esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 3 New Mathematical Model
### Semi-Markov Process Model for 3Z  
Many studies on location registration assume that sojourn time in a zone follows an exponential distribution. On the other hand, our model applies to any general distributions for sojourn time in a zone only if its Laplace-Stieltjes transform can be obtained.  
Furthermore, many studies employ a symmetric random-walk mobility model that gives the same probability of moving to any of the neighboring zones. However, it is easy to see that these assumptions are not realistic when considering the mobility of the actual UE. Normally, the next zone  
Figure 2: Zone-based registration with three zones.  
of the UE is related to the current zone. Therefore the assumption of a symmetric random walk is not realistic and does not reflect true UE behavior. Therefore, the probability of returning to the previous zone is assumed to be \(\theta\) to reflect the dependency between the current zones and the next zone [1; 10; 11]. In a square zone environment, as shown in Figures 1 and 2, \(\theta\) would normally have a value greater than 0.25. Note that \(\theta\) = 0.25 means a symmetric random walk model.  
To illustrate the semi-Markov process model, we take into consideration the composition of the three zones for a 3Z system.
## 2 Topology and Control
### Control  
The control block diagram of the SPR is shown in Figure 6. The common control is voltage and current double closed-loop based on the average current control mode. In Figure 6, \(u_{\rm{or}}\) is the reference of \(u_{\rm{or}}\), \(i_{\rm{sr}}\) is the reference of is, \(G_{\rm{V}}\)(s) is the controller of the outer voltage loop, \(G_{\rm{I}}\)(s) is the controller of the inner current loop, and \(k_{\rm{pwm}}\) is the modulation coefficient. \(1/(sL+r)\) is the model of \(L_{\rm{s}}\) and \(R_{\rm{s}}\). \(m\) is modulating voltage. \(1/s\)C is the model of \(C\).  
The voltage outer loop is responsible for maintaining the stability of the DC voltage, while the current inner loop is responsible for precisely controlling the input current to track the given current command. \(G_{\rm{V}}\)(s) is the proportional-integral (PI) controller. \(G_{\rm{I}}\)(s) is the quasi proportional resonant (QPR) controller.
## 2 Materials and Methods  
### Plant Material and Oil Quality Index Determination  
Oleaster fruits (_Olea europaea_ L.) were harvested at full maturity in December 2021 in Ain Zitouna, which is located in Houari Boumediene city (36\({}^{\circ}\)24\({}^{\prime}\)58\({}^{\prime\prime}\) N, 7\({}^{\circ}\)17\({}^{\prime}\)10\({}^{\prime\prime}\) E), Guelma, Algeria. The plant samples were taken and deposited at the Laboratory of LBEE, University of 8 May 1945 Guelma. The species was identified by a botanical expert using a local botanical guide (_Guide illustre de la fore algerienne_). The oil extraction process followed a traditional method involving crushing, malaxation, and the application of pressure to the paste to obtain the oil. Oleaster fruits liberated from leaves were washed, crushed using millstones, and malaxed into a paste containing solid matter (core debris, epidermis, cell walls, etc.).  
In order to reproduce the conditions of traditional extraction by pressure, the malaxated paste was pressed with a mechanical press for 30 min with a decrease in pressure 5 min before the end followed by a new application of pressure to extract the maximum liquid containing the oil. This liquid was kept in a separator funnel, allowing sedimentation of solid impurities and water to recover the oil on the surface. The extracted oil was centrifuged at 3000 rpm for 10 to 15 min to purify the product [26].  
Following the analytical procedures outlined in the European Union Commission Regulations, several key parameters were calculated, including free acidity (FA), saponification value (SV), peroxide index (PI), iodine index (II), refractive index (RI), and spectrophotometric indices at 232 and 270 nm (K\({}_{232}\) and K\({}_{270}\)).
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 4 Results
### Results over Tarbes
It is important to remember that the intersection means that the detected points are considered irrigation events if they exist within the morning and the evening modes while the combination means that the points are considered irrigation events if they exist in either the morning or the evening acquisition modes. Figure 12a shows that using the morning acquisition, 91% of the irrigated plots has one or more detected irrigation events and 75% of the plots have two and more detected irrigation events. Figure 12b shows that fewer points are detected in the evening acquisition than the morning acquisition where only 50% of the plots have one and more irrigation events. In Figure 12c, the intersection shows that with an interval of 36 h between both acquisitions, only 38% of the irrigation events were commonly detected within both acquisition modes. Finally, the combined use of the morning and the evening acquisitions in Figure 12d shows that 97% of the irrigated plots had and more detected irrigation events. Moreover, 90% of the irrigated plots had two and more detected irrigation events and 65% of the plots had three and more irrigation events. This indicates that the  
Figure 11: Irrigation events detection over a reference irrigated maize plot in Tarbes, France. (**a**) Complete temporal series between March and November 2017; (**b**) irrigation events detected between 29 May and 21 August 2017.  
combined use of the morning and the evening acquisitions helps detect more irrigation events than using only one S1 overpass each 6 days. Figure 12d also shows that the maximum percentage of plots was registered with three irrigation events (29.7%) and the percentage then decreased as the number of detected irrigation events increase (four events and more). The low number of detected irrigation events in Tarbes, compared to those obtained over Catalonia, was expected since Tarbes is a humid region and the frequency of irrigation is less than that in the arid or semi-arid regions. Moreover, the abundance of rainfall events during the summer season could restrict the possibility of detecting all the possible irrigation events.
# Study on the Light Field Regulation of UVC-LED Disinfection for Cold Chain Transportation
## 3 Research Methodology
### The Preparation of the Experiments  
In order to verify the disinfection effect of the designed optical system, a comparative experiment was conducted. Before the experiment, the following conditions were prepared. All the steps were completed in the super-clean platform, and high quality, appropriate concentration of _E. coli_ was cultured in advance. Before culturing _E. coli_, all the experimental supplies (tube, beaker, pipette et al.) that would be used later were put on the super-clean platform, with UV light shining for 15 min to create a sterile environment. Then, we removed _E. coli_ (ATCC25922 standard strain) from the refrigerator, disinfected the surface of the glass tube with 75% alcohol, and opened the test tube in the super-clean platform. We took an appropriate amount of _E. coli_ using the inoculation ring, put it into the petri dish 1, and put the petri dish 1 at 37 \({}^{\circ}\)C with constant temperature incubator for 24 h. Until this step, the initial recovery of _E. coli_ was completed, and then it could be diluted appropriately as needed. For convenience of counting, the _E. coli_ was washed twice with sterile saline solution (0.9% NaCl) by centrifugation. After 24 h culturing, we used the inoculation ring to place an appropriate amount of _E. coli_ from petri dish 1 into sterile saline solution 1 (0.9% NaCl, 5 mL), and shake it well; concentration was approximately 10\({}^{6}\) CFU/mL. Then, we used a pipette to add 10 \(\mu\)L of _E. coli_ solution to sterile saline solution 2 (0.9% NaCl, 10 mL), obtaining a concentration of approximately 1191 CFU/mL. We took an appropriate amount of _E. coli_ using the pipette, put it into the Petri dishes, and put them at 37 \({}^{\circ}\)C with the constant temperature incubator for 24 h. At this point, high quality and appropriate concentration of _E. coli_ was cultured successfully.  
During the experiment, firstly, two sets of optical systems, namely Group 1 and Group 2 were constructed, as shown in Figure 3. Both groups used a UVC-LED (2 mW, 275 nm, chosen from Shenzhen Silverlight Technologies Co. Ltd. (Shenzhen, China), 3535-UVC-270-280 NM-IF40 MA-60\({}^{\circ}\)) as a light source, with an installation height of about 150 mm. Group 1 had the uniform light path using the lens group described in this paper, and the ice block (60 \(\times\) 60 mm) with a thickness of 30 mm and a flat surface was placed on the path of light, the transmittance of which was greater than 90%. At the bottom was the _E. coli_ and a piece of quartz chip, the function of which was to avoid the melted ice. In the experiment, it should be noted that it was necessary to maintain observation at all times and replace the melted ice in time. Group 2 contained only one LED light source as the control, and other installation conditions were consistent with Group 1. In addition, Group 2 also included quartz chips to imitate the attenuation effect of the lens group on the light in Group 1. The results of experiments will be introduced in Section 4.3.
## 5 Conclusions  
In this paper, we implement an accurate numerical prediction for the TVC inception of the PPTC propeller by developing and adopting the numerical prediction method for TVC inception based on bubble dynamics under the Eulerian-Lagrangian framework, and propose a set of cavitation inception discriminating criteria applicable to propellers based on bubble dynamics under the Eulerian-Lagrangian framework.  
The main conclusions are as follows:  
1. Adopting the bubble dynamics model based on the Eulerian-Lagrangian framework proposed in this paper allows us to accurately simulate propeller TVC inception and has more advantages compared with the traditional cavitation model simulation method based on the Eulerian framework.
2. The cavitation inception prediction method based on the bubble dynamics model under the Eulerian-Lagrangian framework has significant advantages compared with the traditional minimum pressure coefficient method. We can accurately predict propeller TVC inception and constrain the relative error within a range of 10% by adopting this method.
3. The pressure acting on a nucleus fluctuates sharply and briefly as the nucleus approaches a vortex core low-pressure region under the action of the tip vortex suction, and decreases rapidly when the nucleus enters the vortex core low-pressure region. Finally, under the continuous influence of the vortex core low pressure, the nucleus commences to experience explosive growth until it collapses rapidly when reaching its maximum size.  
Conceptualization, J.D.; Methodology, J.D.; Software, J.D.; Validation, J.D.; Formal analysis, J.D.; Investigation, J.D.; Resources, J.D.; Data curation, J.D.; Writing--original draft, J.D.; Writing--review & editing, R.X., L.C., R.L. and Z.Y.; Visualization, J.D., R.L. and Z.Y.; Supervision, R.X. and L.C.; Project administration, L.C., R.L. and Z.Y.; Funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Laboratory on Ship Vibration and Noise of China under Grant No. 6142204210706.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available because the data also form part of an ongoing study.  
The authors declare no conflicts of interest.
# HFD: Hierarchical Feature Detector for Stem End of Pomelo with Transformers
## 6 Discussion
### Model Architecture  
To verify the effectiveness of the CAM, we refer to HFD-Swin-base. The [Pat-Tok] \(\times\) [Pat-Tok] attention operation is removed. As shown in Table 3, the mAP of HFD-Swin-base without CAM is lower than that with CAM by 2.24%. However, HFD with CAM takes a little more time. Moreover, Table 3 shows that HFD has higher parameters than that without CAM.  
For HFD, HFFM is added for better optimization. HFFM makes the detector better at extracting multi-scale features and this non-linear fusion is very simple and efficient.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Backbone** & **Parameters (MB)** & **mAP** & **Speed (FFS)** & **Stem End** & **Black Spot** \\ \hline DETR [13] & Swin-tiny [12] & 42.94 & 75.50 & 78.91 & 83.22 & 68.58 \\ DETR [13] & Swin-small [12] & 67.35 & 78.64 & 62.53 & 87.39 & 70.76 \\ DETR [13] & Swin-base [12] & 104.56 & 81.28 & 51.87 & 90.68 & 72.82 \\ \hline Deformable & Swin-tiny [12] & 39.07 & 82.15 & 60.72 & 91.43 & 73.80 \\ DETR [14] & Swin-small [12] & 60.63 & 84.72 & 49.31 & 93.75 & 76.59 \\ Deformable & Swin-base [12] & 98.11 & 87.33 & 37.24 & 96.02 & 79.51 \\ \hline LSTM [15] & SSD [29] & **5.69** & 72.54 & 31.10 & 80.71 & 65.29 \\ \hline SAAN-GRU [16] & ResNet-50 [36] & 10.76 & 70.28 & 48.93 & 79.42 & 62.16 \\ \hline YOLO v4 [17] & CSPDarknet53 [37] & 29.46 & 85.79 & 84.65 & 94.91 & 77.58 \\ \hline YOLO v5 [18] & CSPDarknet53 [37] & 41.02 & 88.06 & **103.58** & 96.67 & 80.31 \\ \hline YOLOS [11] & DeiT-Ti [38] & 7.13 & 76.24 & 45.19 & 81.84 & 71.20 \\ YOLOS [11] & DeiT-S [38] & 31.85 & 80.83 & 36.64 & 86.17 & 76.02 \\ YOLOS [11] & DeiT-B [38] & 102.79 & 85.47 & 29.42 & 93.56 & 78.19 \\ \hline HFD & Swin-tiny [12] & 40.96 & 83.44 & 98.03 & 92.21 & 75.56 \\ HFD & Swin-small [12] & 61.17 & 86.29 & 82.86 & 95.48 & 78.04 \\ HFD & Swin-base [12] & 100.34 & **89.65** & 70.92 & **98.23** & **81.93** \\ \hline \hline \end{tabular} The best results in every category are marked in **bold**.  
\end{table}
Table 2: Comparison of HFD with other network architecture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **CAM** & **Parameters (MB)** & **mAP** & **Speed (FPS)** \\ \hline HFD-Swin-base & \(\times\) & 93.06 & 87.41 & 73.68 \\ HFD-Swin-base & \(\surd\) & 100.34 & 89.65 & 70.92 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The effectiveness of the combination attention module for HFD with Swin-base.  
With the Swin-base backbone, as shown in Table 4, the extension to HFD only adds 2.49 M parameters but mAP improves from 85.37 to 89.65. This is a significant performance gain for the better trade-off between accuracy and speed, while the runtime performance dropped by 5.28 FPS.
# Strength Analysis of Real-Life Passwords Using Markov Models
## 4 Materials and Methods
### Data Collections  
We used 12 datasets that contain real user passwords in plaintext that were leaked and are publicly available for research purposes. These password datasets represent real user passwords, which were compromised by hackers and subsequently publicly disclosed on the Internet. We used only the password information in the datasets and we removed all additional information like user names and/or email addresses included in some datasets.  
The \"RockYou\" dataset [27] contains over 14 million passwords leaked from the social application site RockYou in December 2009. The \"10 million combos\" dataset [28] contains 10 million passwords collected and cracked by [PERSON] for research purposes. The dataset also contained user names that are connected to the passwords. In order to maintain the anonymity of these informations, we removed the related user names and focused only on the released passwords. The \"PhpBB\" dataset [27] contains about 180.000 passwords cracked from MD5 hashes by [PERSON] leaked from Phpbb.com in January 2009. The \"MySpace\" dataset [27] includes 37.000 passwords that were obtained via phishing attack in October 2006. The \"faithwriters\" dataset contains 8K passwords stolen from the religious forum Faithwriters. The basic information and the sources of the rest of the datasets are presented in Table 1. For additional details, readers are referred to original works.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Size** & **Date Obtained** & **Source** \\ \hline rockyou & 14,344,390 & 12-2009 & [27] \\
10\_million\_passwords & 10,000,000 & 02-2015 & [28] \\ passwords & 2,151,220 & 12-2011 & [29] \\ uniqpass\_preview & 1,999,984 & 12-2011 & [29] \\ phpbb & 184,389 & 01-2009 & [27] \\ scribd & 106,358 & Unknown & Source unknown \\ tuscl & 38,820 & Unknown & [27] \\ myspace & 37,139 & 10-2006 & [27] \\ singles\_org & 12,233 & 10-2010 & [27] \\
10k\_most\_common & 10,000 & 06-2011 & [30] \\ hotmail & 8930 & Unknown & [27] \\ faithwriters & 8347 & 03-2009 & [27] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of datasets.
## 3 Operational Requirements
### Navigation and Mobility
#### 3.2.1 Sensors and Awareness of the Environment  
The harvesting robot can use several systems to be able to move around its environment, each method has advantages and disadvantages that deserve an independent study.  
* **Cameras and vision systems:** They capture images of the environment to detect ripe fruits, obstacles and other relevant elements in the field [38]. These systems can be monocular, stereoscopic, or even multispectral cameras, depending on specific detection needs. Just as cameras are used to recognize fruits, cameras can be used to generate an algorithm that allows the robot to move along an unobstructed route around the crop field [39].
* **Proximity sensors:** They detect the presence of nearby objects, such as plants or structures, to avoid collisions and plan safe routes [40]. If the robot deviates from its planned route, location systems allow real-time corrections to be made to maintain accuracy and efficiency during harvesting. This helps optimize the operation time and resources used in the field.
* **Location systems:** They use GPS, GNSS, or other methods to determine the robot's precise position in the field. The robot's position must be precise to ensure that it does not deviate from the path laid out for harvesting [41].  
* **Odometry sensors:** They monitor the robot's movement and direction to calculate the distance traveled and the current orientation. The information provided by the odometry sensors allows the robot to move precisely along the rows of plants and between crops, ensuring that each fruit is harvested efficiently and without damage [42]. By knowing the distance traveled, odometry sensors help optimize the use of energy and resources during robot operation, increasing autonomy and reducing downtime.
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 3 Roadside and Cloud Architecture
### Cloud Architecture
#### 3.2.1 Cloud MQTT Broker
For example, an RSU stationed at (+46.989482, +8.866957) (World Geodetic System 84) with a station ID equal to 5642 that would receive a Unaligned Packed Encoding Rules (UPER)-encoded DENM from the ITS-G5 environment could encode it as XML (currently, \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Feature** & **HTTP** & **MQTT** & **CoAP** & **AMQP** \\ \hline Encryption & ✓(TLS) & ✓(TLS) & ✓(DTLS) & ✓(TLS or SASL) \\ Authentication & ✓(TLS) & ✓(TLS) & ✓(DTLS) & ✓(TLS or SASL) \\ Reliability & ✓(TCP) & ✓(TCP) & ✗(UDP) & ✓(TCP) \\ REQ/REP pattern & ✓ & ✗ & ✓ & ✓ \\ PUB/SUB pattern & ✗ & ✓ & ✗ & ✓ \\ Embedded/IoT & ✗ & ✓ & ✓ & ✗ \\ focus & & Variable & Variable & Variable \\ \multirow{2}{*}{Header length} & \multirow{2}{*}{Variable (min. 26 bytes)} & \multirow{2}{*}{Variable (min. 2 bytes)} & \multirow{2}{*}{(min. 4 bytes)} & \multirow{2}{*}{(min. 8 bytes)} \\  & & & & \\ \end{tabular}
\end{table} 
Table 1: Comparison between several application layer protocols. the only message format used in the implemented MQTT system) and would publish it under the topic indicated in Listing 2. ```
its_center/outqueue/xml/5642/DENM/1/2/0/2/2/1/3/0/0/1/0/0
```  
Listing 2: Example MQTT topic to which an RSU with station ID 5642 stationed at (+46.989482, +8.866957) would publish a DENM. In this case, the system uses a quadtree of zoom level equal to 12. RSUs are also subscribed to various topics which the MOBICS platform can use to disseminate messages. An RSU is subscribed to the outqueue instead of the inqueue, to the station ID equal to 1 (used as the identifier for MOBICS platform), to all types of C-ITS messages, and to all quadtree zoom levels, from level 1 to a predefined level, \(z_{max}\). For example, the same RSU as in the previous example, and for \(z_{max}=14\), it would subscribe to the topics indicated in Listing 3. ```
its_center/outqueue/xml/1/+ its_center/outqueue/xml/1/+/1 its_center/outqueue/xml/1/+/1/2... its_center/outqueue/xml/1/+/1/2/0/2/2/1/3/0/0/1/0/0 itis_center/outqueue/xml/1/+/1/2/0/2/2/1/3/0/0/1/0/0/3
```  
Listing 3: Tile-based MQTT topics to which an RSU stationed at (+46.989482, +8.866957) would subscribe. The wildcard \"+\" means that the RSU subscribes to all ITS messages. The RSU also subscribes to all messages directed only to itself, as indicated in Listing 4. Following these schemas, the road manager can choose to publish messages affecting one or several regions by using a combination of topics with different quadtrees and zoom levels and can also choose to publish messages only to a specific RSU. MQTT security is implemented using TLS certificates. Additionally, the RSU implements a heartbeat mechanism that periodically publishes its current status to the MQTT broker. These heartbeat messages contain summarized information, including current CPU and memory usage, current IP address, and active C-ITS events within its coverage area.
## 5 Experiments and Results  
### Experiment Setup  
In our study, we applied Pix2 Pix and Pix2 PixHD models to a dataset of 53,000 X-ray image pairs across 35 object categories. The training consisted of 200 epochs, with a learning rate of 0.002 and a batch size of 128. We used an input image size of 256 \(\times\) 256 pixels, as well as PyTorch libraries. Our desktop setup included an Intel i9-10900X processor, 192 GB of RAM, and four NVIDIA GeForce RTX4090 24 GB GPUs for efficient training.