esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 3 Security of TinyJambu against Cryptanalytic Attacks
### The Concept of Correlated AND Gates  
As stated in the original document of TinyJambu [6], the designers looked for differential trails with a minimum number of active _AND_ gates. Nevertheless, they considered that all the _AND_ gates are independent. By contrast, in [7], the authors analyzed the possible correlations of the _AND_ gates that modify the number of active _AND_ gates along the keyed permutation \(P_{l}\).  
Indeed, according to Figure 2, the separation between cells entering the _NAND_ gate is \(15\). This means that if a bit \(a\) in cell \(85\) enters the gate at a particular round, then \(15\) rounds later, the same bit will enter the same gate in cell \(70\). More precisely, according to the TinyJambu recurrence relationship, bit \(S_{85}\) will be an input of the _NAND_ gate with bit \(S_{70}\), but after \(15\) rounds, \(S_{85}\) will also be an input of the gate with bit \(S_{100}\). In reference [7], the authors studied the possible correlations between the logic products \(\big{(}S_{85+j}\cdot S_{70+j}\big{)}\) and \(\big{(}S_{100+j}\cdot S_{85+j}\big{)}\) when the bits of the register shift along the successive rounds.  
Let \(\Delta\big{(}S_{85+j}\cdot S_{70+j}\big{)}\) and \(\Delta\big{(}S_{100+j}\cdot S_{85+j}\big{)}\) denote the output difference of the _AND_ gate for inputs \(\big{(}S_{85+j},S_{70+j}\big{)}\) and \(\big{(}S_{100+j},S_{85+j}\big{)}\), respectively. In [7] (pp. 161-162), it was proved that if \(\big{(}\Delta S_{100+j},\ \Delta S_{85+j},\ \Delta S_{70+j}\big{)}=(1,\ 0,\ 1)\) and \(S_{85+j}=1\) for any \(j\) through the permutation \(P_{l}\), then  
\[\Delta\big{(}S_{85+j}\cdot S_{70+j}\big{)}=\Delta\big{(}S_{100+j}\cdot S_{85+j} \big{)}=1. \tag{8}\]  
That is, \(\Delta\big{(}S_{85+j}\cdot S_{70+j}\big{)}=1\) is an active \(AND\) gate, but 15 rounds later, \(\Delta\big{(}S_{100+j}\cdot S_{85+j}\big{)}=1\) is an active \(AND\) gate too. Both differentials will be active \(AND\) gates or not depending on the value of \(S_{85+j}\). In fact, if \(S_{85+j}=1\), then Equation (8) holds, and there are two \(AND\) gates. On the other hand, if \(S_{85+j}=0\), then \(\Delta\big{(}S_{85+j}\cdot S_{70+j}\big{)}=\Delta\big{(}S_{100+j}\cdot S_{85+j }\big{)}=0\), and there are no active \(AND\) gates. Both cases happen with probability \(2^{-1}\), as it depends on a single bit. Under these conditions, both active \(AND\) gates exist or not jointly, and both gates can be counted as a single active \(AND\) gate. This is a different and more favorable way of counting the number of active \(AND\)s. Thus, in this more refined model proposed in [7], the number X of active gates is reduced through \(l\) rounds, and consequently, the success probability \(p=2^{-X}\) of a differential attack is incremented.
# SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
## 4 Experiments
### Results
#### 4.2.3 Ablation Studies  
Ablation studies were conducted on the NYU Depth V2 dataset to explore the impact of the changing local attention window radius, and the encoder output feature \(F_{ori}\) without being up-sampled. We compare the model performance, training time and inference GPU memory use with a different local window radius in Table 3. It shows that setting the radius to 1 led to the best performance, the shortest training time and the smallest memory use.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Methods** & \multicolumn{4}{c}{**Our SPDepth + PP**} & \multicolumn{6}{c}{**P\({}^{2}\)Net [1] + PP**} \\ \hline \multirow{2}{*}{**Scene**} & \multirow{2}{*}{**REL \(\downarrow\)**} & \multirow{2}{*}{**RMS \(\downarrow\)**} & \multirow{2}{*}{**Log10 \(\downarrow\)**} & \multicolumn{2}{c}{**\(\mathbf{\delta<1.25\)**} & \(\mathbf{\delta<}\)**} & \multicolumn{2}{c}{\(\mathbf{\delta<}\)**} \\  & & & & \(\uparrow\) & \(\mathbf{1.25^{2}\uparrow}\) & \(\mathbf{1.25^{3}\uparrow}\) & REL \(\downarrow\) & RMS \(\downarrow\) & Log10 \(\downarrow\) & \(\uparrow\) & \(\mathbf{1.25^{2}\uparrow}\) & \(\mathbf{1.25^{3}\uparrow}\) \\ \hline Chess & 0.190 & 0.420 & 0.082 & 0.667 & 0.939 & **0.993** & **0.183** & **0.408** & **0.081** & **0.669** & **0.940** & **0.993** \\ Fire & 0.163 & 0.305 & 0.070 & 0.741 & 0.960 & **0.995** & **0.157** & **0.291** & **0.068** & **0.767** & **0.965** & **0.994** \\ Heads & **0.187** & **0.194** & **0.079** & **0.707** & **0.927** & **0.982** & **0.187** & **0.197** & **0.079** & **0.701** & **0.924** & **0.982** \\ Office & 0.159 & 0.360 & 0.067 & 0.768 & 0.966 & 0.996 & **0.156** & **0.351** & **0.065** & **0.775** & **0.970** & **0.997** \\ Pumpkin & **0.132** & **0.361** & **0.058** & **0.831** & **0.978** & **0.996** & 0.141 & 0.380 & 0.062 & 0.797 & 0.977 & 0.995 \\ RedKitchen & **0.163** & **0.399** & **0.070** & **0.749** & **0.953** & 0.993 & 0.165 & 0.404 & 0.072 & 0.735 & 0.951 & **0.994** \\ Stairs & 0.174 & 0.486 & 0.075 & 0.748 & 0.899 & 0.964 & **0.158** & **0.454** & **0.068** & **0.767** & **0.911** & **0.971** \\ Average & 0.164 & 0.370 & **0.070** & **0.750** & 0.953 & 0.992 & **0.162** & **0.367** & **0.070** & 0.747 & **0.955** & **0.993** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Zero-shot generalization results of our SPDepth and P\({}^{2}\)Net [1] on the 7-Scenes dataset. PP represents postprocessing. The best results are shown in bold. \(\downarrow\) means the lower the value, the better. \(\uparrow\) means the higher the value, the better.  
Figure 6: Visualization of generalized results in the scene Stairs of the 7-Scenes dataset. (**a**) RGB image; (**b**) P\({}^{2}\)Net [1]; (**c**) our SPDepth; (**d**) ground truth.
# Application of a Local Dynamic Model of Large Eddy Simulation to a Marine Propeller Wake
## 4 Results and Discussion
### Mean Axial Velocity Profiles  
The azimuthal-averaged profile of relative axial velocity at twelve locations \(x/D=2,-1.8,-1,-0.5,0.5,1,3,4,5,6,7,7.8\) are shown in Figure 13 respectively for results of LES-LDKSGS (black solid line), LES-DASGS (black dashed line, only at \(x/D=2\)) and RANS-kEpsilon models with different refinements (red solid line is more refined than red dashed line). For the sake of simplification, the result of LES-LDKSGS model are given only at \(x/D=2\). Note that the number of grids in LES-DASGS simulation is much higher than in LES-LDKSGS simulation. The LES-DASGS simulation uses about 181 million grid cells and the LES-LDKSGS simulation uses about 8.5 million grid cells. However, the results of the two different large eddy simulations (LES-LDKSGS and LES-DASGS) are very close. That is to say, we get basically the same result with less computational cost. To some extent, this also shows the superiority of the method LES-LDKSGS. The results of RANS-kEpsilon are obviously different from those of LES-LDKSGS, even when the same refinement is used. It is obvious that the RANS-kEpsilon model has a smooth effect on the wake and shows more axial velocity dissipation. The result of the RANS-kEpsilon model tend to be closer to the results of LES-LDKSGS model with the increase of refinement in the near field from about \(x=-1.8D\) to \(x=2D\), but the opposite situation does occur about \(x\geq 2D\) downstream of the far field. This result has important practical significance, that is, when we do not pay attention to the near field, in the RANS simulations, it may not be necessarily true that the denser the grid, the better the calculation results.  
Figure 11: Normalized components of velocity and average circulation at \(x=0.12D\) downstream of propeller (case 1).
## 2 Background and Literature Study
### Necessity of Data Security and Privacy
It aims to steal data, disrupt operations, or gain unauthorised access to systems. Supply Chain Attack: A type of cyber-attack where attackers target the less secure elements of a supply chain to infiltrate an organisation. It exploits vulnerabilities in the supply chain network, which can include software vendors, service providers, or other third-party partners. By compromising one of these entities, attackers can gain access to the primary target's systems and data. Social Engineering Attack: This kind of attack aims to manipulate individuals into divulging confidential information, performing actions, or granting access to secure systems. These attacks exploit human psychology rather than technical vulnerabilities, making them particularly effective. Missonfiguration Attack: This kind of attack exploits vulnerabilities arising from improperly configured systems, networks, systems, or applications. These misconfigurations can occur due to errors, oversights, or lack of security best practices during the setup or maintenance of systems. DoS/DDoS Attack: A malicious attempt to disrupt the normal functioning of a targeted server, service, or network by overwhelming it with a flood of internet traffic. The goal is to make the service unavailable to legitimate users by consuming its resources or causing it to crash. It can also be a protocol-based (SYN flood or Ping flood) or application-based (http GET/POST floods) attack.
## 2 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & **Equation** & **R2** & **Within-Day (\% RSD)** & **Between-Days (\% RSD)** & **LOD (\(\upmu\)g/mL)** & **LOQ** (\(\upmu\)g/mL)** \\ \hline
2-phenylethyllamine & y = 3.3 \(\times\) 10\({}^{3}\)x + 28 & 0.9988 & 3.6 & 7.3 & 0.03 & 0.10 \\ \hline cadaverine & y = 1.4 \(\times\) 10\({}^{3}\)x \(-\) 33 & 0.9981 & 5.8 & 8.1 & 0.12 & 0.40 \\ \hline histamine & y = 0.2 \(\times\) 10\({}^{2}\)x \(-\) 8 & 0.9988 & 6.2 & 9.9 & 0.17 & 0.57 \\ \hline isobutylamine & y = 0.9 \(\times\) 10\({}^{3}\)x \(-\) 98 & 0.9987 & 3.7 & 10.3 & 0.95 & 3.16 \\ \hline isopentylamine & y = 0.8 \(\times\) 10\({}^{3}\)x + 24 & 0.9984 & 4.3 & 7.2 & 0.06 & 0.19 \\ \hline propylamine & y = 1.7 \(\times\) 10\({}^{3}\)x + 96 & 0.9995 & 6.1 & 14.3 & 0.21 & 0.70 \\ \hline putrescine & y = 8.0 \(\times\) 10\({}^{3}\)x \(-\) 94 & 0.9981 & 4.5 & 11.4 & 0.03 & 0.10 \\ \hline tryptamine & y = 3.1 \(\times\) 10\({}^{3}\)x + 22 & 0.9951 & 4.6 & 11.4 & 0.07 & 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Equations, coefficient of correlations, precision and limits of BAs with SPME-GC-MS.
## 4 GNN-Based Digital Twins for Next-Generation Networks
### Major Benefits of Using GNN-Based Digital Twins in Next-Generation Networks
#### 4.1.2 Low-Cost Trials  
With the capability to accurately model a dynamic complex system, DT equipped with data-driven methodologies is a key technology for the exact simulation and cost-effective trials on network systems [1; 27; 109]. Moreover, since the network is naturally represented as graphs, it is efficient to leverage GNNs for better analyzing the network systems. Provided with the data collected in real time from the physical system, an NDT empowered by GNN-based models can be trained to capture the behavior patterns of the network in diverse situations. This enables the NDT to play a critical role in low-cost trials where operators can interact with the network, try out several combinations of configurations or various scenarios; the NDT can still provide the predicted behaviors of the network with high precision. With generalized GNN models, users can test diverse sets of inputs, including those that could potentially cause service disruptions, which may be impossible to test in a network with zero fault tolerance [109]. These scenarios could involve increasing traffic loads, simulated link failures, deployment of new Base stations (BSs), or network upgrades. In summary, low-cost trials and simulations benefit from accurate modeling provided by digital twins equipped with data-driven methodologies. The integration of GNNs in NDTs enables operators to experiment with different network configurations and scenarios, predicting network behaviors and assessing the impact of various inputs. This capability allows for cost-effective testing of scenarios that may involve service disruptions, helping operators make informed decisions for network optimization and improvement.
## 2 Materials and Methods  
Two independent datasets were used to validate the developed model. The experiments in the study of [PERSON] et al. [24] were conducted using a spiral-wound TFC-ULP 2514 membrane from Fluid Systems Corporation (now Koch Membrane Systems), San Diego, CA, USA. The experiments were carried out in a full recycle mode, where the concentrate and permeate were recycled back to the feed tank. The transmembrane pressure was 15 bar, with a flow rate of 475 L/h (corresponding to feed velocity of 0.72 m/s) and a temperature of 28-30 \({}^{\circ}\)C. The feed solution contained Ca2\({}^{+}\) (27-36 mM), Mg2\({}^{+}\) (12-16 mM), SO4\({}^{2-}\) (12-16 mM), and Cl\({}^{-}\) (55-73 mM), with CaO\({}_{4}\) supersaturation ratios (S\({}_{\text{a}}\)) ranging from 1.6 to 2.0 (corresponding to 27-36 mM of CaO\({}_{4}\), both without and with a dosage of 6 mg/L of a phosphonic acid sodium salt scale-inhibitor). The induction time was determined through measurements of the permeate flow rate, frictional pressure drop along the membrane, feed turbidity, and Ca2\({}^{+}\) and SO4\({}^{2-}\) concentrations. The authors reported that the four different criteria provided similar indications of the onset of scaling. The linear correlation between the induction time and saturation level on the membrane wall, shown in Figure 7 of [24], was used to validate the model.  
In the study of [PERSON] et al. [25], two different membrane systems were used: a tubular system and a spiral-wound system. In the former, two 30 cm-long tubular AFC99 reverse osmosis membranes (CPI membranes, Wilton, UK) with a diameter of 12.5 mm were connected by a U-tube. Experiments were conducted in a once-through mode, in which both the permeate and the concentrate were withdrawn from the system. The saturation index of CaSO\({}_{4}\) was 3.3 (corresponding to 49 mM of CaSO\({}_{4}\)). Flow rates of 0.4-7.0 L/min (equivalent to feed velocities of 0.16-2.64 m/s) were applied, at a pressure of 30 bar and a temperature of 22-24 \({}^{\circ}\)C. The spiral-wound module was equipped with a Dow Filmtec SW30-2521 element (membrane area of 1.19 m\({}^{2}\)). The experiments were conducted in a full recycle mode at a transmembrane pressure of 30 bar, with a flow rate ranging from 246 to 648 L/h (equivalent to feed velocities of 0.07-0.20 m/s) and a temperature of 20-22 \({}^{\circ}\)C. The feed solution contained Ca2\({}^{+}\), Na\({}^{+}\), SO4\({}^{2-}\), and Cl\({}^{-}\) with a saturation index of 2.54 with respect to gypsum (equivalent to 40 mM of CaSO\({}_{4}\)). The experiments were carried out both with and without a dosage of 2 mg/L phosphinocarboxylic acid AS. In this study, the induction time was determined by identifying the onset of the membrane permeability decline. The results of the induction time as a function of the Re number for both the tubular and spiral-wound systems were used to validate the model. These results are presented in Figure 6 of [25].
# The Dependence of Ultrasonic Velocity in Ultra-Low Expansion Glass on Temperature
## 2 Principle and Method of Ultrasonic Velocity Measurement
### Correlation Method for Measuring the TOF  
#### 2.2.1 The Correlation Calculation Principle  
As the B\({}_{1}\) and B\({}_{2}\) signals of the ULE samples were readily available and were highly similar, the correlation method was used to determine the time interval between B\({}_{1}\) and B\({}_{2}\) as the ultrasonic TOF in the samples. In detail, the ultrasonic signal that was sampled and saved in the PC is designated as \(x(t)\). By separating the B\({}_{1}\) and B\({}_{2}\) signals and noting them as \(x_{1}(t)\) and \(x_{2}(t)\), respectively, the correlation coefficient (\(R\)) can be represented as  
\[R=\frac{\int_{-\infty}^{+\infty}x_{1}(t)x_{2}(t)\mathrm{d}t}{\sqrt{\left[\int_ {-\infty}^{+\infty}x_{1}^{2}(t)\mathrm{d}t\int_{-\infty}^{+\infty}x_{2}^{2}(t )\mathrm{d}t\right]}}(|R|\leq 1). \tag{3}\]  
The ultrasonic signals that were collected by the data acquisition card and the PC, on the other hand, were two discrete signal arrays. As a result, for the discrete signals, the normalized \(R\) can be given as  
\[R=\frac{\sum x_{1}(i)x_{2}(i)-\sum x_{1}(i)\sum x_{2}(i)/n}{\sqrt{\left(\left[ \sum x_{1}^{2}(i)-\left(\sum x_{1}(i)\right)^{2}/n\right]\left[\sum x_{2}^{2}( i)-\left(\sum x_{2}(i)\right)^{2}/n\right]\right)}},i=1,2,\ldots,n. \tag{4}\]  
where \(n\) is the computed length of the signal array, and \(i\) is the location inside the signal array.  
In the time domain of signal \(x(t)\), there is a time interval between the signals \(x_{1}(t)\) and \(x_{2}(t)\). The time interval between \(x_{1}(t)\) and \(x_{2}(t)\) is the round trip time of the ultrasonic wave propagated in the thickness direction, i.e., the TOF. Equation (4) is used to generate the correlation array, with the position having the largest correlation coefficient corresponding to the temporal position of \(m\). The corresponding TOF (\(\Delta t\)) is equal to \(m/f_{\rm S}\) if the sampling frequency is noted as \(f_{\rm S}\). Finally, the following equation can be used to calculate the ultrasonic velocity:  
\[c_{\rm L}=\frac{2d\cdot f_{\rm S}}{m}. \tag{5}\]  
Figure 1: Schematic diagram of the immersion pulse reflection method: (**a**) the transmission process of ultrasonic waves; (**b**) the transmission time of ultrasonic waves.
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 6 Application Cases Overview
The construction of shafts in highly permeable sand layers underlain by fractured rock presented considerable challenges. In Asia, VSM was first employed for this project to address these challenges and demonstrates several advantages over caisson shaft construction [30]. The world's largest-diameter vertical shaft was successfully excavated using a \"Dream\" boring machine with an excavation diameter of 23.02 m and a depth of 53 m in the underground smart parking garage in the Jing'an District of Shanghai [99]. VSM was utilized for the first time in the construction project of a parking garage in the Jianye District of Nanjing City, successfully completed in heterogeneous ground conditions of sand, gravel, and limestone with an outer diameter of 12.8 m and a depth of 68 m. The results indicate that this  
Figure 10: Illustration of shaft construction project at St. Petersburg after [98]. method achieves minimal construction footprint and negligible effects on the surrounding environment, with construction efficiency exceeding that of alternative methods [100]. In the Pudong New Area of Shanghai, 17 shafts of the Zhuyuan Bailonggang Sewage Connecting Pipe Project were constructed using VSM technology. The internal diameter and excavation depth of the shafts were 6 m and 43.22 m, respectively, with an average excavation rate of 1.9 m per day. The soft ground conditions, including silty clay, clayey silt, and clay, resulted in maximum settlement of approximately 15.2 mm and maximum horizontal displacement of about 3.74 mm at a depth of 40 m. Compared to other construction methods, the disturbance to the surrounding soil was minimal [29]. Successful shaft excavation projects worldwide utilizing VSM technology are summarized in Table 5.  
The newly developed PIAUS construction technology (ACPP) has successfully completed its first project in constructing the escape shaft at the Yunle Road Station, part of the west extension of Shanghai Metro Line 13. The verticality of the sinking well achieved a precision of 0.65%, with a maximum ground settlement of 4.95 mm and a corner height difference of only 2 mm, illustrating the capability of this advanced technology. Furthermore, the segment lining exhibited no leakage or misalignment, indicating exceptional overall performance. PIAUS construction technology, VSM, and ACPP have emerged as highly efficient solutions for constructing shafts in UUS, offering reduced construction time, cost savings, enhanced safety, and improved quality control. Off-site prefabrication minimizes noise, vibration, and disruptions while reducing on-site hazards and accidents, making it ideal for UUS projects where stability, minimal ground disturbance, and long-term performance are critical. Numerous aforementioned global projects have demonstrated its advantages across various challenging environments. Figure 11: Construction of shaft by VSM in confined space between two rows of houses in (**a**) Girona, Spain and (**b**) Naples, Italy after [27].
# Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning
## 4 Experiments
### Experimental Results on the Generated Image Caption
In a similar vein, we conducted a thorough comparison between the performance scores of the baseline model, which lacks an attention mechanism, and our proposed model, employing diverse evaluation metrics. The results presented in Table 6 clearly depict the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Flickr30K**} & \multicolumn{4}{c}{**MSCOCO**} \\ \cline{2-9}  & **B-1** & **B-2** & **B-3** & **B-4** & **B-1** & **B-2** & **B-3** & **B-4** \\ \hline Deep VS [41] & 57.3 & 36.9 & 24.0 & 15.7 & 62.5 & 45.0 & 32.1 & 23.0 \\ m-RNN(AlexNet) [40] & 54.0 & 36.0 & 23.0 & 15.0 & - & - & - & - \\ m-RNN(VGGNet) [40] & 60.0 & 41.0 & 28.0 & 19.0 & 67.0 & 49.0 & 35.0 & 25.0 \\ Hard-attention [26] & **66.9** & 43.9 & 29.6 & 19.9 & **71.8** & 50.4 & 35.7 & 25.0 \\ Bi-LSTM [9] & 62.1 & 42.6 & 28.1 & 19.3 & 67.2 & 49.2 & 35.2 & 24.4 \\ Deep Bi-LSTM [9] & 63.1 & 44.2 & 29.7 & 20.0 & 67.5 & 49.0 & 35.5 & 24.9 \\ Our model (Bi-LSTM & 64.5 & **44.6** & **29.8** & **20.2** & 68.8 & **51.0** & **35.9** & **25.2** \\ and attention mechanism) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Compare the BLEU score of each model on Flickr30K and MSCOCO. Figure 5: (**a**) Comparison of METEOR scores of three models on two benchmark datasets; (**b**) comparison of CIDEr scores of three models on two benchmark datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **METEOR** & **CIDEr** \\ \hline [PERSON]’s model [29] & 16.3 & 39.0 \\ [PERSON]’s model (SNOC) [31] & **21.9** & 39.5 \\ Our model (Bi-LSTM) & 17.8 & 34.5 \\ Our model (Bi-LS-AttM) & 21.5 & **41.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the METEOR and CIDEr scores between our and the state-of-the-art models. relative performance of the baseline model in contrast to our model. Remarkably, our model consistently surpasses the baseline model in terms of performance metrics on both the MSCOCO and Flickr30k test sets. Furthermore, the line graph depicted in Figure 6 visually demonstrates the competitive advantage of our model across various evaluation metrics. It is noteworthy that our model exhibits a more pronounced competitive edge, particularly on the MSCOCO test set. We attribute this observation to the larger scale of the MSCOCO test set, enabling a more comprehensive evaluation and, subsequently, yielding higher performance scores.