esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PD Sensor Label** & **Pulses** & **Temperature Groups** & **Mean Difference** & \(p\) \\ \hline \multirow{4}{*}{ISO-002-ADW/UHF-1} & \multirow{4}{*}{UHF} & Low vs. Moderate & \(-\)18.60 \({}^{*}\) & \(<\) 0.0005 \\  & & Low vs. High & \(-\)79.08 \({}^{*}\) & \(<\) 0.0005 \\  & & Moderate vs. High & \(-\)60.49 \({}^{*}\) & \(<\) 0.0005 \\ \cline{2-5}  & \multirow{4}{*}{AE} & Low vs. Moderate & \(-\)4.95 \({}^{*}\) & \(<\) 0.0005 \\  & & Low vs. High & \(-\)22.10 \({}^{*}\) & \(<\) 0.0005 \\  & & Moderate vs. High & \(-\)17.15 \({}^{*}\) & \(<\) 0.0005 \\ \hline \multirow{4}{*}{ISO-002-ADW/UHF-2} & \multirow{4}{*}{UHF} & Low vs. Moderate & \(-\)0.74 \({}^{*}\) & \(<\) 0.0005 \\  & & Low vs. High & \(-\)1.00 \({}^{*}\) & \(<\) 0.0005 \\  & & Moderate vs. High & \(-\)0.26 & 0.154 \\ \cline{2-5}  & \multirow{4}{*}{AE} & Low vs. Moderate & \(-\)386.96 \({}^{*}\) & \(<\) 0.0005 \\  & & Low vs. High & \(-\)534.64 \({}^{*}\) & \(<\) 0.0005 \\  & & Moderate vs. High & \(-\)147.68 \({}^{*}\) & \(<\) 0.0005 \\ \hline \multirow{4}{*}{ISO-002-HF} & \multirow{4}{*}{HFCT} & Low vs. Moderate & \(-\)39.46 \({}^{*}\) & \(<\) 0.0005 \\  & & Low vs. High & \(-\)25.98 \({}^{*}\) & \(<\) 0.0005 \\ \cline{1-1}  & & Moderate vs. High & 13.48 \({}^{*}\) & \(<\) 0.0005 \\ \hline \hline \end{tabular} \({}^{*}\) The mean difference is significant at the 0.05 level.  
\end{table} 
Table 7: Results of multiple comparisons of mean PD pulses within groups using post hoc Games–Howell test. Transform) algorithm in the FPGA for the acoustic emission channel, which will create the ability to identify the type of PDs based on selected frequency domain features and artificial neural network (ANN) based classification. In the case of the hardware layer, research works are underway to adapt the system to diagnose power equipment other than transformers. The first successful implementations of the prototype monitoring system on 110 kV gas-insulated switchgear are described in [117]. Conceptualization, W.S., K.W. and W.G. ; methodology, W.S, K.W. and W.G. ; software, W.G., W.S. and K.W. ; validation, W.S., W.G. and K.W. ; formal analysis, W.S. ; investigation, W.S, W.G., C.S., K.W. ; resources, W.S and W.G; data curation, W.S. ; writing--original draft preparation, W.S. ; writing--review and editing, W.S., K.W., W.G. and C.S. ; visualization, W.S. ; supervision, W.S. ; project administration, W.S. and W.G. ; funding acquisition, W.S., W.G and K.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the Polish National Centre for Research and Development, within the Applied Research Programme, grant No. PBS3/A4/12/2015. The authors declare no conflict of interest.
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
## 2 Materials And Methods
### Evaluation: Point Pattern Analysis  
We analyze the dispersion of surface points covering the land registry area using point pattern analysis to measure the quality of the land registry data collection step. We use a well-known technique, the Quadrats method [27] in our study, which divides a region into equal area sub-regions and counts the number of points in each sub-region to measure the distribution of spatial patterns. As a result, the pattern can be asserted as clustered, random, or regular [28; 29; 30] depending on the frequency distribution of the counts compared with the theoretical distribution of spatial randomness [31].  
We initially insert regularly distributed synthetic points on each collected land registry area. Our motivation is to understand the adequacy of land registry data via checking the distribution of synthetic points in the area covered by land registry data. If there is a regular distribution of points over the land registry data, it can be concluded as adequate.  
An example of surface points can be seen in Figure 6a where yellow shapes represent the land registry parcels, and the dots represent the surface points over parcels. The points are inserted with 0.001 latitude and longitude increments.  
To determine the optimal grid size, we tried different grid sizes ranging from 2 \(\times\) 2 to 14 \(\times\) 14 for the Quadrats method. After that, we calculated the number of regular regions with respect to the total number of regions to select the best grid size for our study. Using a method similar to the elbow method in cluster analysis, we selected the grid size (9 \(\times\) 9) as the regularity reaches a certain level afterward. Figure 7 depicts the percentage of regular places with respect to the grid size.  
Figure 6: **(a)** An example of regularly inserted points on each collected land registry data inside a fishnet. **(b)** Another example of a fishnet depicting quadrants, counts, surface points, and parcels. Small ovals and blue shapes represent the surface points and the intersecting parcels within the fishnet, respectively. The numbers indicate the counts.  
As a result, we applied the Quadrats method with 9 \(\times\) 9 sub-regions. Figure 6b demonstrates a sample of quadrants and intersected land registry parcels along with the surface points and quadrant counts.  
Depending on the quadrant counts, we applied the Quadrat test. As a data preprocessing step, which is detailed in Section 2.3, we only selected the fishnets having a regular surface point pattern with a significance threshold of \(p\)-value \(\leq\) 0.05.
# Research on the Performance of an Aircraft Landing
## 3 Results of Dynamic and Static Analyses  
### Test Results  
In order to examine the locking performance of hook lock, retracting and locking performance tests were carried out on the landing gear. Figure 4 illustrates the pressure results from the test, where the solid black curve represents the pressure in the rodless side chamber of retraction actuator, and the gray dashed line represents the unlocking pressure of hook lock. During retraction of the landing gear, hydraulic pressure is supplied to the rodless side chamber of retraction actuator. As the pressure rises, the landing gear is retracted slowly. When the pressure reaches the peak, it indicates that the lock roller hits the lock hook to complete the locking action. This peak value is precisely the critical impact force at which the hook lock can complete the locking action. By measuring and calculation based on the test pressure curve, the locking moment can be obtained that \(M=1346.8\) N - mm. During extension of the landing gear, the hook lock is unlocked initially to release the landing gear. At this point, the hydraulic system supplies pressure to the unlocking actuator, and the peak pressure site is precisely the critical driving force that enables unlocking of the hook lock. Calculation based on the measured pressure curve \(F_{Unlock}\)= 82.4 N.
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 3 Methodology
### Spatial-Temporal-Semantic Feature Extraction
#### 3.1.1 Temporal Feature Matching  
To transform continuous temporal information into discrete time, a proper time interval should be selected. Positioning devices such as the GPS module inside the mobile phone collect track points at a fixed sampling rate, usually one every second. However, due to the cost of network transmission and storage problems, points collected are not completely uploaded to the server. For example, the locations of couriers are sent to the server every five seconds and it changes to 30 s in the applications of taxi or bus. The precision of location prediction is affected by the sampling rate. If the time interval between each location is five minutes, the model will predict the next location five minutes from the current one. The time interval is determined by the demand of specific services. Generally, the time interval should be larger than the average sample rate.  
Trajectories are usually stored and segmented by natural day. However, the record of tracks may be interrupted because of the GPS blocking problem. First, each trajectory is divided into segments if the time interval between two track points is over 30 min. Intervals under 30 min can be regarded as blocking, which does not affect the continuity of trajectory. After segmentation, track points are allocated to _timebins_. Time of a day is divided into multiple time bins by the size the time bin. For example, if the size of time bin is 15 min, there is four time bins in an hour and 144 time bins in a day, identified from 0 to 143. Next, time of each track points is mapped to a time bin by Equation (2), where \(t_{i}\) is the time of \(p_{i}\). \(t_{zero}\) is the zero time of a day and \(Round()\) is the integral function:  
\[timebin1D=Round((t_{i}-t_{zero})/Size\,of\,timebin) \tag{2}\]  
The temporal mapping process is illustrated in Figure 6. After mapping, there might be several points in the same time bin. A representative point is selected by calculating the linear center of the points with the average longitude and latitude.  
Figure 5: Flowchart of the STS-LSTM.  
Trajectory \(T=\{p_{1},p_{2},\ldots|p_{i}=(lon_{i},lat_{i},t_{i})\}\) is transformed into \(T_{temporal}=\{p^{\prime}_{1},p^{\prime}_{2},\ldots|p^{\prime}_{i}=(lon^{\prime }_{i^{\prime}},lat^{\prime}_{i^{\prime}},timbeinID_{i})\}\), where \(p^{\prime}_{i}\) is the representative point in each time bin. The sequence of representative points is sorted in ascending order of the time bin ID. After time matching, temporal information is fixed and discretized.
## 2 Thinning Methods
### Chemical Thinning
However, it was found that cytokinin sprays had no effect on fruit size [61]. Other substances tested include molasses, California liquid, potassium soap, Tergitol, 2-4% vegetable oil emulsion, or copper [15,17,20]. ATS is a popular agent used to thin apple and peach flowers. Its disadvantage is that it may have a phytotoxic effect on leaves, which in turn may reduce photosynthesis and reduce fruit size [18]. It has been reported that the use of 2% ATS does not cause visible signs of leaf damage [20]. However, at higher concentrations (3%), ATS was found to cause visible phytotoxicity on leaves, and at lower concentrations (1 and 2%), it did not reduce fruit set [6]. To reduce fruit set without damaging the leaves ATS can be applied twice at a concentration of 2%, as shown in the study on 'Bing' sweet cherries grown on 'Gisela 5' rootstock, which yielded less fruit of better quality after the treatment [20]. The timing of application is important--the best result was obtained by using ATS for the first time at 10% full bloom and again at 90% full bloom. In other studies, ATS effectively reduced cherry yield but did not improve fruit weight, fruit size, or content of titratable acids [18]. The phytotoxicity of chemical thinners can limit leaf carbon dioxide exchange. Young leaves were found to be more susceptible to leaf blade margin drying than older ones, but the leaves regenerated after the treatment [20,21]. The use of vegetable oil reduced mostly the leaf chlorophyll content (11%) for 23 days and neither showed a thinning effect nor improved fruit quality. However, in another experiment, ATS, Californian liquid, and tergitol reduced fruit set, improved fruit quality, and size and allowed achieving a higher yield value. The use of Californian liquid resulted in a higher fruit diameter (24.2 mm\(\longrightarrow\)23.2% increase), whereas a maximum of 21.4 mm was achieved after tergitol and ATS treatments. Tergitol, ATS, and (lime sulphur) FOLS similarly increased total soluble solids and firmness [20]. The use of the gibberellin inhibitor, uniconazole, at 30% full bloom reduced the fruit set of 'Bing' sweet cherries. This shortened the time spent on manual thinning by about 50% and the crop load by \(\sim\)40%, and there was also a considerable shift to larger a fruit size (>26 mm), provided that the crop load was not too low. No negative effects on flowering and yield were observed in the following year [30]. Both mechanical and chemical treatments showed a strong thinning effect as well as an increase in fruit size (Table 2), which, however, could only partially make up for the yield losses caused by thinning [15; 16]. Another chemical thinner used for pome species is 2-chlorophosphonic acid (ethorphon), but tests on cherries did not give positive results. Substances tested in recent years include the photosynthesis inhibitor metamitron (Brevis) and two naturally occurring compounds, abscisic acid (ABA) and 1-aminocyclopropane-1-carboxylic acid (ACC). However, the research is currently at an early stage and it is now hard to predict how effective they will be [79].
## 3 Roasting Process
### Hot Air Oven  
The common method for roasting almonds involves using hot air roasting at temperatures ranging from 129.5 \({}^{\circ}\)C to 154.5 \({}^{\circ}\)C for 30 to 10 min, respectively, to achieve a light to medium roast [37]. During the roasting process, heat is transferred to the food through two primary mechanisms: convection, involving the transfer of heat through the movement of hot air, and radiation, involving the transfer of heat through electromagnetic waves emitted by the oven walls [38]. [PERSON] et al. (2024) reported different temperature ranges: low temperature (120-140 \({}^{\circ}\)C), moderate temperature (140-160 \({}^{\circ}\)C), and high temperature (160-180 \({}^{\circ}\)C) [28]. For example, hazelnuts can be roasted at 140 \({}^{\circ}\)C for 17 min or at 170 \({}^{\circ}\)C for 10 min [39], while almonds are roasted for 5 to 60 min at temperatures between 100 \({}^{\circ}\)C and 180 \({}^{\circ}\)C [40]. [PERSON] et al. (2011) evaluated the impact of different roasting methods on pistachio kernels, varying temperatures from 90 to 190 \({}^{\circ}\)C and times from 5 to 65 min. They assessed various parameters, including color, hardness, taste, and moisture content, to optimize pistachio paste production. The study revealed a decrease in kernel hardness from 91 N for pistachios roasted at 70 \({}^{\circ}\)C for 35 min to 26 N for those roasted at 190 \({}^{\circ}\)C for 65 min. Concurrently, an increase in brown pigments was observed, consistent with caramelization reactions, as temperature and time increased. Similarly, the moisture content of the kernels decreased with increased roasting time and temperature. Based on these findings, the researchers concluded that roasting at 134 \({}^{\circ}\)C for 35 min in a conventional oven represents the optimal temperature and time settings for pistachio paste production [31]. Hot air roasting, hot pan roasting, and classic industrial oven roasting are basic methods for roasting nuts. These methods are time consuming, energy intensive, and have a low production efficiency. Additionally, classical hot air roasting systems can over-roast the outer surfaces of nuts, while leaving the centers under-roasted [26]. To ensure uniform heating and heat distribution in food samples, new technologies have been developed. One such technology is the hot air circulation oven, which circulates hot air at different velocities ranging from 1 to 3 m/s. In this study, cashew nuts were roasted for 20 min at three different temperatures: 110, 130, and 150 \({}^{\circ}\)C, using a hot air circulation oven. The roasted nuts were then analyzed and tasted by a sensory panel. The results indicated that this innovative roasting process can significantly impact the quality and flavor of cashew nuts. Optimal results were obtained at 110 and 130 \({}^{\circ}\)C, comparable to those achieved with traditional oven roasting at 180 \({}^{\circ}\)C for 30 min. E-nose and GC-MS analyses confirmed that this innovative technology can trigger the formation of traditional
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 6 Multi-Operator Game for Channel Selection
### Player Utilities  
Each player, i.e., network operator, will seek to greedily maximize the sum of its own normalized throughput that was obtained by the Aloha medium access scheme. The utility per network operator can be expressed as:  
\[U_{i}^{c}=\sum_{c=1}^{\mathcal{C}}\sum_{s=1}^{S}x_{c}^{i}\log(G_{sc}^{i}\exp(-2 G_{sc})) \tag{9}\]  
where \(\exp(-2G_{sc})\) represents the success ratio per spreading factor and per channel in the entire system (and it coincides with the success ratio of any network \(i\)), and \(G_{sc}^{i}\) represents the traffic load of network \(i\). Again, each player of the game is seeking, through channel selection, to maximize its own normalized throughput.
# Torque Requirements and the Influence of Pilot Holes on Orthodontic Miniscrew Microdamage
## 3 Results
### Microdamage and Bone Thickness  
Summary statistics were calculated for bone damage characteristics according to both pilot hole status and bone thickness (Table 2). There was strong evidence for an interaction between pilot hole status and bone thickness for total damage area (\(p\) = 0.0064) and diffuse damage area (\(p\) = 0.0003). There was no evidence that bone thickness had a significant effect on the maximum damage radius, maximum diffuse damage radius or maximum crack length.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & \multicolumn{2}{c}{**No Pilot Hole**} & \multicolumn{2}{c}{**Pilot Hole**} \\  & **(\(n\) = 15)** & **(\(n\) = 30)** & & \\ \hline
**OMI INSERTION** & & & & \\ Insertion time (s) & 28.77 \(\pm\) 2.07 & 17.43 \(\pm\) 2.80 & & \\ Average insertion force (N) & 22.08 \(\pm\) 2.25 & 17.02 \(\pm\) 4.74 & & \\ Max insertion torque (Ncm) & 19.67 \(\pm\) 3.86 & 21.38 \(\pm\) 4.19 & & \\
**DAMAGE** & & & & Difference & \(p\)-value \\
**CHARACTERISTICS** & & & & \\ Total damage area (mm\({}^{2}\)) & 4.77 \(\pm\) 1.80 & 5.60 \(\pm\) 1.32 & 0.83 & 0.085 \\ Diffuse damage area (mm\({}^{2}\)) & 3.40 \(\pm\) 1.26 & 3.34 \(\pm\) 1.04 & \(-\)0.05 & 0.879 \\ Max damage radius (mm) & 1.96 \(\pm\) 0.49 & 1.82 \(\pm\) 0.34 & \(-\)0.14 & 0.272 \\ Max diffuse damage radius (mm) & 1.32 \(\pm\) 0.27 & 1.26 \(\pm\) 0.27 & \(-\)0.07 & 0.447 \\ Max crack length (mm) & 1.62 \(\pm\) 0.46 & 1.34 \(\pm\) 0.35 & \(-\)0.28 & 0.025 \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}\)\(p\) \(<\) 0.05.  
\end{table}
Table 1: Characteristics of bone block specimens of combined thicknesses with and without a pilot hole drilled prior to insertion of the orthodontic miniscrew implant (OMI).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**No Pilot Hole**} & \multicolumn{2}{c}{**Pilot Hole**} \\ \cline{2-6}  & **1.5 mm** & **2.0 mm** & **2.5 mm** & **1.5 mm** & **2.0 mm** & **2.5 mm** \\
**Damage Characteristics** & **(\(n\) = 5)** & **(\(n\) = 5)** & **(\(n\) = 5)** & **(\(n\) = 10)** & **(\(n\) = 10)** & **(\(n\) = 10)** \\ \hline Total damage area (mm\({}^{2}\)) & 4.45 \(\pm\) 1.86 & 4.55 \(\pm\) 1.76 & 5.30 \(\pm\) 1.83 & 4.98 \(\pm\) 1.07 & 5.79 \(\pm\) 1.12 & 6.03 \(\pm\) 1.53 \\ Diffuse damage area (mm\({}^{2}\)) & 3.52 \(\pm\) 1.50 & 3.24 \(\pm\) 1.32 & 3.43 \(\pm\) 1.04 & 2.76 \(\pm\) 0.75 & 3.54 \(\pm\) 0.88 & 3.72 \(\pm\) 1.21 \\ Max damage radius (mm) & 2.12 \(\pm\) 0.66 & 1.78 \(\pm\) 0.36 & 1.99 \(\pm\) 0.39 & 1.66 \(\pm\) 0.24 & 1.92 \(\pm\) 0.34 & 1.89 \(\pm\) 0.36 \\ Max diffuse damage radius (mm) & 1.35 \(\pm\) 0.39 & 1.33 \(\pm\) 0.25 & 1.30 \(\pm\) 0.15 & 1.15 \(\pm\) 0.31 & 1.28 \(\pm\) 0.20 & 1.34 \(\pm\) 0.25 \\ Max crack length (mm) & 1.66 \(\pm\) 0.62 & 1.49 \(\pm\) 0.29 & 1.72 \(\pm\) 0.43 & 1.22 \(\pm\) 0.26 & 1.43 \(\pm\) 0.32 & 1.37 \(\pm\) 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Observed mean and standard deviation for each damage characteristic according to pilot hole status and bone thickness.
## 4 Description of the System
### The Communications System  
This element allows the OBC to communicate with other external systems through wireless communication infrastructures: 3G/GPRS/GSM and Wi-Fi. The 3G/GPRS/GSM infrastructure allows long-distance communication and is used for communication of technical alarms for any anomaly in the OBC or in the VPS. This communication is performed through short data packets. Wi-Fi communication is used to transfer data from the vehicle and the control center. Wi-Fi is also used to automatically update the onboard computer software. The Wi-Fi infrastructure, which allows vehicles to transfer data autonomously, consists of a set of access points located at points of the transportation network along which vehicles frequently pass and where they also remain static during significant periods of time: stations, stops at the beginning of the route, garages and shops. To perform intelligent communication and optimize the consumption of energy when executing data transfers, the system uses a geographical database that contains locations of Wi-Fi coverage points. Thus, the system uses the vehicle positioning provided by the VPS to determine when it can establish a connection to transfer data.
## 3 Results and Discussion
### Spatial Assessment of Heavy Metals in Surface Water and Sediment
All the mean concentrations of heavy metals in surface water were lower than the Chinese heavy metal guidelines [41] and World Health Organization (WHO) guidelines, while for sediments, all the mean metal concentrations were higher than the heavy metal guidelines [31]. The heavy metal concentrations of this tributary were compared with existing literature to identify the current pollution status in surface water and sediments. Regarding surface water, we compared the heavy metals to other freshwater ecosystems, such as Poyang, Chishui River, Dongting, Taihu, Chaohu, Hongze, Hong, Daye, Dianchi, and specific areas of the Yangtze River [10; 42; 43]. The mean concentration of As (5.44 \(\pm\) 0.58 mg/L) in our surface water was similar to Chao and Taihu Lakes, and it was slightly higher than the standard value observed in the Yangtze River (3.4-4.7 mg/L). Likewise, most of our heavy metal values were similar or in range with the Chaohu, Taihu, and Poyang lakes, indicating that heavy metal pollution in surface waters is a massive problem most freshwater ecosystems face [44]. Most of these lakes are prone to agricultural runoffs, discharges from wastewater treatment plants, and anthropogenic activities [3; 9; 10]. However, we did observe that our heavy metal concentrations for surface water were higher than the concentrations found in the middle and lower parts of the Yangtze River [7; 45]; we believe that this could be due to the exposure of this tributary to an excessive agricultural runoff that caused elevated levels of heavy metals, as this was also observed by [38; 46]. The sediment heavy metal concentrations were compared with Dianchi, Taihu, Haohu, Dongting, Poyang, Yangtze, Haihe River, Yellow River, Pearl River, Honghu, Donghu, and Daye lakes [6; 11; 47; 48]. Unlike surface water, the sediment metal concentrations were within range. Even though the values were within range, they were still higher than the guideline values, indicating that the sediments are exposed and vulnerable to diverse contamination sources, including industrial, agricultural, and anthropogenic activities [14]. The observation of sediment metal concentrations being higher than standard guidelines has been commonly observed by other studies that have also explored lakes and river sediments for metals [46; 47; 48]. The heavy metal concentration in this tributary indicates that government intervention with stricter guidelines and policies for lakes and rivers is highly required, and removing or reducing the heavy metal concentration could benefit the ecosystem.