). We perform experiments on two datasets (Market-1501 and DukeMTMC-reID), and used Rank-1 accuracy as the evaluation index. The value of \(k\) means that there are \(k\) erased features are regarded as similar features of the target pedestrian features. If the value of \(k\) is too large, features with different ID may be judged into the same class, which resulting in performance degradation. Similarly, if the value of \(k\) is too small, CEFL may lose some similar features. Losing similar features is not conducive to the subsequent process of CEFL network. In CEFL, we set different values of \(k\) to judge the influence of the parameters \(k\). Experiments on two dataset show that the CEFL performance better than others when the value of \(k\) is set to 25, as shown in Figure 6.  
In order to further explore the impact of CEFL, we evaluate the effects of three network components on two classic datasets respectively. Experiments on two dataset show that the CEFL performance better when the three network branches work together, as shown in Table 3. We test Cross-camera global feature Learning (CL) and Erased partial feature Learning (EL), as shown in Table 3. GL classifies features by minimizing intra-class gaps while maximizing inter-class gaps in global feature space. In EL  
Figure 5: The effect of the parameters \(t\) in loss function \(L_{G}\).  
Figure 6: The effect of the parameters \(k\) in loss function \(L_{B}\).  
similar erased feature vectors are pulled together and dissimilar features are pushed away. The result of 'GL+EL' is clearly better than 'CL' or 'EL' on both datasets. This is because combining 'GL' and 'EL' can provide more effective learning guidance to the network. The result of CEFL shows the best performance on two datasets. This is because joint global and partial feature learning can combine global features and erased features together.