esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mechanism** & **Description** & **References** \\ \hline Glyclysis and Krebs Cycle Remodeling & Reconfiguration of central carbon metabolism to & [12; 13; 14] \\  & redirect energy flow and optimize survival & \\ \hline Glyoxylate Shunt Activation & Diversion of carbon flux through secondary pathways & [5; 7; 23] \\  & to reduce ROS generation & \\ \hline Lipid and Amino Acid Metabolism Shifts & Reprogramming of biosynthetic routes for adaptive & [12; 13; 14] \\  & stress responses & \\ \hline Modulation of Membrane Composition & Alteration in lipid profile to affect & \\  & antibiotic permeability & [25; 26] \\ \hline Suppression of Respiration and ROS & Downregulation of oxidative metabolism to limit & \\  & bactericidal ROS activity & [6; 27] \\ \hline Growth Arrest and Metabolic Slowdown & Temporal dormancy to outlast antibiotic action & [22; 28] \\ \hline \hline \end{tabular}  
ROS: reactive oxygen species.  
\end{table} 
Table 1: Mechanistic basis of metabolic rewiring in response to antibiotic stress. Figure 2: Mechanistic overview of how metabolic rewiring enables bacteria to circumvent antibiotic action. Key adaptive strategies include (1) the modulation of antibiotic uptake via changes in membrane composition or electrochemical gradients; (2) the reduction of intracellular reactive oxygen species (ROS) production by suppressing oxidative metabolism; and (3) the reprogramming of cell growth and division to delay or escape the temporal window of antibiotic efficacy. These processes contribute to the establishment of a transient, drug-tolerant phenotype, facilitating bacterial persistence under antimicrobial stress. Image created using BioRender (accessed via web application, version as of May 2025), GraphPad Prism (version 10.1.2-2024), and PowerPoint (Microsoft 365, version 2404). It is also important to emphasize that metabolic rewiring is not merely a passive response: in many bacterial species, environmental signals (nutrients, stress molecules, low-dose antibiotics) can activate bona fide regulatory circuits that actively promote the transition to alternative metabolic states, via redox transducers, quorum sensing systems, and global regulators such as Crp, SoxR, and ArcA [29; 30]. Finally, from an evolutionary perspective, an intriguing synergy emerges between metabolic plasticity and genetic selection: bacteria that survive through metabolic reprogramming may provide the phenotypic substrate within which stable mutations are subsequently selected, thus positioning metabolic rewiring as a potential precursor to genuine genetic resistance [31; 32]. A comparative overview of the key features distinguishing metabolic rewiring from classical genetic resistance is provided in Table 2.
# Preamble Design and Collision Resolution in a Massive Access IoT System
## 3 Preamble Design
### Conventional RA Procedure  
The RA procedure includes two manners, i.e., contention-based manner and non-contention-based RA manner [21]. Since preamble collisions only occur in contention-based manner, this paper mainly focuses on contention-based manner. As depicted in Figure 1, a conventional RA procedure consists of four steps [22].  
* Step-1: A device randomly selects a preamble from the available preamble sequence set, and then transmits the selected preamble on the available PRACH slot. If different devices transmit identical preambles on the same PRACH resources, RA contention will occur.  
Figure 1: Conventional RA procedure.  
Step-2: The gNB sends Random Access Response (RAR) message to the device after it detects a received preamble and captures the corresponding TA value. The RAR message contains the TA command, an uplink resource grant for MSG3 and a temporary cell-radio network temporary identifier (C-RNTI) for the device.
* Step-3: After receiving the RAR message, the device sends MSG3 to the gNB. MSG3 includes Radio Resource Control (RRC) connection request, scheduling request, and temporary C-RNTI. If preamble collision occurs, the gNB will be unable to decode the MSG3 data correctly.
* Step-4: If the gNB decodes the MSG3 successfully, it sends back the contention resolution message including RNTI acquired from the decoded MSG data. If a device receives its own RNTI within corresponding time window, RA procedure is accomplished successfully. Otherwise, the device restarts a new RA procedure.  
Please note that in step-2, the gNB can only detect a specific preamble, but it cannot judge whether the detected preamble is sent by a single device or multiple devices. In addition, the preamble collisions cannot be detected before step-3. Once the collision occurs, it leads to a long RA delay and a waste of PUSCH resource.
## 4 Results and Discussions
### Shear Surface and Grain Crushing before and after Testing  
In landslide movements, the larger the particle is, the greater the shear resistance [30]. This means that strong interactions between soil particles are unavoidable during mass movement. In the post-failure stage, spreading and high mobilization of debris flow transformed from the initial slope failure may be influenced by mixing with broken landslide sediments of pre-existing deposition in the flow channel. It should be noted that the landslide materials on natural slopes have various grain sizes. They typically consist of very fine particles, sand, and gravel (pebbles and cobbles) to boulder sizes. Landslide mobilization, e.g., runout distance and velocity, is also strongly affected by the grain size  
Figure 4: Shear stress and displacement as a function of rotation at 10, 30, 50, 100, and 110 mm. Shear characteristics and determination of residual shear stress with shear displacement for (**a**) drained and (**b**) undrained conditions.  
distribution. One of the greatest advantages of this experiment is that the shear surface in the soil sample can be actually observed.  
Figure 5 shows the shear surface that is observed for a given shear displacement of 10, 30, 50, and 100 mm. It is sufficiently long to observe the shear surface even though part of the sample was damaged in the process of removing the upper ring (Figure 5f,h). In general, the longer the shear displacement, the more fractural and fragmentation phenomena appeared. In Figure 5, the difference in the grain size distribution indicates the particle crushing effect [32; 33]. However, it is difficult to examine the degree of crushing effect in laboratory experiments. For a given shear displacement, the difference in grain size distribution is very small, regardless of drainage condition, because the soil samples are typical weathered soils subjected to limited shearing time. Interestingly, the area of the shear surface is clearly visible and observed for all tests. The sample was divided into three parts, i.e., upper, middle, and lower parts, to perform the grain size distribution experiments. Figure 6 shows the results of grain size distribution by sieving analysis. As observed by [PERSON] et al. [27], the grain crushing effect is significant for sandy and gravelly soils subjected to high shearing speeds (i.e., maximum 1000-3000 mm/s) in the confined container. Compared with this study, it differs by one order of magnitude. The main difference in the rotational distance (i.e., shear displacement) applied to the test. According to previous research performed by [PERSON] and colleagues [26; 27], the shear displacement can reach approximately 3000 cm. In this study, the rotation was limited to 100 mm owing to the prevention of water leakage and destruction of the rubber edge in the ring shear box. This can lead to a large difference in the results. There are limitations in this study to overcome in the future.
# Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model
## 2 Mathematical Model of the Rolling Element Bearing  
### Description of the 2-DOF Mathematical Model of the Rolling Element Bearing  
In Figure 1, the ball bearing is presented as the nonlinear spring-damper oscillator with the rotating shaft-inner ring system driven by a constant angular velocity \(\omega_{s}\) and rigid outer ring. The 2-degrees of freedom (DOF) mathematical model of rolling-element bearing represents the basic operation of the deep groove ball bearings (DGBB) in the x-y plane. In the model, the inner ring and the shaft are treated as one rotating mass and the outer ring is fixed in the housing. The derived mathematical model takes into account external loads that are subjected to the outer ring, the eccentricity of the shaft caused by improper shaft's coupling or seating, shape errors on the rolling surfaces of the inner and outer ring, variable deformations related to Hertzian contact theory, while the gyroscopic effect is neglected.  
In the model, interactions related to the friction torque are neglected, so there are no fluctuations in the angular velocity. The deformation of each rolling element-raceway is taken into account in form of a nonlinear spring. In the DGBB, rolling elements are distributed equally around its circumference with the constant angle \(\psi_{i}\), so the angular position of \(i\)-th rolling element is calculated from the vertical axis according to the following formula:  
\[\psi_{i}=\psi_{0}+\int_{\rm o}^{t}\omega_{c}(t)dt, \tag{1}\]  
\[\psi_{0}=\frac{2\pi(i-1)}{n}, \tag{2}\]  
where, \(\psi_{0}\) is the angular position of the first ball, \(i\) is the angular position of the ball (\(i=0\), \(1\),..., \(n-1\)), \(n\) is the number of rolling elements, \(\omega_{c}\) is the rotational velocity of the cage.  
The value of the rotational velocity of the cage \(\omega_{c}\) is determined by the internal geometry of the ball bearing and subjected velocity of the shaft. It is worth emphasizing, that the velocity of the cage is the same as the velocity of each rolling element assuming no slippage.  
\[\omega_{c}=\ \frac{\omega_{s}}{2}\left(1-\frac{D}{d_{p}}\right), \tag{3}\]  
where, \(D\) is the ball diameter, \(d_{p}\) is the pitch diameter.  
The dimensionless model gives sensitive information on the influence of applied terms on each other [47; 48], then the system is scaled and provides the information on the existing dependencies between dimensionless terms and their impact on the system's dynamics. Inthe simulation of the derived model, as the global variable, the value of the radial internal clearance is assumed. The mentioned term was taken intentionally, as it has a significant impact on the dynamic response and is related to the variable contact in a ball bearing. In the practical application, the RIC strongly affects the tribology features of ball bearings. In the following subsections, applied nonlinear effects in the mathematical model are discussed.
## 2 Results and Discussion
### Materials Characteristics
#### 2.1.3 SAXS Investigation
Figure 4a shows the volume-weighted particle (or pore) size distribution Dv(R) from the scattering curve of an ensemble of spherical particles (or pores) with homogeneous inner electron density distribution. Here, the size of the scattering objects can be determined. In this case, all nanometer objects are very similar (they have similar sizes). The difference concerns the relative number of these objects, which is reflected in the intensity of the maxima of Dv(R) curves. For the DVB:TEVS = 1:2 sample (Figure 4a) the size of most inhomogeneities is estimated to 40 nm, however, a significant amount of these objects is in the range from 20 nm to 100 nm. The size of scattering objects was significantly lower for DVB:TEVS = 1:1 and DVB:TEVS = 2:1 samples. Here the Dv(R) function points to the significant number of objects with dimensions of 35 nm and 20-30 nm for DVB:TEVS = 1:1 and DVB:TEVS = 2:1, respectively. Moreover the radius of gyration as the mean square distance from the center of their distribution provides a measure of the overall size of the scattering objects. Here, the DVB:TEVS = 1:2 sample exhibit some cylinder geometry as cross-sectional dimension of the objects in comparison to typical spherical scatters. For DVB:TEVS = 1:1 and DVB:TEVS = 2:1 samples the fit of the PDDF function (pair distance distribution function) was satisfactory only for spherical systems (additional calculations were performed but not presented here). The roughness of the surface was evaluated by investigation of the Porod exponent (Figure 4c). The slope of a log-log plot of intensity vs. q vector shows the fractal dimension of the scattering object. At high q values, the q\({}^{-3}\) function illustrates the smooth interfaces. The log-log plots of investigated samples shows the linear range of Porod range. The Porod exponent for scattering from materials is between 2 and 3 for a surface fractal in three-dimensional space. The obtained results suggest the smooth surface for DVB:TEVS = 2:1 microspheres.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 3 HAWK Optimization Through Profiling
### Optimized Implementation of Critical Operations
#### 3.2.2 Optimized Implementation of Add _mul_small() Function  
In this paper, Algorithm 6 was implemented with fewer instructions than before by using the'madd' instruction, which can perform multiplication and addition operations with one instruction. The implementation corresponds to line 7 of Algorithm 6. In line 7, to calculate the z value, one multiplication and two addition operations are performed using values such as yw, s, xw and cc. [Implementation 3] is the assembly code for calculating the z value. In the code before optimization, you can see that the process of calculating'mul' and 'add' is combined into one'madd' instruction. The detailed description is available in Table 10.
```
0:uint32_t*restrictx,size_tlen,size_txstride,constuint32_t*restricty,uint32_t
0:Updatedxarray.
1:uint32_tcc=0;
2:forsize_tu=0;u<len;u++do
3:uint32_txw,yw;
4:uint64_tz;
5:xw=*x;
6:yw=y[u];
7:z=(uint64_t)yw*(uint64_t)s+(uint64_t)xw+(uint64_t)cc;
8:*x=(uint32_t)z&0x7 FFFF;
9:cc=(uint32_t)(z>31);
10:x+=xstride;
11:endfor
12:*x=cc;
```  
**Algorithm 6** Void zint_add_mul_small.  
Figure 4: The w calculation process using the ‘csel’ instruction: This version of the computation uses the ‘csel’ instruction, which performs a conditional selection based on the comparison of inputs. The process is more efficient compared to the version without ‘csel’.  
Figure 3: The w calculation process without using the ‘csel’ instruction: This diagram shows the computation of the value \(w\) without using the conditional select instruction. Standard arithmetic operations, such as subtraction, logical and addition, are used to determine the result.  
This process is also illustrated in Figures 5 and 6. As seen in Figure 5, when the'madd' instruction is not used, the instruction (represented by the yellow box) must be used four times. In contrast, when the'madd' instruction is used, it can be confirmed that only three instructions are needed, ultimately achieving the same result with fewer instructions by omitting one.  
By utilizing the sbfx instruction, bit manipulation operations that would have required multiple instructions can be handled with a single instruction. This reduces clock cycles and improves performance, while also reducing memory access by eliminating the need for separate load/store operations. Additionally, the csel instruction allows conditional selection without branching, thereby preventing performance degradation caused by branch misprediction. These instructions optimize execution at the instruction level, minimizing computational overhead and enhancing overall processing performance.
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 2 Related Work
### Tracking-by-Detection  
In the TBD paradigm, a class of widely adopted MOT frameworks [1, 2, 3] employs a two-stage pipeline: objects of interest are first detected, and then cross-frame associations are established using complementary cues such as appearance and motion features. SORT [2] exemplifies this paradigm by leveraging the Kalman Filter for motion state prediction and the Hungarian algorithm for association. To address occlusion challenges, DeepSORT [3] enhances the association strategy by incorporating appearance features extracted from deep convolutional neural networks into the cost matrix. Building on this foundation, [PERSON] et al. [1] introduced the StrongSORT framework, which refines detection, embedding, and association modules through lightweight, plug-and-play components. [PERSON] et al. [6] proposed a General Recurrent Tracking Unit that captures long-term temporal dependencies to score track proposals, mitigating detection. [PERSON] et al. [7] advanced monocular tracking via a detection multiplexing strategy, introducing a multiplex labeling graph model with independent nodes.  
Despite their advancements, most of these methods predominantly rely on bipartite graph matching for data association, which often results in the inadequate modeling of intricate situations.
## 4 Experiments
### Main Results
#### 4.4.2 Results on OPUS-100  
We summarize the experimental results of the OPUS-100 dataset in Table 4 and observe consistent performance gains with the IWSLT-8 experiments. Our strong MNMT model and bilingual pivot translation surpass previous baselines [21] in zero-shot translations (0.84 BLEU, (1) \(\rightarrow\) (4)). We also present pivot translation based on the multilingual model, which is slightly worse than the bilingual pivot system (0.37 BLEU, (6) \(\rightarrow\) (4)). For zero-shot  
\begin{table}
\begin{tabular}{c l l l l l l l} \hline \hline  & & & \multicolumn{2}{c}{**Supervised**} & \multicolumn{3}{c}{**Zero-Shot**} \\ \hline
**ID** & **Method** & **BLEU** & **METOER** & **TER** & **BLEU** & **METEOR** & **TER** \\ \hline (1) & MNMT [42] & 25.80 & - & - & - & - & - \\ \hline (2) & Bilingual and Pivot & 27.36 & 50.20 & 55.22\% & 9.12 & 29.52 & 83.40\% \\ (3) & MNMT & 27.13 & 49.07 & 55.77\% & 2.50 & 10.70 & 95.20\% \\ (4) & 3 + Pivot & - & - & - & 8.97 & 25.85 & 85.00\% \\ \hline (5) & mRASP2 [27] & 26.93 & - & - & 8.44 & 24.33 & 87.05\% \\ (6) & 3 + ROBT [21] & 26.35 & - & - & 7.83 & 20.97 & 89.27\% \\ (7) & 3 + POBT & 26.47 & 47.55 & 57.33\% & 8.55 & 25.02 & 86.99\% \\ (8) & 3 + SOBT & 27.25 & 49.88 & 55.35\% & 11.27 & 33.50 & 75.42\% \\ (9) & 3 + SOBT + CLOS & **27.74** & **51.05** & **54.20\%** & **12.89** & **36.85** & **70.44\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance on the IWSLT-8 dataset. “Supervised” means English-centric language directions seen during training. “Zero-shot” means all other 56 non-English directions unseen during training. “Bilingual and Pivot” means separately trained bilingual models for supervised directions and pivot translation based on bilingual models for zero-shot directions. The best results, indicated in bold, are statistically significant against all other results, for \(p<0.05\).  
translation, mRASP2 and ROBT provide substantial performance gains (9.35 BLEU, (5) \(\rightarrow\) (7) and 7.51 BLEU, (5) \(\rightarrow\) (8)), but still lag behind POBT (0.30 BLEU, (7) \(\rightarrow\) (9) and 2.14 BLEU, (8) \(\rightarrow\) (9)). For zero-shot directions, our data augmentation algorithm significantly outperforms mRASP2 and ROBT (3.36 BLEU, (7) \(\rightarrow\) (11) and 5.20 BLEU, (8) \(\rightarrow\) (11)), even surpassing strong pivot translation (1.98 BLEU, (4) \(\rightarrow\) (11)). For supervised directions, our algorithm also achieves the best performance surpassing bilingual NMT systems on average, while mRASP2 and ROBT achieve BLEU score improvements on zero-shot translations at the sacrifice of BLEU score loss on supervised directions. We also observe consistent performance improvements with the METOER and TER metrics in Table 4.
## 5 Error Analyses of the New Method
### The Effect of the Measurement Error of Sensors  
Compared with the existing method described in Section 3.1 and Figure 2, the result of each reconstruction point in the new method needs to use more measurement data when calculating the differential values, which means that the measurement result and the reconstruction result are more affected by the measurement error. However, considering the advantage of the new method in eliminating the data processing error, it is needed to analyze the extent to which the results are affected by the measurement error. According to the calculating process of \(mf\) in Equations (5) and (6), the combined standard uncertainty of \(mf(x_{k})\) can be expressed as:  
\[u_{m}^{2}=\frac{4}{s^{2}}u_{r}^{2}, \tag{21}\]  
where \(u_{r}\) is the standard uncertainty of the probe output due to the measurement error. To get the reconstruction results, the integral process in Equations (7) and (8) can be written as:  
\[Y_{f}(x_{k})=\left\{\begin{array}{cc}m_{1}(x_{0})&k=0\\ Y_{f}(x_{0})+\sum\limits_{i=0}^{k-1}mf(x_{i})\cdot s&k=1,2,\cdots,\text{N}\\ \end{array}\right.. \tag{22}\]Therefore, the combined standard uncertainty of \((k+1)th\) reconstruction point, which is defined as \(u_{new}(k)\), can be calculated by:  
\[u_{new}{}^{2}(k)=k\cdot s^{2}\cdot u_{m}^{2} \tag{23}\] \[=4 ku_{r}^{2}\]  
Similarly, the combined standard uncertainty of \((k+1)th\) reconstruction point, which is obtained from the method described in Figure 2 and Equations (12)-(14), can be expressed as:  
\[u^{2}(k)=2k\cdot\frac{s^{2}}{d^{2}}\cdot u_{r}^{2}. \tag{24}\]  
It can be seen that, when the number of the measured points increases, the influence of the sensor measurement error on the reconstruction result will increase. When \(k\) takes the same values, compared with the reconstruction profiles shown in Figure 8, whose reconstruction errors caused by data processing error are on the same magnitude as stepwise variations, the new method has better performance, as \(u_{new}(k)\) and \(u(k)\) are on the same magnitude.