esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis  
### Experimental Environment  
The experiments were conducted in a high-performance computing environment equipped with an NVIDIA GeForce RTX 4090 GPU (NVIDIA Corporation, Santa Clara, CA, USA). The RTX 4090, NVIDIA's high-end graphics processing unit, is renowned for its formidable computational power and efficient parallel processing capabilities, making it ideally suited for executing deep learning and other data-intensive tasks. The hardware platform utilized in the experiments provided the necessary computational resources to support the fine-tuning and training of large-scale language models. The operating system was Ubuntu 20.04, and Python 3.11.9 served as the development framework. To accelerate the inference of large language models, open-source tools such as llama.cpp were employed for quantized inference, and unsloth was applied for instruction tuning.  
Model Selection: Based on the minimal viable analysis in academic research, this experiment selects five models for experimental analysis to observe the impact of model quantization and model versions on the experimental results. The selected models are Llama3 8B, Llama3 8B 4 bit, Llama3.1 8B Instruct, Llama3.1 8B Instruct 4 bit, and Tongyi Qianwen Qwen2.5 14B. These five versions of models have low hardware requirements, can be deployed locally, and all support 128K contexts, meeting the vast majority of needs.  
To evaluate the effectiveness of sensitive data identification, this paper employs the confusion matrix as an assessment metric, where true positives (TPs) represent the number of instances correctly predicted by the model as belonging to the positive class; true negatives (TNs) represent the number of instances correctly predicted by the model as belonging to the negative class; false positives (FPs) represent the number of negative class instances incorrectly predicted by the model as belonging to the positive class; and false negatives (FNs) represent the number of positive class instances incorrectly predicted by the model as belonging to the negative class. Using these four values, we can calculate the accuracy, precision, recall, and F1-score.  
Accuracy: The ratio of the number of correctly predicted samples to the total number of samples. Higher accuracy indicates higher prediction accuracy of the model, as shown in Equation (5).  
\[Accuracy=\frac{TP+TN}{N} \tag{5}\]  
Precision: The proportion of all predicted positive instances that are actually positive, as shown in Equation (6).  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
Recall: This is a metric that measures the proportion of actual positive instances that are correctly predicted by the classification model, as shown in Equation (7).  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
F1-Score: The harmonic mean of precision and recall, as shown in Equation (8).  
\[F1-Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{8}\]
## 2 Methodology
### Scattered Decision-Level Fusion
#### 2.2.2 Estimation of Partial Densities
To this end, we define the normalization factor from Equation (2):  
\[N\left(\mathbf{h}_{S},\Delta^{S}\right)=1/max\left\{\frac{h_{x}}{\Delta_{x}^{S}}, \frac{h_{y}}{\Delta_{y}^{S}}\right\} \tag{5}\]  
This essentially normalizes with regard to the number of pixels per kernel size, which implicitly relates to the potential number of hits in each dimension. Note that if the kernel size \(\mathbf{h}_{S}\) is defined according to Equation (4), then we have \(N\left(\mathbf{h}_{S},\Delta^{S}\right)=1/max\left\{\frac{h_{x}}{\Delta_{x}^{S}}, \frac{h_{y}}{\Delta_{y}^{S}}\right\}=\Delta_{x}^{S}/h_{x}=\Delta_{y}^{S}/h_{y }=min\left\{\Delta_{x}^{S},\Delta_{y}^{S}\right\}/\hat{u}\). In our method, the per-sensor normalization factor \(N\) replaces the conventional kernel normalization factor for product kernels, \(N\left(\mathbf{h}_{S}\right)=1/\left(h_{x}\cdot h_{y}\right)\). Although the previous considerations are valid for all types of kernel functions, we suggest using a compactly supported kernel function like the _Epanechnikov product kernel_ as defined in Equation (6). The compact support has the advantage of limiting the spatial influence area of each hit, which is expressed by the kernel bandwidth parameters \(\mathbf{h}=\left(h_{x},h_{y}\right)\), and thus facilitates faster computation than e.g., the non-vanishing Gaussian kernel:  
\[K_{\mathbf{h}}(\mathbf{v})=\left\{ \begin{array}{c}\left(1-\left(\frac{v_{x}}{h_{x}} \right)^{2}\right)\left(1-\left(\frac{v_{y}}{h_{y}}\right)^{2}\right),|v_{x}| \leqslant h_{x}\text{ and }|v_{y}|\leqslant h_{y}\\ 0,else\end{array} \right. \tag{6}\]  
To further adjust the quantification of density, we scale the kernel functions according to the weight \(w_{d}\) per hit; see Equation (2). These weights control the influence of each hit on the final KDE. We set each weight proportional to the hit's signal to noise ratio, so that clear indications have more impact on the final density than insignificant ones. Also, the weights offer additional flexibility to regulate the fusion result with regards to specific sensors or different inspection areas.
## 4 Numerical Results and Discussion
### Dynamic Analysis  
Subjected to different wind scenarios with wind speed from cut-in to cut-out, the two cases are simulated. To analyze the influence of BCS on dynamic and fatigue characteristics of large-scale wind turbines, we chose two displacement components and four load components from the results of dynamic analysis to describe the wind turbine motions in major locations. The detailed descriptions about the six components are shown in Table 2.  
In No. 2, the three-dimensional curves graphical representation of the relationship between different average \(V_{\text{hub}}\) and rotor speed time histories is shown in Figure 5. And the related three-dimensional curves graphical for \(\beta\) is shown in Figure 6. It is noted that when the wind speed is lower than the rated wind speed, the rotor speed increases with the increase of wind speed in the region of below-rated wind speed range; however, the rotor speed changes around the rated rotor speed in the region of above-rated wind speed range. It is also noted that the \(\beta\) is zero in the region of below-rated wind speed range, and the \(\beta\) increases with the increase of wind speed in the region of above-rated wind speed range. These are the results of BCS regulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline BSX & Blade flapwise tip displacement (relative to the tower top) \\ BFX & Flapwise shear force at the blade root \\ BMY & Flapwise moment at the blade root \\ TTSX & Tower-top fore-aft (FA) displacement \\ TBFX & Tower base FA shear force \\ TBMY & Tower base FA moment \\ \hline \hline \end{tabular}
\end{table}
Table 2: Displacement and load components for wind turbine motions.  
Figure 4: Flowchart of the two cases.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 3 Proposed Method
### Depth Position Mapping and Transformer Integration
#### 3.3.3 Depth Position Mapping (DPM) Module  
The Depth Position Mapping (DPM) module embeds depth-related positional information to enhance the model's understanding of spatial-depth relationships, as shown in Figure 5. Given the input features, \(X\in R^{B\times N\times C}\), where B is the batch size, \(N=H\times W\) is the number of spatial positions, and \(C\) is the number of spatial positions, and \(F\in R^{B\times C\times H\times W}\). Based on previously predicted depth bins \(D_{b}=[d_{1},\dots,d_{D}]\in R^{D\times C}\), where \(D\) is the number of depth bins and \(d_{i}\) represents the learnable embedding for the i-th depth category, the depth features are locally refined using a depth-wise separable convolution with a \(3\times 3\) kernel. The operation is defined as follows:  
\[F^{\prime}=Conv_{3\times 3}(F)+F \tag{9}\]  
where \(Conv_{3\times 3}\) integrates local depth information while maintaining computational efficiency. The resulting depth-sensitive feature map \(F^{\prime}\) is then flattened back to \(\text{R}^{\text{B}\times\text{N}\times C}\), ensuring alignment between the depth positional mappings and the spatial features. By embedding depth-sensitive positional cues through \(D_{b}\), the DPM module improves the model's ability to capture 3D geometric structures, while preserving efficiency and scalability for downstream tasks.
# Modeling and Profiling of Aggregated Industrial Network Traffic
## 2 Network Traffic Modelling
### Network-Level Transmission Intensity  
The variances of the throughput in any specific time frame are the direct result of the total number of transmitted packets and the sum of the transmitted data. Since the number of devices in the network and communication between network entities are predefined, it is valid to assume the throughput turbulence is mainly a variety of changes in the number of transmitting devices and the number of transmissions by these devices, in any time interval. In other words, for profiling different communication states of the network in each work cycle, the number of active transmitters, the number of transmissions by devices, and the amount of transmitted data need to be taken into account. We define the following parameters to capture and quantify the network-level transmission intensity (network-level dynamic).
## 5 Discussion
### Communication Performance
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Message Length** & **4G (100 Mbps)** & **5G (20 Gbps)** \\ \hline
**Authentication phase** & \(1L_{m}+2L_{h}+4L_{sig}+8L_{thr}=\) & \\  & \(1\times 432+2\times 160+4\times 1024+8\times 80=5488\) bits & 5488/102,400 = 0.054 ms & 5488/20,480,000 = 0.27 us \\ \hline
**Event-trigger phase** & \(1L_{m}+2L_{h}+2L_{sig}+4L_{thr}=\) & \\  & \(1\times 432+2\times 160+2\times 1024+4\times 80=3120\) bits & 3120/102,400 = 0.030 ms & 3120/20,480,000 = 0.15 us \\ \hline
**Task allocating phase** & \(2L_{m}+4L_{h}+4L_{sig}+10L_{thr}=\) & \\  & \(2\times 432+4\times 160+4\times 1024+10\times 80=6400\) bits & 6400/102,400 = 0.065 ms & 6400/20,480,000 = 0.31 us \\ \hline
**Task accomplished phase** & \(3L_{m}+6L_{h}+6L_{sig}+11L_{thr}=\) & \\  & \(3\times 432+6\times 160+6\times 1024+11\times 80=9280\) bits & 9280/102,400 = 0.091 ms & 9280/20,480,000 = 0.45 us \\ \hline \hline \end{tabular} Notes: \(L_{m}\): The message length of an access or event message (432 bits). \(L_{h}\): The message length of a hash value (160 bits). \(L_{sig}\): The message length of a signature (1024 bits). \(L_{char}\): The message length of other (80 bits).  
\end{table} 
Table 5: Communication costs of the proposed scheme.
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 2 Requirements of Loop-Free Rerouting
### OSPF Link Weight and Fake Node Assignment  
Given the desired working paths and that every node in the acyclic graph generated by the working paths has at least one R1, R2, or R3 neighbor, we need to assign OSPF weight and fake node assignment to let the flows follow the desired routing paths in the normal state. In addition, we also need to deploy some fake nodes so the network can successfully recover from a failure.  
We apply the same technique introduced in COYOTE to deploy fake nodes to realize the routing for working paths. As shown in Figure 6, the acyclic graph represents the routing paths toward destination node D. Each node sends 5 Mbps to D. The demand volume carried by each link is indicated in the graph. The total demand from C to D is 15 Mbps (5 Mbps from A and 10 Mbps from C). If we want the two outgoing links (C, G) and (C, E) to carry 10 and 5 Mbps, respectively, the splitting ratios for link (C, G) and (C, E) are 2:1. Therefore, two fake nodes and one fake node are deployed associated with link (C, G) and (C, E), respectively. As a result, C follows the ECMP rule to split the traffic to (C, G) and (C, E) with the target ratio 2 and 1.  
In our system, the OSPF weight for each link is assigned to be 10 and the weight for the fake link is one. To simplify this example, we do not include the backup path planning in this example. To deploy a R2 neighbor or a R3 neighbor, we need to introduce an additional fake node. For example, if there is a link (B, C) in the physical network, node C is a R3 neighbor of node B because they are in different branches. We can generate a fake node associated with link (B, C) with path cost 2. In the normal state, E is the only next hop node of B. As node E or link (B, E) fails, node B will take C to reroute the traffic with destination node D. Since the traffic reroute is triggered by LFA, the network can recover a failure within a very short time. We can apply the same technique to generate a fake node for the R3 neighbor. After inserting the fake nodes to enable R2 and R3 neighbors, the network is guaranteed to be 100% survivable against any single node or single link failure.
# Determinization and Minimization of Automata for Nested Words Revisited
## 3 Nested Regular Expressions
### Determinization of NWAs  
Determinization for _NWAs_ was first studied by [PERSON] and [PERSON] [2] in the 1980s, where _NWAs_ are named _input-driven pushdown automata_. We notice that the determinization algorithm was published only in the journal version of this paper, but not in the conference version. Later on, the same algorithm was rediscovered in the context of visibly pushdown automata and republished for nested word automata.  
**Definition 2**.: _An NWA \(A\) is called deterministic or equivalently a MNWA if_  
* _I contains at most one element;_
* _there is no epsilon rule, i.e.,_ \(\varepsilon^{\Delta}=\emptyset\)_,_
* \(a^{\Delta}\) _and_ \(\_^{\Delta}\) _are partial functions from_ \(Q_{h}\) _to_ \(Q_{h}\) _for all_ \(a\in\Sigma\)_, and_ \(\tau^{\Delta}\) _is a partial function from_ \(Q_{h}\) _to_ \(Q_{h}\)_;_
* _for all_ \(q\in Q_{h}\) _and_ \(\gamma\in\Gamma\) _there exists a most one_ \(q^{\prime}\in Q_{h}\) _such that_ \(q^{\prime}\in\langle\lambda^{\Delta}_{\gamma}\)_; and_
* \(\rangle^{\Delta}_{\gamma}\) _is a partial function from_ \(Q_{t}\) _to_ \(Q_{h}\) _for all_ \(\gamma\in\Gamma\)_._  
**Proposition 1** ([PERSON] and [PERSON] [2]).: _A NWA with \(n\) states can be determinized in time \(O(2^{n^{2}})\)._  
Many of our results are based on the determinization algorithm going back to [PERSON] and [PERSON]. For self-containedness, we recall the version of this algorithm that we will use in the Appendix A. For illustrations, the determinization of the _NWA_ in Figure 6 is also presented here too, see Figure 19. It has size 271 while the nondeterministic _NWA_ has size 39 (12 states + 2 letters + 3 stack symbols + 22 rules). The blow-up is even worse in general as our experimental results will show and as noticed earlier by [18].
Evaluation of the Absorption of Methionine Carried by Mineral Clays and Zeolites in Porcine _Ex Vivo_ Permeability Models  
[PERSON]  
1 Dipartimento di Scienze Veteriarie per la Salute, la Produzione Animale ela Sicureza Alimentare (VESPA), Universita Degli Studi di Milano, Via del'Universita 6, 26900 Lodi, Italy; [EMAIL_ADDRESS] (S.P.) [EMAIL_ADDRESS] (M.D.) [PERSON]L_ADDRESS] (A.B.) [EMAIL_ADDRESS] (L.R)  
[PERSON]  
1 Dipartimento di Scienze Veteriarie per la Salute, la Produzione Animale ela Sicureza Alimentare (VESPA), Universita Degli Studi di Milano, Via del'Universita 6, 26900 Lodi, Italy; [EMAIL_ADDRESS] (S.P.) [EMAIL_ADDRESS] (M.D.) [PERSON]L_ADDRESS] (A.B.) [EMAIL_ADDRESS] (L.R)  
[PERSON]  
3 Dipartimento di Chimica, Materiali e Ingegneria Chimica \"[PERSON]\" CMIC, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy; [EMAIL_ADDRESS] (A.B.) luciana.rossi@unimi.  
Among essential amino acids, methionine (Met) has key importance in protein synthesis, methyl donation, and in antioxidant and immune defense processes in swine [5]. Free Met delivery may recover growth performance in pigs receiving a Met-deficient diet [6; 7] due to nitrogen retention and muscle protein accretion by increasing protein biosynthesis [8] and decreasing protein degradation [9].  
However, thermal processing has negative effects on sulfur amino acids such as Met and cystine. Therefore, supplemental dietary AAs need to be provided in a form preventing their degradation during technological treatments to guarantee high bioavailability in the organism.  
Although the protection of amino acids finds its natural application in dairy cow feeding due to its essential role in rumen protection [10; 11], the application of delivery systems may represent an efficient solution for reducing peptide-food matrix reactions and food processing protection.  
An important prerequisite of a good delivery system, as applied to the encapsulation of food bioactive, is that it must not modify the physicochemical and organoleptic properties of the product [12] nor have reactivity with the nutritional compound. A broad spectrum of colloidal systems has been proposed as carriers for oral peptides delivery, such as liposomes, microemulsions, emulsions, biopolymer microgels, and solid lipid nanoparticles [13; 14].  
Our group developed a protocol for the protection of Met via organolx intercalation (natural carriers) as more sustainable alternatives to polymeric coating [15; 16]. In particular, two different bentonite-zeolite-based mineral clays were tested, Adsorbene (ADS) and BioKi (BIO). In the present study, the release of Met from ADS (Met-ADS) and BIO (Met-BIO) was tested. Furthermore, given the higher Met release from Met-ADS, its effect on _in vitro_ culture of porcine intestinal epithelial cells (IPEC-J2) and Met absorption capacity in jejunum using an _ex vivo_ porcine permeability model were tested.
###### Abstract  
This paper proposes a novel hardware design for a compact crypto processor devoted to elliptic-curve point multiplication over \(GF(2^{233})\). We focus on minimizing hardware usage, which we obtain using an iterative bit-serial finite field modular multiplier for polynomial coefficient multiplication. The same multiplier is also used for modular squares and inversion computations, further optimizing the hardware footprint. Our design offers flexibility by permitting users to load different curve parameters and secret keys while keeping a low-area hardware design. To efficiently generate the control signals, we utilize a finite-state-machine-based controller. We have implemented the proposed crypto processor on Virtex-6 and Virtex-7 FPGA devices, and we have evaluated its performance at clock frequencies of 100, 50, and 10 MHz. Specifically, for one point multiplication computation on Virtex-7 FPGA, our crypto processor uses 391 slices, attains a maximum frequency of 161 MHz, has a latency of 4.45 ms, and consumes 77 mW of power. These results, along with a comparison to state-of-the-art designs, clearly demonstrate the practicality of our crypto processor for applications requiring efficient and compact cryptographic computations.  
area-efficient; crypto processor design; ECC; point multiplication; FPGA +
Footnote †: journal: Applied  
_sciences_
## 3 Results and Discussion
### Cell Viability Assessment  
To evaluate user safety, the cytotoxic effects of BLAuNPs, ECAuNPs, and Chemicals-5 nm on viable HEMn cells and CCD966 SK cells were assessed after a 72 h treatment by using the MTT method. Based on previous results (Figure 3 and Table 1), the maximum required concentrations for BLAuNPs, ECAuNPs, and Chemicals-5 nm to achieve optimal antioxidant and antimicrobial activity were 18.2, 14.8, and 50 mg/L, respectively. Therefore, the effects of 0-100 mg/L AuNPs on cell viability were evaluated.  
Figure 5 shows that the cytotoxicity of AuNPs to the tested cells increases as the AuNPs concentration increases. Compared with BLAuNPs, ECAuNPs were more toxic. At up to 2 \(\times\) IC\({}_{50}\) for BLAuNPs and ECAuNPs (36.4 and 29.6 mg/L, respectively), the cell viability of HEMn and CCD966 SK cells still exceeded 98.0%. When the tested concentration of Chemicals-5 nm was up to 2 \(\times\) MIC (100 mg/L), the cell viability exceeded 92.1%. No obvious signs of cytotoxicity were observed compared with control. In addition, the differences in the inhibition of cell viability by BLAuNPs and ECAuNPs were non-significant (\(p>0.05\)) between CCD966 SK and HEMn cells when the tested concentration was \(\leq\)60 mg/L. However, previous reports have indicated that AuNPs biosynthesized by Albizia amara exerted cytotoxic effects on HeLa and Vero cells, with IC\({}_{50}\) values of 47.77 and 72.28 mg/L, respectively [33]. The IC\({}_{50}\) of AuNPs biosynthesized by Olax nana on HepG2 cells was 2.97 mg/L [22]. Previous studies have revealed that AuNPs could be uptaken by cells through the clathrin-mediated endocytosis pathway [12; 38]. Thus, the cytotoxicity of BLAuNPs and ECAuNPs to CCD966 SK and HEMn cells can be prevented in a similar manner. These results clearly indicated that BLAuNPs and ECAuNPs can be safely applied in the medicine and cosmetics.
## 2 Introduction to the Details of the Algorithm
### Sheep Flock Optimization Algorithm (SFOA)
#### 2.2.2 Two-Dimensional Sheep Flock Movement Factor  
The physical meaning of the two-dimensional movement factor of the sheep flock \(\Phi(P^{n},P^{m},step^{n})\) is that sheep \(n\) moves towards sheep \(m\), and \(step^{n}\) is the moving step size of sheep \(n\). The movement process is as follows.  
(1) Randomly select a certain row \(i\) and column \(j\) in \(P^{n}\), determine the value of \(P^{n}_{i,j}\), and compare this value to \(P^{m}_{i,j}\); if the two values are equal, repeat step (1).  
(2) If the value of \(P^{m}_{i,j}\) is 1, change the value of \(P^{n}_{i,j}\) to 0, and jump to step (4).  
(3) If the value of \(P^{n}_{i,j}\) is 0, change the value of \(P^{n}_{i,j}\) to 1.  
(4) Number of iterations + 1; if the number of iterations is less than \(step^{n}\), jump to step (1); otherwise, end the loop.  
The pseudo-code for sheep movement is shown in Algorithm 2.
## 2 Background
### 3D ECT Image Reconstruction
They involve an iterative non-linear reconstruction loop, where the numerical forward problem and the 3D sensitivity map are optimized and converged through incremental adjustments to the image's dielectric properties distribution. These techniques commonly integrate Finite or Boundary Element Methods, employing precise 3D grid discretization coupled with numerical simulations of ECT sensor structures [9]. The non-linear reconstruction method with a complete 3D ECT sensor model was the starting point for the enhancement of the 3D image resolution of GCN-based ECT [3], as it has been successfully validated for high-quality 3D ECT image reconstructions of challenging dielectric objects in the past [9]. This algorithm defines a process for finding an approximate 3D inverse solution using a fully non-linear scheme, a complete simulated 3D ECT sensor model, and direct FEM-based iterative back-projection image optimization. The flowchart below (Figure 4) shows the main blocks and data flow in this method:  
Artificial Neural Networks (ANNs) have recently been used to improve the reconstruction of ECT images and improve the 3D ECT image quality. Most applications of ANNs in  
\[\
abla[\epsilon(x,y,z)\
abla\phi(x,y,z)]=0\ \ x,y,z\in\ \Omega \tag{1}\]0 Flowchart of NLECTCM 3D reconstruction algorithm [9]. Electrical Capacitance Tomography involve integration with direct or iterative image reconstruction methods. These algorithms have been adapted to refine the electrical tomography image reconstruction process and to enable faster extraction of industrial process parameters. Examples include auto-encoder-based image reconstruction methods [16], deep learning [17], Convolutional Neural Networks (CNNs) [18], multiscale CNNs [19], DBAR methods [20], LSTM methods and their optimizations [21; 22], and Graph Convolutional Networks (GCNs) [3]. As Graph Convolutional Networks (GCNs) become increasingly prevalent in 3D ECT image reconstruction, the need for reliable image quality metrics has become increasingly apparent. This paper seeks to establish a new benchmark for evaluating the fidelity of GNN-based 3D ECT images.