esa-sceva/satcom-chunk-collection huggingface dataset:
# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
## 3 Air-YOLOv3  
In terms of the limited computing capacity of the UAV, it is vital to balance model performance and high computation overhead.  
We propose an improved YOLOv3, AIR-YOLOv3, which learns a more compact network structure by pruning to detect pedestrians in infrared images, as shown in Figure 1. Firstly, aerial infrared pedestrian data was captured through the self-built UAV, and it was manually cleaned and marked, as shown in Figure 1a,b. Secondly, according to aerial data characteristics, the object's anchor boxes are reclustered, and a more appropriate number and size of anchor boxes are reselected. Third, we fine-tuned YOLOv3 on the self-build dataset, as shown in Figure 1c. Fourth, we imposed channel-wise sparsity by introducing Smooth-L1 regularization on scale factors and pruning the channels with less feature information, as shown in Figure 1d. After channel pruning, the layer pruning, as shown in Figure 1e, was carried out to further compress the model. Figure 1d,e show the processes of pruning optimization. Finally, the model is deployed on the airborne NVIDIA Jetson TX2.  
The main optimization process of AIR-YOLOv3 is shown in Algorithm 1. The whole process is divided into three modules: (a) routine training, corresponding to steps 1 to 4;(b) sparse training, corresponding to steps 5 to 8; (c) channel pruning and layer pruning, corresponding to steps 9 to 13.  
**Input**: the training set \(\mathcal{D}_{train}\) and the validation set \(\mathcal{D}_{val}\)  
**Output**: the pruned model  
```
1:Initialization by loading the pre-trained model
2:while not converge do
3: Sample data from \(\mathcal{D}_{train}\) and optimize the weights to minimize the \(Loss_{yolov3}\).
4:endwhile
5:while not converge do
6: Add Smooth-L1 regularization on scale factors \(\gamma\) for channel-wise sparsity
7: sample data from \(\mathcal{D}_{train}\) and optimize the weights to minimize the \(Loss_{total}\).
8:endwhile
9:calculate \(\pi\): the minimum value within the maximum \(\gamma\) of every BN layer
10: Use the local threshold \(\pi\) and pruning ratio \(\eta\) to determine the channels that need to be pruned.
11: Prune the channels of the corresponding convolutional layer.
12: calculate \(\tilde{\gamma}_{i}\) of the BN layer before the **23** Res-units of the pruned channel model, \(Sort\{\tilde{\gamma}_{1},\tilde{\gamma}_{2},\ldots,\tilde{\gamma}_{22},\tilde {\gamma}_{23}\}\)
13: Remove the connection of res-units with small \(\tilde{\gamma}\) according to the given pruned layers number.
14: Fine-tune model
```  
**Algorithm 1** Procedure of AIR-YOLOv3.
## 3 Results
### Flow Cytometry Experiments  
HepaRG cell survival was evaluated by flow cytometry (PI staining) for the compounds with the highest interest (Figure 4). The reduction in cell viability for compound **4** was not statistically significant at 24 h. However, compound **5** led to a drastic reduction in cell viability (92%). Additionally, in Figure 5 are presented images of HepaRG cells after the treatment with this compound.  
Figure 3: _E-screening_ assay of compounds **4** and **5** in T-47D cells. Each bar represents the mean \(\pm\) SD (two independent experiments). *** \(p\) < 0.01 vs. control; *** \(p\) < 0.001 vs. control (one-way ANOVA post-hoc Bonferroni test).  
Figure 2: Relative cell proliferation of MCF-7, T47-D, LNCaP, HepaRG, and Caco-2 cancer cells and normal fibroblasts (NHDF) exposed to the tested compounds for 72 h at 30 \(\mu\)M (MTT assay). Data are expressed as a percentage of cell proliferation relative to the negative control, are presented as mean \(\pm\) SD, and are representative of at least two independent experiments. *** \(p\) < 0.01; *** \(p\) < 0.001 vs. control (Student \(t\)-test).  
Despite the interesting IC\({}_{50}\) and SI values determined for compound **5** (Tables 1 and 2) and the drastic reduction in cell viability, this compound had a potential estrogenic effect (Figure 3), which is not interesting for the development of anticancer drugs. Therefore, it was decided to explore the effects of compound **4** in cell cycle progression by flow cytometry as well as by the carboxyfluorescein succinimidyl ester assay. By means of a cell cycle distribution study, it was evidenced that steroid **4** (50 uM) induced a G\({}_{0}\)/G\({}_{1}\) cell cycle arrest at 24 h post treatment (Figure 6), with an increase in cell percentage in G\({}_{0}\)/G\({}_{1}\) and a reduction in the percentage of cells in S phase. Using an adapted carboxyfluorescein succinimidyl ester assay, the cell proliferation after 48 and 72 h was also studied [48]. Interestingly, compound **4** led to a higher intensity signal than control cells due to a lower number of cell replications (Figure 7). Furthermore, from 48 to 72 h, control cell median fluorescence diminished (evidence of cell division), but the fluorescence of treated cells remained unchanged, suggesting that no cell division occurred during this time period.  
Figure 4: Percentage of viable HepaRG cells treated with steroids **4** and **5** (50 uM, for 24 h) by flow cytometric assay with propidium iodide (PI) staining. Untreated cells were used as the control. The percentage of cells in R1 (live cells, PI negative) as compared to the total number of events in R1, R2 (dead cells), and R3 (undetermined cells) was considered the percentage of viability. Each bar represents the mean \(\pm\) SD (originated from two independent experiments). * \(p\) < 0.05; *** \(p\) < 0.001 vs. control (two-way ANOVA post-hoc Bonferroni test).  
Figure 5: Microscopic visualization of HepaRG cells (**A**), control treated with compound **4** (**B**), compound **5** (**C**), and 5-fluorouracilacil (**D**) at 50 uM for 24 h. Amplification of 100\(\times\).
## 4 Experiments and Result Analysis  
### Experimental Setup  
The experiment was conducted on a 110 kV GIS (Sieyuan ZF28A-145, rated voltage 145 kV, rated frequency 50 Hz) in the laboratory. Figure 3 shows the detection system of the experiment. The AE sensor used to detect ultrasonic PD signals were mounted on the surface of the GIS with a thin layer of acoustic couplant to assure great sensitivity and fixed by tape. The signals detected by the sensor were amplified by the preamplifier and transmitted to the LeCroy storage oscilloscope. The AC free-halo test transformer (YDTW-25/100) was adopted as a power supply. The average temperature of the test environment was 25 \({}^{\circ}\)C. The sampling frequency of the oscilloscope was set to 50 Ms/s.  
Four typical insulation defect models of GIS were designed, as shown in Figure 4.  
Figure 4: The designs of four typical kinds of insulation defects. (**a**) Metal protrusion; (**b**) Floating electrode; (**c**) Free metal particles; (**d**) Creeping discharge.  
Figure 3: The detection system of insulation defects of GIS.  
The steps of the experiment are as follows:  
1. Test air tightness for safety. Pump the chamber of the GIS to vacuum, and 24 h later, measure the pressure in the device. The GIS can be used for experiments only if the air leakage is less than 0.001 MPa/h.
2. Place the defect model into the chamber of the GIS shown as Figure 5.
3. Evacuate the chamber, and then charge with SF6 gas of about 0.5 MPa.
4. Connect the experimental devices according to the wiring in Figure 3.
5. Apply voltage to the device and increase the voltage slowly and observe the screen of partial discharge detector (DDX9101) and the waveform on the oscilloscope.
6. When a partial discharge occurs, stop increasing the voltage and collect several sets of signals with the storage oscilloscope.
7. Before replacing another defect model in the tank of the GIS, slowly decrease the voltage applied to the device to 0, turn off the high-voltage power supply, and remove the wiring.
8. Repeat Steps 2-7 until all the defect models have been tested.
# Petri Net-Based Semi-Compiled Code Generation for Programmable Logic Controllers
## 2 Methodology
### Algorithm for PN Marking Management  
The basis of the proposal is a PN-based algorithm with compliance with the following specifications:  
* An appropriate program block structure, with a fixed part (related to PN structure and marking evolution) and a customizable or to-be-programmed part (PN interpretation), properly differentiated.
* Apply PN marking evolution rules.
* Coding in ST, due to (i) simple portability regardless of target device vendor, (ii) operating matricially element by element.  
Figure 4: Proposed PN implementation in PLC.  
* Variables required for algorithm proper operation already pre-declared, and PN structure matrix representation not declared, for its direct transfer by a parser application.  
Note that the only task related to PN structure is transferring its matrix representation (C, \(m_{0}\)) to PLC data memory. PN interpretation has to be coded, differing upon particular application.  
In compliance with the specifications described above, the algorithm kernel implements the transition firing and marking updating rules for a general PN, as detailed in Algorithm 1. Provided with any particular PN pre-incidence and post-incidence matrices and initial marking vector contained in a PLC data block, this algorithm allows token movements among places managing, thus allowing transition enabling, firing and resulting marking computation. It is coded in ST, in order to operate more simply with matrices and for an easier portability to other manufacturers' devices. The program block execution flow is described below.  
```
\(i=1,...,m\); where \(m\) is the cardinality of \(P\); \(\wedge j=1,...,n\); where \(n\) is that of \(T\).  marking initialization: \(x(p_{i}):=x_{0}(p_{i})\);  incidence matrix initialization: \(Inc(p_{i},t_{j}):=Post(p_{i},t_{j})-Pre(p_{i},t_{j})\); whileTRUEdo  create list of enabled transitions \(E(t_{j})\):  a transition is enabled \(\Longleftrightarrow x(p_{i})\geq Pre(p_{i},t_{j}),\forall p_{i}\in I(t_{j})\); \(I(t_{j})\) is the set of input places of a transition \(t_{j}\in T\).  compute conditions list: \(Cond(t_{j}),\forall t_{j}\in E(t_{j})\);  create list of firable transitions \(F(t_{j})\):  a transition is firable \(\Longleftrightarrow Cond(t_{j})=TRUE,\forall t_{j}\in E(t_{j})\); if\(\exists t_{j}/F(t_{j})\
eq 0\)then  decide on which transition \(t_{j}\in F(t_{j})\), if any, should be fired; if it is decided to fire \(t_{j}\in F(t_{j})\)then  compute new marking reached: \(x^{\prime}(p_{i}):=x(p_{i})+Inc(p_{i},t_{j})\);  update marking: \(x(p_{i}):=x^{\prime}(p_{i})\);  update actions: \(\forall p_{i}/x(p_{i})>0\Longrightarrow Acc(p_{i}):=TRUE\);  end if  end while  end while
```  
**Algorithm 1**Transition firing and marking updating for a general PN.
# Precise Photon Correlation Measurement of a Chaotic Laser
## 5 Influences of Detector Time Resolution and High Order Omitted Terms
The relative error caused by the coherence time was smaller than that of the photon intensity. We compared the relative error caused by the above two factors (photon intensity and coherence time). The yellow dashed line in Figure 7 indicates the case that the coherence time was 0.5 ns, which corresponds to the experiment condition. For the same delay time, the relative error caused by coherence time was lower than that caused by photon intensity. Thus, high accuracy \(g^{(2)}(\tau)\) requires well controlling the photon intensity [24]. From the above discussion, the high order correction of second order photon correlation was affected by the variations of the mean photon intensity and coherence time of the laser, and we analyzed the relative error caused by the two factors respectively. The relative error from incident photon intensity was larger than that from coherence time. In Figure 7, the dashed line on the error surface was under the condition that the intensity was 0.04 photons/ns and \(\tau_{c}\) was 0.5 ns, which corresponds to the experimental condition. In our experiment, the maximum relative error in ninth order correction of \(g^{(2)}(\tau)\) did not exceed \(5\%_{\infty}\) within 50 ns delay time. The relative errors caused by the photon intensity and coherence time retained the uncertainty \(\pm 0.01\) photon/ns and \(\pm 0.2\) ns respectively, and the overall error within 50 ns delay time did not exceed \(1\%\) in our condition. Figure 6: The relative error varying with the photon intensity and delay time. The coherence time is set as one ns. Figure 7: Relative error as functions of coherence time and delay time. The photon intensity was 0.04 photons/s and the yellow dashed line corresponds to the experimental condition.
# Optimized Implementation of Argon2 Utilizing the Graphics Processing Unit
###### Abstract  
In modern information technology systems, secure storage and transmission of personal and sensitive data are recognized as important tasks. These requirements are achieved through secure and robust encryption methods. Argon2 is an advanced cryptographic algorithm that emerged as the winner in the Password Hashing Competition (PHC), offering a concrete and secure measure. Argon2 also provides a secure mechanism against side-channel attacks and cracking attacks using parallel processing (e.g., GPU). In this paper, we analyze the existing GPU-based implementation of the Argon2 algorithm and further optimize the implementation by improving the performance of the hashing function during the computation process. The proposed method focuses on enhancing performance by distributing tasks between CPU and GPU units, reducing the data transfer cost for efficient GPU-based parallel processing. By shifting several stages from the CPU to the GPU, the data transfer cost is significantly reduced, resulting in faster processing times, particularly when handling a larger number of passwords and higher levels of parallelism. Additionally, we optimize the utilization of the GPU's shared memory, which enhances memory access speed, especially in the computation of the hash value generation process. Furthermore, we leverage the parallel processing capabilities of the GPU to perform efficient brute-force attacks. By computing the H function on the GPU, the proposed implementation can generate initial blocks for multiple inputs in a single operation, making brute-force attacks in an efficient way. The proposed implementation outperforms existing methods, especially when processing a larger number of passwords and operating at higher levels of parallelism.  
Argon2; password hash function; GPU; optimized implementation; cracking +
Footnote †: journal: Applied Sciences
## 4 Comparison of Attacks According to Affected Parameters
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Attacks**} & \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Example**} & \multirow{2}{*}{**Objectives**} & \multicolumn{3}{c}{**Affected Parameters**} \\ \cline{3-6}  & & & _Confidentiality_ & _Integrity_ & _Availability_ \\ \hline Insider & [60] & Flooding attacks, user to root, and port scanning & The authorized user tries to harm the network. & \(\surd\) & x & \(\surd\) \\ \hline Flooding Attacks & [61] & DOS, DODS, Direct and Indirect DOS, & Attackers try to flood or block a machine or network by sending invalid information. & x & x & \(\surd\) \\ \hline DOS & [61,62] & Pring of death, Buffer overflow, ICMP flood, Smart, UDP flood, SYN flood. & Attackers try to make resources unavailable to the intended users. & x & x & \(\surd\) \\ \hline Port Scanning & [63] & TCP scanning, UDP Scanning, SYN scanning, FIN scanning, ACK & Attackers try to find the open port, closed ports, and & & & \\  & & scanning, PIN scanning, ACK & filtered port in a list of open ports for attacking services & x & x & \(\surd\) \\ \hline Application Layer & \multirow{2}{*}{[62]} & Spamming, race condition attacks, buffer overflow attacks, and man-in-the-middle attacks. & These attacks target the application layer and cause faults in the application or in a server’s operating system. & x & \(\surd\) & \(\surd\) \\ \hline Passive attacks & [35,64] & Eavesdropping, traffic analysis, and location disclosure. & The intention of passive attackers is to disturb the performance and operation of the network and locate valuable information. & \(\surd\) & \(\surd\) & x \\ \hline Active attacks & [35,64] & Routing attacks and malicious traffic dropping. & Active attacks interrupt the network operation by introducing malicious code, modifying information, and causing damage to the entire network, which results in network performance degradation. & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline Routing Attacks & [65] & Spoofing attacks (IP and URL spoofing), rushing, gray hole, black, Okyl, man-in-the-middle attacks, and sleep deprivation. & Routing attacks aim to modify the routing protocol in mobile ad hoc network (MANET). & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline Code Red Attacks & [66] & To exploit a known vulnerability in Microsoft IIS servers. & x & \(\surd\) & \(\surd\) \\ \hline Side Channel & [23] & To extract confidential information from systems by exploiting computational characteristics. & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline Cover Channel & [67] & To extract secret information by using a covert channel. & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline Adversarial & \multirow{4}{*}{[59]} & These attacks disable the IDFS and affect the detection accuracy of IDS by modifying their internal processing and disrupting the functionality of the detection algorithm. & \(\surd\) & x & x & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of attacks according to affected parameters.
# A Unified Theory for 3D Gear and Thread Metrology
## 5 Some Examples  
In this section, two examples are introduced to underpin the advantages of holistic and three-dimensional inspections. Two artifacts representing common helical machine elements were selected for this purpose: a single-start right-handed screw thread ring gauge and a right-handed involute cylindrical gear artifact with twelve teeth and certain flank modifications. The examples presented are intended to give a qualitative impression of the advantages of the areal measurement and evaluation approach rather than a quantitative comparison with the classical measurement strategy. For screw threads, such a comparison can be found in [19]. For involute gears, this is part of ongoing research and will be presented in a later publication.  
In order to capture the surface of the parts, measurements were taken under laboratory conditions with temperature control \(T=20\,^{\circ}\mathbb{C}\pm 0.2\,\mathrm{K}\) on high-precision coordinate measuring machines equipped with integrated rotary tables with ball styli for tactile probing. To meet the definition of a holistic evaluation, the reference axis remained fixed during the measurements. For screw thread calibration, the surface was measured by scanning the flanks along the helix successively on varying radii. For gear calibration, additional profile scans were taken.
## 1 Introduction
Compared to surface-treated implants with other conventional methods, femtosecond laser-treated titanium implants are expected to bond more strongly with bone tissue and recruit osteocytes at a faster rate, based on the degree of roughness. To date, few clinical studies using animal models have compared the microstructure of femtosecond laser-treated titanium implants to assess their osseointegration benefits. Research on femtosecond laser-treated titanium implants is relatively recent, and therefore, comparative studies with conventional implants such as SLA (Sandblasted, Large-grit, Acid-etched) implants are limited. The purpose of this study was to compare the degree of osseointegration and the soft-tissue healing patterns and duration for femtosecond laser surface-treated titanium implants (FEMTO group) versus sandblasted, large-grit, acid-etched (SLA) surface-treated titanium implants (SLA group). Using a BLP10 (Saeshin Precision Co., Ltd., Daegu, Republic of Korea), a hybrid model of a laser and implant placement drill, and a control group using a scalpel and conventional implant placement drill, this study evaluated the titanium implants in a rabbit model through macroscopic observation, histologic analyses, and radiologic analyses.
## 5 Model for a DM Whose Preferences Are Compatible with a Weighted-Sum Function
### Making judgments of Satisfaction and Dissatisfaction with the Functional Model
The following propositions are valid:_  
* \(x\) _is assigned to a single element of the set of classes (satisfactory, unsatisfactory, neither satisfactory nor unsatisfactory)._
* _The assignment suggested for_ \(x\) _is independent of the assignment of other solutions._
* _The class to which_ \(x\) _is assigned to by the_ \(i\)_-th DM is independent of the assignment made by any other DM._
* _If there is_ \(b_{k}\in B\) _fulfilling_ \(x=b_{k}\)_, then_ \(x\) _is neither satisfactory nor unsatisfactory._
* _If_ \(x=x^{*}\)_, then_ \(x\) _is assigned to the satisfactory class._
* _Suppose that_ \(x\) _is_ \(\alpha\)-preferred to all_ \(b_{k}\in B\)_. Let_ \(y\) _be a feasible solution such that_ \(yD(\alpha)x\)_. Then_ \(y\) _is assigned to the satisfactory class._  
Proof. : Proposition 3(a): The proof is obvious from Definitions 16, 18, 19, and 20. Proposition 3(b): The proof is obvious from Definitions 16, 18, 19, and 20. Proposition 3(c): The proof is obvious from Definitions 16, 18, 19, and 20. Proposition 3(d): From the way in which \(B\) is built (Assumption 7), there is no \(b_{i}\in B\) such that \(b_{k}\) is _\(\alpha\)-preferred_ to \(b_{i}\) or \(b_{j}\) is _\(\alpha\)-preferred_ to \(b_{k}\). Then, \(b_{k}\) is not _\(\alpha\)-preferred_ to \(B\) and is not _\(\alpha\)-preferred_ to \(b_{k}\) (Definition 16). From Definition 20, it follows that \(b_{k}\) is neither satisfactory nor unsatisfactory. Proposition 3(e): The proof follows from Definitions 16 and 18 and the way in which the limiting boundary is built (Assumption 7). Proposition 3(f): It is evident that \(y\)\(D(\alpha)x\) and \(P(\mathbf{U}(x)\geq\mathbf{U}(b_{k}))\geq\alpha\Rightarrow P(\mathbf{U}(y)\geq\mathbf{U}(b_{ k}))\)\(\geq\alpha\). In addition \(yD(\alpha)x\) and \(xD(\alpha)b_{k}\Rightarrow yD(\alpha)b_{k}\) from transitivity of dominance (Proposition 1.v). Hence, \(y\) is \(\alpha\)-_preferred_ to the Boundary \(B\) (Definitions 14 and 16). From Definition 18, \(y\) is assigned to the satisfactory class. The proof is finished. **Remark 6**. :
1. _Unlike the proposal in Section_ 4_, the solutions in the limiting boundary do not belong to any class. Objectives of these solutions may be described by interval numbers, what gives more flexibility to the DM and may reduce their cognitive effort._
2. _Proposition_ 3.4 _is consistent with Condition iii of Assumption_ 7_. It follows that a solution only slightly different from any_ \(b_{k}\in B\) _should be considered neither satisfactory nor unsatisfactory by the DM._
# Grid Anchor Lane Detection Based on Attribute Correlation
## 4 Experimental Results
### Experimental Setting
#### 4.1.2 Implementation Details  
For convenience in computation, the CULane and Tusimple images fed into the model are resized to 1600 \(\times\) 320 and 960 \(\times\) 480, respectively. The model training process consists of 40 iterations using the SGD optimizer with an initial learning rate of 0.015. The learning rate is reduced by 1/10 at epochs 15 and 25. The deep learning framework used for the model is PyTorch 1.12.1, and distributed training is performed on a server equipped with 8 NVIDIA GeForce RTX 2080 Ti GPUs, with a batch size of 12 per GPU. During training, random rotations, scaling, and translations of the images are applied for data augmentation. To prevent overfitting, dropout is used in the MLP with a dropout rate of 0.3.