esa-sceva/satcom-chunk-collection huggingface dataset:
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 4 Results and Discussion
### Spatial Characteristics of Emergency Bell  
In this subsection, the spatial characteristics of emergency bell installations across Gwangju Metropolitan City were examined by districts. Table 4 shows the installation status of five districts. Specifically, 2317 emergency bells have been previously installed in the metropolitan city of Gwangju. Gwangju is divided into five districts: North, South, West, East, and Gwangsan. Precisely, the North district had the highest number of installations with 600 units, while the East district had the lowest with 272. Because of its low population, the East district had the highest number of emergency bells per 10,000 people (25.89). Meanwhile, the Gwangsan district had a high number of emergency bells (598) but the lowest density of emergency bells per 1 km\({}^{2}\) (2.68), considering the population and area characteristics. Despite a considerable budget being allocated for installation of emergency bells in the Gwangsan district, the comprehensive surveillance of the extensive area remained unattainable. Particularly, the low number of emergency bells per 1 km\({}^{2}\) in the Gwangsan district indicated that the district should prioritize installing new emergency bells.
## 4 Performance Analysis of the Generated Models in Virtual Environments
### Performance Analysis of Models
#### 4.2.2 Stale Frames  
Stale frames indicate the number of times a frame could not be delivered on time due to the parallel processing of the GPU and CPU, resulting in the reuse of the previous frame. Stale frames can cause problems when the value recorded is greater than 0 but less than the refresh rate, i.e., 72. The issues arising from this may include increased latency between user actions and their visual representation in the virtual environment or even motion sickness due to the perceived lag between these actions and their visual feedback. Table 5 shows the percentage of records where values less than 72 but greater than 0 were collected.  
Although it can be observed that even in scenes with a frame rate close to the refresh rate there are percentages close to or above 50% of stale frames, this does not necessarily signify a problem for the user experience. According to Meta's own documentation, extra latency mode is enabled by default in applications built with Unity. This mode establishes communication with the Asynchronous TimeWarp (ATW) system (a technology that helps reproject frames based on the most recent head tracking information) to always wait for an extra frame. This could explain why the fluidity of the application is not affected. On the other hand, the low percentage of stale frames in scenes with HP objects indicates better synchronisation between generating a new frame and the refresh cycle. However, the low rate of generation of these new frames (frame rate) makes it more difficult for them to become stale.
## 2 Materials and Methods
### Mathematical Transformation and Spectral Indicators Construction  
The mathematical transformations used in this study include reciprocal (\(\mathbf{1}/\mathbf{R}\)), logarithm \(\mathbf{InR}\), exponential \(\mathbf{e}^{\mathbf{R}}\), square root \(\mathbf{R^{0.5}}\), first derivative \(\mathbf{R^{{}^{\prime}}}\), and second derivative \(\mathbf{R^{{}^{\prime\prime}}}\). The following formulas were used:  
Reciprocal of spectral reflectance: \(\mathbf{RecI\_R}=\mathbf{1}/\mathbf{R}\);  
Logarithm: \(\mathbf{LOGE\_R}=\mathbf{InR}\);  
Exponent: \(\mathbf{EXP\_R}=\mathbf{e}^{\mathbf{R}}\);  
Square root: \(\mathbf{SQRT\_R}=\mathbf{R^{0.5}}\).  
The differences between the adjacent elements was calculated, which is similar to performing a first-order difference on discrete data mathematically. The first-order difference of the discrete data points \(\mathbf{R_{1}},\mathbf{R_{2}},\mathbf{R_{3}}\ldots\mathbf{R_{n}}\) is represented as follows:  
\[\mathbf{DIFF1R_{i}}=\mathbf{R}_{i}-\mathbf{R}_{i-1}\]  
The second-order difference is obtained by performing another difference on the first-order difference, represented as follows:  
\[\mathbf{DIFF2R_{i}}=\mathbf{R}_{i}^{{}^{\prime}}-\mathbf{R}_{i-1}^{{}^{\prime}}\]  
Since a single band contains limited spectral information, after extracting the spectral reflectance from the remote sensing images, operations can be performed between different bands. These indicators are derived from prior calculations in existing research (see Table 4). A total of 29 common spectral indicators were selected.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Spectral Indicators** & **Formula** & **References** \\ \hline
**SI-T** [_Salinity Index_] & \(\mathrm{R}/\mathrm{NIR}*100\) & [21] \\
**NDSI** [_Normalized Difference Salinity Index_] & \(\mathrm{R}-\mathrm{NIR}/\mathrm{R}+\mathrm{NIR}\) & [22] \\
**SII** [_Salinity Index_1] & \(\sqrt{\mathrm{R}\cdot\mathrm{G}}\) & [22] \\
**SI2** [_Salinity Index_1] & \(\sqrt{\mathrm{G}^{2}+\mathrm{R}^{2}+\mathrm{NIR}^{2}}\) & [22] \\
**SI3** [_Salinity Index_1] & \(\sqrt{\mathrm{G}^{2}+\mathrm{R}^{2}}\) & [22] \\
**S1** [_Salinity Index_] & \(\frac{\mathrm{B}}{\mathrm{R}}\) & [23] \\
**S2** [_Salinity Index_] & \(\frac{\mathrm{B}-\mathrm{R}}{\mathrm{B}^{+}\mathrm{R}}\) & [23] \\
**S3** [_Salinity Index_] & \(\frac{\mathrm{G}-\mathrm{R}}{\mathrm{B}}\) & [23] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spectral indicators in this study B, G, R, NIR, SWIR1, and SWIR2 separately means band blue, green, red, near infrared, short wave infrared 1, and short wave infrared 2.
## 4 Experimental Results
### Ablation Studies
In contrast to the movement strategy in BAR [49], we provided more options for root strategies. Compared to TSP-PRL [25], our method adjusted candidate windows at the finer leaf level. Table 8 also shows that our method could achieve more accurate results within the fewest steps. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position** & **Number** & **IoU = 0.3** & **IoU = 0.5** & **IoU = 0.7** & **Runtime** \\ \hline \(\left\lfloor 1/4,3/4\right\rfloor=(1)\) & K = 1 & 64.97 & 48.11 & 22.88 & **1.2 s** \\ \(\left\lfloor 1/3,2/3\right\rfloor+(1)=(2)\) & K = 3 & 65.25 & 51.17 & 23.25 & 1.5 s \\ \(\left\lfloor 1/5,2/5\right\rfloor+(1)=(3)\) & K = 3 & 65.52 & 51.29 & 23.27 & 1.5 s \\ \(\left\lfloor 2/6,3/6\right\rfloor+(1)=(4)\) & K = 3 & 65.38 & 51.26 & 23.23 & 1.5 s \\ \(\left(2\right)+(3)\) & K = 5 & 65.97 & 51.31 & 23.36 & 1.7 s \\ \(\left(2\right)+(4)\) & K = 5 & **66.14** & **51.41** & **23.42** & 1.7 s \\ \(\left(3\right)+(4)\) & K = 5 & 66.10 & 51.38 & 23.40 & 1.7 s \\ \hline \hline \end{tabular} Bold mark represent the optimal performance across various methods evaluated under same metric.  
\end{table} 
Table 6: Ablation studies of candidate windows on the Charades-STA dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**\(thr\)** & \multicolumn{2}{c}{**R@1**} & \multirow{2}{*}{**Steps**} \\ \cline{2-2} \cline{4-4}
**\(thr\)** & & **IoU = 0.3** & **IoU = 0.5** & **IoU = 0.7** \\ \hline
0.9 & 62.16 & 49.60 & 22.35 & 13.5 \\
0.85 & 64.89 & 50.61 & 23.01 & 12.3 \\
0.8 & **66.14** & **51.41** & **23.42** & **11.7** \\
0.75 & 64.51 & 50.51 & 22.89 & 12.3 \\
0.7 & 62.25 & 47.03 & 21.86 & 12.9 \\ \hline \hline \end{tabular} Bold mark represent the optimal performance across various methods evaluated under same metric.  
\end{table} 
Table 7: Ablation studies on the threshold \(thr\) in Equation (13) on the Charades-STA dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Length of Features** & **IoU = 0.3** & **IoU = 0.5** & **IoU = 0.7** \\ \hline Query = 15 & 64.93 & 49.91 & 22.78 \\ Query = 20 & **66.14** & **51.41** & **23.42** \\ Query = 25 & 65.57 & 50.62 & 23.05 \\ Video = 50 & 64.87 & 50.21 & 22.65 \\ Video = 100 & **66.14** & **51.41** & **23.42** \\ Video = 120 & - & - & - \\ \hline \hline  
**Influence of hyper-parameters \(\mathbf{\lambda_{1}}\) and \(\mathbf{\lambda_{2}}\) in Equation (10):** Table 9 discusses the influence sensitivity of hyper-parameters \(\lambda_{1}\) and \(\lambda_{2}\) in the loss function Equation (10). **Discussion on maximum number of steps in RL:** In the RL process, the agent stops moving the window when it reaches the maximum number of steps. Therefore, it is essential to investigate the influence of setting different maximum numbers. In Figure 3(b), we observe the curves for R@1 [EMAIL_ADDRESS] and R@1 [EMAIL_ADDRESS] under different settings of the maximum number of steps. The results indicate that a maximum number larger than 12 yields satisfactory performance. To strike a balance between training cost and accuracy, we chose 12 as the maximum number of steps in our experiments.
## 4 Performance Evaluation
### (Simulation Results) SINR and Throughput of the D2D Links  
As described above, in FFR-3, two frequency sections are allocated for a D2D link in one zone. The two allocated frequencies are also symmetric in terms of geographical distribution, so they have similar SINR distributions. In Figure 16, FFR-3_1 and FFR-3_2 represent SINR distributions for the two frequency sections used by D2D links, respectively, and show similar distributions. Both frequency sections allocated for the D2D links in FFR-3 show superior SINR over the semi-static scheme.  
Figures 17 and 18 show the average throughput of the D2D links according to the transmission (Tx) power of the D2D links when the cellular link Tx power is 30 dBm and 40 dBm, respectively. As shown in the figure, the throughput of the D2D link in the semi-static scheme is hardly affected by the transmission power of the cellular link and other D2D links. This is because the target D2D link uses orthogonal resources with respect to the cellular link and the transmission power of all the D2D links is assumed to be the same. On the other hand, in FFR-3, the throughput of the D2D link is affected by the transmission power of the cellular link and the surrounding D2D link. This is because D2D links reuse the same radio frequency used by cellular links and D2D links in the adjacent zones. Thus, if the transmission power of the D2D link becomes larger than that of the cellular link, the throughput of the D2D link rapidly increases in FFR-3.  
Figure 16: CDF of sensor SINR in D2D link (D2D Tx power = 30 dBm).  
Figure 17: D2D link throughput versus D2D transmit power (cellular Tx power = 30 dBm).
# Automatic Brix Measurement for Watermelon Breeding
## 2 Materials and Methods
### Automatic Brix Measurement System  
The automatic Brix measurement system includes a laser rangefinder, 2 Red-Green-Blue (RGB) cameras, a refractometer, a button-pushing servo motor, a 3-axis robotic arm, and a cutting device. As shown in Figure 1, the working procedures of this system are as follows: (i) the diameter of the watermelon is acquired using the laser rangefinder; (ii) the watermelon is cut into precise halves, then the upper half is removed by a sucker; (iii) the position of the testing point is calculated after a set of image analysis processes and the refractometer is driven into the testing point; (iv) the START button on the refractometer is pushed using a servo motor, then the digital tube is recognized using the image analysis method.  
The laser rangefinder, produced by the Ruxing company in China, has a measurement capacity of 0.05-80 m, an accuracy of 1.0 mm, and a laser band between 620-690 nm. The RGB cameras, manufactured by the Weixinshijie company in China, have a resolution of 1920 \(\times\) 1080. The refractometer is PEN-PRO, which is manufactured by the Atago company from Japan, and has a measurement range of Brix 0.0 to 85.0%, an accuracy of \(\pm\)0.2%, and a temperature compensation range of 10 to 100 \({}^{\circ}\)C. The 3-axis robotic arm is constructed with linear stage actuator, where the refractometer is mounted on its end. The cutting device consists of a driven cylinder, an air compressor, two slideways, a blade-supporting shelf,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Weight/kg** & \begin{tabular}{c} **Vertical** \\ **Diameter/mm** \\ \end{tabular} & \
\begin{tabular}{c} **Diameter** \\ **Along 45\({}^{\circ}\)/mm** \\ \end{tabular} &
\begin{tabular}{c} **Horizontal** \\ **Diameter/mm** \\ \end{tabular} \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: The automatic watermelon Brix measurement system and the camera coordinate system.  
a blade driven by the cylinder, and a blade position adjustment module driven by a step motor linear actuator.
# Methods for Sampling Biogas and Biomethane on Adsorbent Tubes after Collection in Gas Bags
## 3 Biogas and Biomethane Samples  
In total, 14 samples of biogas and six samples of biomethane were collected in Altef gas bags. The samples were real samples collected from different biogas plants and covering different substrates including waste water treatment sludge, food wastes and industry wastes. The biomethane used in this study samples originated from biogas upgraded either using a water scrubber or an amine scrubber. From the bags, 100 mL of gas was drawn on to Tenax TA tubes using each of the three methods (three tubes per method) at the same time (within a 30 min period). The total storage time was in all cases less than 1 day. Between 5 to 15 VOCs were quantified for each sample; 130 VOCs in total. The VOC concentrations varied from 30 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) to 35,000 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) as shown on Figure 4. The VOCs belong to several different families: linear alkanes (heptane, octane, decane, undecane, dodecane, tridecane and tetradecane), aromatic hydrocarbons (toluene and o-xylene), terpen (\(\alpha\)-pinene, \(\beta\)-pinene, camphene, 3-carene, D-limonene and p-cymene), sulphur compounds (dimethyl sulphide, dimethyl disulphide, thiophene, 2-methylthiophene, tetrahydrothiophene and 1-propanethiol), alcohols (1-propanol and 2-butanol), ketones (2-butanone, 3-pentanone and 2-pentanone), esters  
Figure 3: Sampling method 3—Sampling pump method.  
Figure 2: Sampling method 2—Easy volatile organic compound (VOC) method.  
(ethyl propanoate, ethyl butanoate and ethyl pentanoate), furrans (2-methylfuran and ethyl-furan) and other alkanes (decahydronaphthalene and methyldecahydronaphthalene). Boiling points vary from 38 \({}^{\circ}\)C (dimethyl sulphide) and 234 \({}^{\circ}\)C (tetradcane).
# PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration
## 1 Introduction
[22] proposed an end-to-end deep learning architecture for 3D point cloud registration and compared the results of SVD and neural networks based on regression problems, with experiments showing the latter to be faster and more accurate. These end-to-end registration methods have the advantage of being designed and optimized specifically for point cloud registration using neural networks, while retaining the advantages of both traditional mathematical theory and deep neural networks. However, the distance measurement between point clouds is measured in Euclidean space, with its transformation parameter estimation regarded as a \"black box\" with no traceability. In addition, these methods often ignore local information in the point clouds. Although end-to-end point cloud registration using deep learning has become a primary research topic, effectively extracting point cloud features conducive to registration and carrying out robust, efficient, and accurate registration remain issues to be studied. To overcome these problems, we propose an end-to-end registration model called Point Transformer for Registration Network (PTRNet) that takes both local and global features into account. Using a KNN topology, this method encodes point clouds locally and uses an attention mechanism to distinguish the importance of the point cloud features. Doing so extracts more powerful global information. Our experimental results show thatPTRNet offers good robustness and high registration accuracy.
# Intelligent Air Cutting Monitoring System for Milling Process
## 5 Results and Discussion
### Verification
#### 5.3.2 Experiment Verification 2  
The cutting path of experiment 2 is the same as experiment 1, but the cutting length is 100 mm and 61 mm, respectively. The experiment is repeated five times, with a constant feed rate of 1200 mm/min. The cutting parameters are shown in Table 4. The results of the experiment are shown in Figure 10. It is seen that the average cutting time for 100 mm length is 5.087 s. The theoretical calculation using equation Tm = L \(\times\) 60/F, where Tm is machining time (s), L is cutting length (mm), F is Feed rate (mm/min) for cutting length 100 mm, and feed rate 1200 mm/min, is 5 s, and there is about 0.1 s deviation with the monitoring system. However, the overall monitoring data are still very stable. When the cutting length is 61 mm, the monitoring system shows that the average cutting time is about 3.155 s, which is similar to the theoretical cutting time of 3.05 s. The cutting time difference for two cutting lengths (100 mm and 61 mm) is about 1.932 s. The theoretical calculation of air cutting time for this cutting length difference (39 mm) is 1.95 s, which has a deviation of 0.02 from the monitoring system. The results indicate that the air cutting monitoring system based on spindle load current is stable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Experiment 1** & **Experiment 2** \\ \hline Spindle speed (rpm) & 5000 & 5000 \\ Feedrate (mm/min) & 750–1750 & 1200 \\ Width of Cut (mm) & 0.3 & 0.5 \\ Depth of Cut (mm) & 3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cutting parameters for experiments 1 and 2.  
Figure 9: Results of verification experiment 1.
# Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques
## 2 Proposed Method
### Pupil-Tracking Algorithm
The aligner needs to accurately handle eye shapes under significant motion, making the large motion DB more suitable for this purpose. The specialized event camera image DB played a crucial role in training both the eye-nose region detector and aligner. By incorporating distinct motion categories, we ensured that the algorithms could effectively adapt to different motion levels and diverse eye shapes encountered in real-world scenarios. As a result, while the performance may not be considered superior, our fine-tuned detector and aligner have shown great potential in leveraging event camera imaging for pupil tracking. Indeed, one of the most notable achievements of our event-camera-based pupil-tracking method is its ability to capture rapid eye movements that are challenging for traditional RGB-frame-based systems. The event camera's asynchronous operation and high temporal resolution allow it to detect and respond instantly to pixel-level intensity changes triggered by motion events. This unique sensing mechanism enables us to accurately track fast eye movements, which are often difficult for conventional frame-based cameras to capture.
## 4 Results and Discussion
### CTAM Performances under Different Compaction Methods  
After the determination of the gyration compaction number N\({}_{\text{design}}\), the feasibility of using SGC to design CTAM was evaluated by comparing the performance of CTAM under Proctor, vibration and gyration compactions, including aggregate degradation, maximum dry density, optimal moisture content, unconfined compressive strength and dry/temperature shrinkage coefficient. The Proctor and vibration compaction tests were conducted according to the specification of JTG E51-2009 [14] and reference [15], respectively.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation
### Ratio of Meaningful Bytes to Total Bytes in a Packet (\(\beta\))  
Since many GSTs are expected to be deployed in various parts of the world, it is imperative to identify the data source as the origin country and the location of the GST. As a result, a data packet format has been standardized for all GSTs, which includes GST ID, data type, time of data collection and sensor data. Two bytes are assigned for GST ID, and the first byte is for country identification. The second byte is used when there is more than one GST in a country, and it would be identified by using the second byte. The data type specifies what kind of data and how many kinds of data are uplinked in the packet. Four bytes are assigned for timestamp, which is the time at which the sensor data was recorded, and it should include seconds, minutes, hour, day, month, and year. Each country can choose whether they want to send data with either short or long data packet format. The optimal data packet length is set to 32 bytes since longer data transmission time consumes more power, and the data packets can have errors due to doppler shift rate and unstable attitude of the satellite. A checksum byte is assigned at the end of the packet to ensure the satellite received the correct packet. The packet formats for GSTs are shown in Figure 16.  
The ratio of meaningful bytes to the total number of bytes can be deduced from Equation (5) that considers only the bytes that contain the time and sensor data.  
\[\beta=\left\{\begin{array}{ll}0.75,&\text{for short data packet format}\\ 0.88,&\text{for long data packet format}\end{array}\right. \tag{5}\]
# 5G-AKA-FS: A 5G Authentication and Key Agreement Protocol for Forward Secrecy
## 2 Related Works
Upon a receipt of the new SUCI, the HN decrypts it into the corresponding SUPI, counts on its timestamp \(T_{HN}\) to validate the received \(T_{SN}\), and authenticates the UE. In such a way, the authentication process is optimized to have one round trip, reducing the computation and communication overhead. Also, the HN utilizes timestamps to prevent the SUCI replay and Denial-of-Service (DoS) attacks on itself while enhancing the security of the session keys by deriving \(K_{AUSF}\) from \(sk_{HN}\), \(k\), \(RAND_{SN}\), and \(T_{SN}\). However, it should be noted that when generating \(K_{AUSF}\), both \(k\) and \(sk_{HN}\) are utilized. Therefore, if \(k\) and \(sk_{HN}\) are leaked, an attacker can recover \(K_{AUSF}\) and conduct subsequent malicious attacks. Furthermore, to address SUCI replay attacks, 5 GAKA-LCCO introduces freshness to SUCI by utilizing \(T_{SN}\). However, this approach requires time synchronization, which may pose challenges in situations such as roaming. Consequently, the use of \(T_{SN}\) is not desirable for mobile telecommunication scenarios. Furthermore, 5 GAKA-LCCO exhibits an unconventional protocol flow compared to the 5G-AKA standard, and the differences in the _Authenticate SIM command_ can lead to compatibility issues, particularly with Legacy Universal Subscriber Identity Modules (USIMs), potentially resulting in backward compatibility problems. **5G-AKA** \({}^{\prime}\), introduced by [16], focuses on addressing linkability attacks by reusing the ECIES secret \(k_{HN}\), which is used to protect SUPI in the initial step. In this protocol, the HN encrypts its randomly generated number \(RAND\) into \(RAND^{\prime}\) with \(k_{HN}\), then sending to the UE the encrypted result instead of \(RAND\) along with the authentication token AUTN. At this point, it is worth noting that since the UE trusts the freshness of \(k_{HN}\), it also trusts the freshness of \(RAND^{\prime}\). Therefore, if successfully decrypting \(RAND^{\prime}\) with \(k_{HN}\), the UE can trust the freshness of its received AUTN, thereby detecting the message replay attack prior to arriving at the Sync_Failure while defending against the linkability attack. In spite of such a successful defense against the linkability attack, this protocol is vulnerable to active attacks by malicious SNs because it allows the HN to send \(K_{SEAF}\) to the SN without authentication to the UE. More importantly, 5G-AKA\({}^{\prime}\) fails to achieve forward secrecy because the old anchor keys can be recovered if the long-term key \(k\) shared between the UE and the HN is leaked. **3. Preliminaries**  
_3.1. Notations_  
Table 1 shows abbreviations and notations to be used throughout this paper.