esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition, even if the known point has the initial precision at the decimeter level, the ratio of squared residuals of the best integer vector to the second-best vector is remarkably uncertain. The realization of initialization without high-accuracy known points has always been a vital research point in pseudolite positioning. To solve these problems, a new method is proposed in this paper. This method cannot only eliminate the limitation of KPI, but can also obtain the initial value satisfied with the high accuracy requirement of LAMBDA, so as to achieve high-precision indoor pseudolite positioning. Focusing on a relatively cheap commercial pseudolite system, the double difference (DD) observation equation is utilized to eliminate or weaken the clock difference, phase center deviation,and so on. Firstly, the approximate coordinates of rover station can be obtained based on the differential pseudolite pseudorange method (DPL), and the Gauss-Newton iteration is used to improve the precision. Then, a search space of the AFM is established in the corresponding epoch and the coordinate of the highest AFV can be obtained. Moreover, the best step length and search scope are explored, and the wrong peaks of the AFM are eliminated by some constraint strategies. At present, the corresponding coordinates of the real peak can reach the centimeter level, which can meet the initial accuracy requirement of LAMBDA. Finally, the LAMBDA method is applied to fix the ambiguities of the DD carrier phase, and the iterative EKF is employed for parameter estimation. Thus, the high-precision positioning of indoor pseudolite is achieved.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 3 Association Rule Mining Algorithm
### Indices of Association Rule  
Taking item set \(FA_{\alpha}=\left\{B_{b}\), \(T_{t}\), \(F_{f}\), \(M_{m}\right\}\) as an example (in which \(B_{b}\subseteq B\), \(T_{t}\subseteq T\), \(F_{f}\subseteq F\), \(M_{m}\subseteq M\)), the association rule can be expressed as \(F_{f}\Rightarrow T_{t}\). This means if factor \(F_{f}\) happened, there is a possibility that fault \(T_{t}\) would also happen. Two indices used to evaluate the association rule are as follows:
## 2 Experimental Part  
The experimental setup in this work is the same as in [4]. For the reader's convenience, we describe some items in more detail here:  
A flash-lamp-pumped electro-optically Q-switched Nd: YAG laser produced pulses of 532 nm radiation with a variable energy E\({}_{532}\) at 1 Hz for Raman laser pumping. The pulse duration t\({}_{\text{P}}\) was 10.6 ns at E\({}_{532}\) = 80 mJ and was reduced to 7.5 ns at E\({}_{532}\) = 450 mJ. The maximum E\({}_{532}\) was 500 mJ. The spectral width of the 532 nm radiation was less than 0.1 nm. Rectangular pump spots with a linear size of a\({}_{1}\)\(\rightarrow\)4 mm and a\({}_{2}\)\(\rightarrow\)2 mm, with an area of A\({}_{1}\) = 0.18 and A\({}_{2}\) = 0.045 cm\({}^{2}\), were formed inside a 7 \(\times\) 7 \(\times\) 50 mm, \"b\"-axis-cut KGW crystal, using a combination of a microlens array (MLA) homogenizer and a condenser lens.  
Figure 1 shows the fluence profile of the pumping beam in the focal plane of the condenser lens. One can observe almost a flat top profile without hot spots. The variations in the pump fluence over the pump spot area here do not exceed \(\pm\)11%.  
Figure 1: The fluence profile of the 532 nm pumping spot at KGW crystal input.  
The external cavity of the Raman laser was formed by two mirrors: pump and output mirrors. The pump mirrors had a high (>94%) transmission at 532 nm and a high reflection (>99%) within 550-650 nm. For selective cascade generation of the second Stokes order, the output mirrors had a high reflection at 532 nm and 559 nm to create a high Q-factor resonator for the first Stokes order, and a high transmission (90-99%) at 589 nm. The mirrors were installed near the KGW crystal's ends, so the cavity geometrical length was 52-54 mm. In all cavity configurations, we only used flat output mirrors. Plano-concave pump mirrors with radii of curvature of 2000 mm, 1000 mm, and 500 mm were used in stable spherical cavities. Plano-convex pump mirrors (2000 mm and 750 mm) were used in unstable cavities.  
The Raman laser characteristics were measured at three levels of pump intensity for pump spots with the above spot size using: I\({}_{532}\) = E\({}_{532}\)/(A \(\times\) tr) = 120, 220, and 350 MW/cm\({}^{2}\).  
The laser energy was measured using a J-50-YAG pyroelectric energy sensor from Coherent. Beam intensity profiles were recorded using a CMOS-1.001-Nano camera and software from Cynogy Technologies (Duderstadt, Germany). The wavelengths of the Raman laser output radiation were monitored via a flame spectrometer from Ocean Insight.
## 3 Non-Thermal Technologies in Food Processing
### Ozonation
#### 3.3.1 Application of Ozone in Fruits and Vegetables
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Food Category** & **Product** & **Ozone Form and Concentration** & **Exposure Time** & **Effect** & **Reference** \\ \hline \multirow{6}{*}{Fruits and Vegetables} & Fresh-cut lettuce & Oxonated water, 2 mg L\({}^{-1}\) & 5 min & 2.57 log reductions against 5. _Tphimurium_ & \multirow{6}{*}{[165]} \\  & Fresh-cut lettuce & Oxonated water, 2 mg L\({}^{-1}\) & 5 min & 3.71 log reduction against 5. _coli_ color properties and sensory quality without & \\ \cline{3-6}  & & & 15 min & 3.47 log reductions against _E. coli_ color properties and sensory quality without & \\ \cline{3-6}  & Spinach & Gaseous ozone, 1 ppm & 10 min & 1 log reduction in _E. coli_ and _Listeria_ spp. & [166] \\ \cline{2-6}  & Spinach & Combination of ozone 3.33 g min\({}^{-1}\) & 10 min & 1.46 log reduction in _E. coli_ & [167] \\  & Spinach & and ultrasound 40 kHz & & _E. coli_, _Salmonella_, and _Listeria_ & \\ \cline{2-6}  & Fresh-cut green bell peppers & Aqueous ozone, 24 mg L\({}^{-1}\) & 5 min & 3.71 log reduction in total plate count & [168] \\ \cline{2-6}  & Strawberry & Aqueous ozone, 0.1 ppm & 2 min & Retention the fruit quality and extension the storage life & [169] \\ \cline{2-6}  & Strawberry & Aqueous ozone, 3.5 mg L\({}^{-1}\) & 5 min & Reduced decay caused by _B. micron_ almost 17\% & [170] \\ \cline{2-6}  & Fresh-cut cabbage & Aqueous ozone, 1.4 mg L\({}^{-1}\) & 5 min & Significantly inhibited aerobic bacteria, coliforms, and yeasts, reduced ethylene production & \\ \cline{2-6}  & Fresh-cut oimos & Aqueous ozone, 1.4 mg L\({}^{-1}\) & 5 min & Significantly inhibited aerobic bacteria, coliforms, and yeasts, reduced respiration rate, reduced residual & \\ \cline{3-6}  & & & & & \\  & Fresh-cut oimos & Aqueous ozone, 1.4 mg L\({}^{-1}\) & 5 min & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{6}{*}{Fish Products} & White shrimp & Oxonated water, 1 ppm & 10 min & Increased shelf life (up to 24 days), maintained acceptable sensorial attributes & [173] \\ \cline{2-6}  & Sea bream & Aqueous ozone, 640 ppm & 15 min & 0.29 log reductions in _Enterobacterricione_ & [174] \\ \cline{2-6}  & Oyster & 0.6 mg/L/ & 6 h & 1.3 log reductions in _E. coli_ & [175] \\ \cline{1-1} \cline{2-6}  & Mathrooms & _Agurizas bisprons_ & Cascous ozone, 20 mg/L & 30 and 60 min & Increased firmness & [176] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recent studies of applications of ozone in different food categories.
# STHarDNet: Swin Transformer with HarDNet for MRI Segmentation
## 4 Proposed Method: STHarDNet
### Swin Transformer Block  
Figure 5 shows the structure of the Swin Transformer model used in this study. The input image passes through the patch partition layer and is segmented into patches with a 4 \(\times\) 4 size to generate patch tokens having a shape of (W/4, H/4, 4 \(\times\) 4 \(\times\) channel). The generated patch tokens go through the linear embedding in stage 1. Afterward, they are input into two connected Swin transformer blocks to generate tokens of (W/4, H/4, C) where C refers to an arbitrary dimension. Stages 2 and 3 consist of patch merging and Swin transformer blocks, respectively. In patch merging, adjacent 2 \(\times\) 2 patches are merged into one patch, and the tokens are down-sampled to 1/2, whereas C is doubled. In stages 2 and 3, the shape of the tokens is (W/8, H/8, 2C) and (W/16, H/16, 4C), respectively. In the Swin Transformer's last \"Linear Projection\", the feature dimension is expanded to eight times the input dimension, and after going through a 1 \(\times\) 1 convolution, a feature map of the same shape is output. The Swin Transformer model receives a 112 \(\times\) 112 \(\times\) 48 feature map that was output from the first HarDNet block of the encoder, which is then connected to the last HarDNet block of the decoder. The window size of the Swin Transformer model used in this study is seven.
# Space-Efficient Fully Dynamic DFS
### Applications  
For static undirected graphs, _connectivity_, _biconnectivity_ and _2-edge-connectivity_ queries can be answered by using a DFS tree (details for these queries are in Section 8). The existing fully dynamic DFS algorithms [4; 5] can be applied to solve these queries in fully dynamic graphs including vertex updates. Our algorithms can also be extended to answer these queries under the fully dynamic setting including vertex updates. Though we need some additional considerations for our algorithms (Section 8), the worst-case update time complexity and the required space can be kept same as the dynamic DFS algorithms in Table 1 (Theorems 6 and 7). Moreover, as well as the existing fully dynamic DFS algorithms, our algorithms can solve these queries in worst-case \(O(1)\) time.  
For the _dynamic connectivity problem_ under vertex updates, the _dynamic subgraph connectivity problem_[13; 14] has been extensively studied. In this problem, given an undirected graph \(G\), a binary status is associated with each vertex in \(G\) and we can switch it between \"on\" and \"off\", and the query is to answer whether there is a path between two vertices in the subgraph of \(G\) induced by the \"on\"  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Worst-Case Update Time**} \\  & **Space (bits)** & **Fully Dynamic** & **Incremental** \\ \hline
[4] & \(O(m\log^{2}n)\) & \(O(\sqrt{mn}\log^{2.5}n)\) & \(O(n\log^{3}n)\) \\
[5; 7] & \(O(m\log^{2}n)\) & \(O(\sqrt{mn}\log^{1.5}n)\) & \(O(n)\) \\ A & \(O(m\log n)\) & \(O(\sqrt{mn}\log^{0.75+\epsilon}n)\) & \(O(n\sqrt{\log n})^{*}\) \\ B & \((3m+o(m))\log n\) & \(O(\sqrt{mn}\log^{1.25}n)\) & \(O(n\log n)\) \\
[11] & \(O(m\log n)\) & \(O(\sqrt{mn}\log^{0.5}n)\) & - \\ \hline \hline \end{tabular}  
* If \(m=O(n^{2}/\sqrt{\log n})\), this can be reduced to \(O(n\log^{n}n)\).  
\end{table}
Table 1: Comparison of required space and worst-case update time for dynamic DFS algorithms.  
vertices. Indeed, our dynamic setting including vertex insertions and deletions is a generalization of this dynamic subgraph setting. Under the dynamic subgraph setting, we cannot change the topology of \(G\), i.e., all edges and vertices in \(G\) are fixed, while under our setting we can. Under the generalized fully dynamic setting (i.e., our setting), we improve the deterministic worst-case update time bound of [4; 5] (with keeping query time \(O(\mathrm{polylog}(n))\)) by a \(\mathrm{polylog}(n)\) factor. We also compress the required space of their algorithms.  
For the _dynamic biconnectivity_ and _2-edge-connectivity_ problems, the setting including vertex updates was not well considered. Under the fully dynamic setting including vertex updates, we improve the deterministic worst-case update time bound of [4; 5] (with keeping query time \(O(\mathrm{polylog}(n))\)) by a \(\mathrm{polylog}(n)\) factor. We again compress the required space of their algorithms.
# LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis
## 2 Related Works
### Transformer-Based Log Anomaly Detection  
Transformer architectures have significantly advanced log anomaly detection by capturing long-range dependencies and semantic relationships within log sequences--without the need for manual feature engineering. This has enabled more flexible modeling of log data compared to traditional sequence models.  
Early works like HitAnomaly [36] used hierarchical encoding of templates and parameters to capture structure, but were limited by their dependence on pre-parsed templates. NeuralLog [37] addressed this by embedding raw logs directly, increasing format robustness, though at the cost of reduced semantic depth due to shallow representations.  
Later models, such as LogBERT [17] and BERT-Log [38], applied masked prediction and fine-tuning to enhance sequence modeling. However, they often required retraining per dataset and struggled with unstructured or noisy logs. Generative models like LogGPT [18] and LogFiT [19] explored event prediction and masked sentence learning, improving adaptability but remaining limited in detecting subtle or multi-step anomalies.  
These approaches share a fundamental limitation: they operate under a passive detection paradigm, processing logs as static sequences without the capacity for dynamic context integration or reasoning across distributed evidence. Overcoming these constraints requires a more proactive and adaptable framework--one that supports contextual reasoning, on-demand information retrieval, and iterative analysis aligned with evolving threat conditions. To effectively handle real-world attack scenarios, models must move beyond static inference and toward intelligent systems capable of dynamic interpretation and multi-source correlation.
## 7 Conclusions  
In this paper, we propose a compact high-efficient equivalent circuit model for the multi-quantum-well VCSEL with the noise included. In the model, the number of nonlinear equations significantly decreases, resulting in improvement of the computation efficiency by 3.3-fold for three QWs-VCSEL and by six-fold for five QWs-VCSEL. In addition, parameter extraction becomes much easier compared to conventional MQW models, due to the reduction in the number of parameters. The model is system-oriented and can be used as a part of the VCSEL-based link modelling.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Unit** & **Value** & **Parameter** & **Unit** & **Value** \\ \hline \(\tau_{\text{inj}}\) & - & 0.86 & b3 & mA/K\({}^{3}\) & \(2.4\times 10^{-4}\) \\ \(\tau_{\text{e}}\) & ps & 400 & b4 & mA/K\({}^{4}\) & \(-1.9\times 10^{-6}\) \\ \(\tau_{\text{c}}\) & ps & 1 & A\({}_{\text{N}_{\text{S}}}\) & s\({}^{-1}\) & \(1.1\times 10^{8}\) \\ \(\tau_{\text{p}}\) & ps & 2.8 & B\({}_{\text{N}_{\text{S}}}\) & s\({}^{-1}\) & 5.3 \\ V\({}_{\text{QW}}\) & m\({}^{3}\) & \(6\times 10^{-18}\) & C\({}_{\text{N}_{\text{S}}}\) & s\({}^{-1}\) & \(1.9\times 10^{-8}\) \\ V\({}_{\text{act}}\) & m\({}^{3}\) & \(2.3\times 10^{-16}\) & A\({}_{\text{N}}\) & s\({}^{-1}\) & \(1.3\times 10^{8}\) \\ N\({}_{\text{SEQ}}\) & m\({}^{-3}\) & \(4\times 10^{19}\) & B\({}_{\text{N}}\) & s\({}^{-1}\) & \(1.8\times 10^{1}\) \\ N\({}_{\text{EQ}}\) & m\({}^{-3}\) & \(6\times 10^{14}\) & C\({}_{\text{N}}\) & s\({}^{-1}\) & \(9.1\times 10^{-6}\) \\ \(\tau_{\text{n}}\) & ns & 5 & A\({}_{\text{S}}\) & s\({}^{-1}\) & 0 \\ R\({}_{\text{sp}}\) & s\({}^{-1}\) & \(1.4\times 10^{12}\) & B\({}_{\text{S}}\) & s\({}^{-1}\) & \(1.8\times 10^{-3}\) \\ b0 & mA & \(5.1\times 10^{-2}\) & C\({}_{\text{S}}\) & s\({}^{-1}\) & 0 \\ b1 & mA/K & \(1.2\times 10^{-1}\) & v\({}_{\text{g}}\) & m/s & \(8.6\times 10^{7}\) \\ b2 & mA/K\({}^{2}\) & \(-8.7\times 10^{-2}\) & G\({}_{\text{N}}\) & m\({}^{-1}\) & \(4.2\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for the rate equations model.  
Figure 7: (**a**) The measured eye diagram of VCSEL at 25 Gbps; (**b**) the simulated eye diagram based on the proposed model.  
**Author Contributions:** VCSEL modeling, S.L.; parameter extraction, S.L.; experiment, S.L., M.S.N., and D.R.; validation: S.L.; writing--original draft preparation, S.L.; writing--review and editing, S.L., M.S.N., D.R. and O.L.-L.; supervision, O.L.-L.; project administration, O.L.-L.; funding acquisition, O.L.-L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Mitacs Accelerate program, Reflex Photonics, FRQNT B2X program and the NSERC Strategic Partnership Grant program.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Multigap Resistive Plate Chambers for Time of Flight Applications
## 3 Applications in Large Experiments
### STAR Experiment in RHIC  
The solenoidal tracker at the relativistic heavy ion collider (RHIC) [47], known as STAR [48], is dedicated to searching for the quark-gluon plasma (QGP) that existed in the early universe. It utilizes a large cylindrical time-projection chamber (TPC) with a large solenoid magnet, providing a close to 4\(\pi\) angle tracking capability for charged particles from collisions. The full-acceptance MRPC-based ToF system [49] was proposed to extend STAR's PID capability.
## 3 Controller Design
### Disturbance Observer
\tag{36}\]  
The surge force and yaw moment can be defined as follows:  
\[\tau_{u}=d_{11}u-m_{22}vr-\hat{d_{1}}+m_{11}\bigg{[}\dot{u}_{d}-\frac{q_{1}}{ \beta_{1}p_{1}}(e_{u})^{2-\frac{p_{1}}{q_{1}}}-\frac{\varepsilon_{1}q_{1}}{ \beta_{1}p_{1}}\mathrm{sign}(S_{1})\bigg{]} \tag{37}\]  
\[\tau_{r}=d_{33}r-(m_{11}-m_{22})uv-\hat{d_{3}}+\frac{m_{22}m_{33}}{m_{22}u_{d}- m_{11}u}\Big{\{}-\frac{1}{m_{22}}\big{(}-m_{11}\dot{u}r-d_{22}v\big{)}+\Gamma\]  
\[\quad\quad-\frac{q_{2}}{\beta_{2}p_{2}}\big{(}\dot{e}_{v}\big{)}^{2-\frac{p_{2 }}{q_{2}}}-\frac{\varepsilon_{2}q_{2}}{\beta_{2}p_{2}}\big{(}\dot{e}_{v}\big{)} ^{1-\frac{p_{2}}{q_{2}}}\mathrm{sign}(S_{2})\Big{\}} \tag{38}\]  
For the stability analysis of the trajectory tracking control law under disturbances, the following Lyapunov function is constructed:  
\[V_{4}=\frac{1}{2}\Big{(}S_{1}^{2}+S_{2}^{2}\Big{)} \tag{39}\]  
Taking the time derivative at both ends of Equation (38), and then substituting the control laws, Equations (36) and (37) yield  
\[\dot{V}_{4} =S_{1}\dot{S}_{1}+S_{2}\dot{S}_{2} \tag{40}\] \[=-S_{1}\Big{[}d_{1}-\hat{d_{1}}+\varepsilon_{1}\mathrm{sign}(S_{1 })\Big{]}-S_{2}\Big{[}d_{3}-\hat{d_{3}}+\varepsilon_{2}\mathrm{sign}(S_{2}) \Big{]}\]Since the designed disturbance observer converges, Equation (39) can be rewritten as  
\[\dot{V}_{4} =-\varepsilon_{1}S_{1}\text{sign}(S_{1})-\varepsilon_{2}S_{2}\text{ sign}(S_{2}) \tag{41}\] \[=-\varepsilon_{1}|S_{1}|-\varepsilon_{2}|S_{2}|\leq 0\]  
where \(\varepsilon_{1},\varepsilon_{2}>0\), \(V_{4}>0\) and \(V_{4}>0\). In a similar way, the designed NTSM control law can converge to \(b\) in a finite period of time. To eliminate the chattering phenomenon arising from the discontinuous sign function, the following saturation function is used to replace the sign function,  
\[\text{sat}(S_{i})=\left\{ \begin{array}{ll}\text{sign}(S_{i}/\phi),&S_{i}>| \phi|\\ S_{i}/\phi,&S_{i}\leq|\phi|\end{array} \right. \tag{42}\]  
where \(\phi>0\) and \(i=1,2\).
## 3 Results
### Mixing Time
#### 3.2.5 Validation and Application of Mixing Time Models  
The correlation of the mixing time by combining the form of Equations (6) and (19) is established by experiment as following:  
\[t_{m}=5.8685(\frac{V_{L}}{D^{3}})^{1.293}(\frac{P}{V_{L}})^{0.3321}\text{ at water }\left(8.94\,\times\,10^{-4}\text{ Pa}\cdot\text{s}\right) \tag{20}\]  
\[t_{m}=3.1854(\frac{V_{L}}{D^{3}})^{0.482}(\frac{P}{V_{L}})^{0.3059}\text{ at }5\%\text{ glycerin }\left(1.35\,\times\,10^{-3}\text{ Pa}\cdot\text{s}\right) \tag{21}\]  
\[t_{m}=2.8893(\frac{V_{L}}{D^{3}})^{0.313}(\frac{P}{V_{L}})^{0.2987}\text{ at }10\%\text{ glycerin }\left(2.05\,\times\,10^{-3}\text{ Pa}\cdot\text{s}\right) \tag{22}\]  
\[t_{m}=2.766(\frac{V_{L}}{D^{3}})^{0.1805}(\frac{P}{V_{L}})^{0.2808}\text{ at }20\%\text{ glycerin }\left(2.4\,\times\,10^{-3}\text{ Pa}\cdot\text{s}\right) \tag{23}\]  
These equations were demonstrated to be valid in the following range and when liquid motion was induced in the orbitally shaken bioreactor:  
\[4.61\times 10^{4}<\text{Re}<1.75\times 10^{5}\]  
\[1<\text{Fr}<2.3\]  
\[2.76\text{ w m}^{-3}\ <\ \frac{P}{V_{L}}\ <\ 6.9\text{ w m}^{-3}\]  
\[0.18\ <\ \frac{V_{L}}{D^{3}}\ <\ 0.46\]  
and when liquid motion was induced in the OSB that can be considered as being fully validated on scale-up.  
Figure 10: Effect of volumetric power consumption on mixing time. (**a**) Water; (**b**) 5 vol% glycerin; (**c**) 10 vol% glycerin; (**d**) 20 vol% glycerin.
## 5 Potential Use Cases
### Services Exploiting TSN over 5G  
The term Industry 4.0 is used nowadays to refer to automation and data exchange in technologies of today's manufacturing companies. The term was first employed by the German government as a strategy to give a modern name to the new industrial revolution, which supposes even more automation than in the third industrial revolution, adding the concept of cyber-physical systems, thanks to industrial IoT. The main aim is to reach a digital transformation with a real-time connected network able to execute autonomous decision-making processes and the corresponding monitoring of the whole system. Accord  
Figure 10: Deployment scenario.  
ingly, 5G networks present a great challenge for these Industry 4.0 uses cases. TSN consists of a set of standards as a key enabler of deterministic and low-latency communication in the factories of the future (FoF). Further, 5G TSN is a service with high reliability and availability, capable of providing packet transport with QoS characteristics. In this context, in the following subsections, two indicative use cases are described, highlighting the need for time synchronization in NPNs deployed in manufacturing environments.