esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background and Proposed Method
### Adaptive Threshold Segmentation
Although the defect is segmented out as a whole compared with Figure 2d, too large a filtering window makes the hub geometry interfere with the defect area, such that the two come together, resulting in segmentation failure. Figure 3c shows the segmentation result when the threshold \(T\) is set to 0, and the noise interference and the effect of the light and dark edges are significantly stronger than in Figure 2d. Figure 3d shows the segmentation result when the threshold \(T\) becomes 20, and it can be seen that both noise and edge interference disappear, but the defect is just partially segmented out. Figure 3: Results of adaptive threshold with different parameters: (**a**) result of the smaller size filter; (**b**) result of the bigger size filter; (**c**) result of the smaller threshold; (**d**) result of the bigger threshold. It becomes clear now that too large or too small a smoothing window size or threshold value affects the final segmentation result. The two parameters, smoothing window size and threshold value, have to be determined intelligently to bring about perfect segmentation results. However, in practical applications, it is hard to achieve this goal, especially in defect detection of wheel X-ray images.
## 4 An Interoperable IoT Architecture for Precision Agriculture (RQ4)
### Connectors  
The main focus of this research is the introduction and development of connectors. A connector is software that enables communication between various sensors deployed in the real world and the end user (such as a farmer) [81]. The system is designed with as many connectors as there are different types of data to convert. For example, in our system, there are five types of sensor produced by different manufacturers, each with different payload formats. As a result, five connectors will be set up to handle the data conversion for each sensor type. The deployment of multiple connectors brings significant advantages to the system compared to a single-connector configuration. Some potential advantages are listed below.  
**Workload reduction on the connector**: The connector listens on a channel where it is certain that the received data is convertible, so there is no need to verify the origin of the data.  
**System scalability**: If there is a need to add or replace sensors in the system, it is sufficient to work only with the involved connectors, adding new ones if necessary, without affecting the other connectors.  
**Software maintenance**: In case of bugs or sensor firmware updates, it is enough to update the software of the associated connector.  
As mentioned earlier, the purpose of a connector is to receive the data, convert them, and then send them to the central database. Its operation consists of four phases, which are listed below:  
1. **Waiting for Data**: The connector remains in a waiting state to receive data on the \"topic\" to which it has subscribed. It is the broker's role to act as an intermediary between the gateway and the connectors. Each connector is subscribed to one and only one \"topic\" for the benefits mentioned earlier.
2. **Data Verification**: Upon receiving the data, the connector checks that the data are intact and that there are no errors in the payload. If any issues are found, the received data are discarded.
3. **Data Conversion**: Following the integrity check, if successful, the conversion occurs. In practice, the connector reads the data and, using a predefined schema associated with the data type, converts it into alphanumeric values suitable for the OGC SensorThings standard and collects them into a JSON object to be sent to the central database.
4. **Data Sending**: The JSON object is sent to the OGC SensorThings server to the corresponding entity using the methods described in the previous sections.
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 5 Evaluation
### Performance Results
#### 5.2.5 Comparison with Common File Systems  
We compared our in-network cooperative storage system with IPFS and CEPH, which are common distributed storage systems. We wrote different files having sizes of 1, 5 and 10 GB to these file systems under different cluster sizes (including three and five nodes). Our experiments show that the average throughput of the three-node CEPH cluster is about 8 Gbps. When the number of CEPH cluster nodes increases to five, the average throughput is about 9.15 Gbps for a single client. The average throughput of the five-node CEPH cluster drops to about 8.05 Gbps when two clients write simultaneously. It is shown that CEPH achieved a relatively high performance through load-balancing strategies. However, this is still implemented in the application layer based on the current IP infrastructure. CEPH has inherent flaws, such as application layer protocol processing and scheduling overhead. IPFS is another popular distributed storage system based on DHT. It takes about 2 min to upload a 10 GB file for the first time, but the time for subsequent uploads is reduced by half. The average upload rate is about 1.5 Gbps in IPFS.  
We used five servers to write simultaneously to R4, and the bandwidth of PATH4 was limited to 2 Gbps. The traffic of the R4 output interface is shown in Figure 11. Four servers can write at an average of 4 Gbps, whereas one server can write at only 1.6 Gbps. The peak throughput reaches 16.3 Gbps when five servers write simultaneously, and the average throughput is about 9.3 Gbps. Through experiments, it is shown that our storage system co-designed with the network is more suitable for real-time data writing. By comparison, IPFS is more suitable for file sharing, and CEPH is a cluster system, which is limited by its scale.  
Figure 11: Throughput when five servers write simultaneously.  
Figure 10: Results of the influence of the number of flow entries.
## 4 Discussion
### Countermovement Jump Performance
[8], who found increases in lower body power output in trained subjects performing CMJ with AEL via 30% of BM compared to the CMJ condition that used only their BM. Similarly, [PERSON] et al. [17] also reported increases in lower body peak power in elite male volleyball players using a countermovement blocking jump with a 20 kg AEL load compared to the blocking jump without AEL. Evidence has suggested that eccentric training may be effective in increasing power output through changes in muscle architecture that may reflect a shift toward a more explosive phenotype and a stiffer muscle-tendon unit, improving the ability to produce force rapidly [4]. It is important to mention that accentuating the eccentric load does not improve concentric muscle production \"per se\" [53]. The load used must be considered as manageable for the tissues by the nervous system; otherwise, muscle elasticity increases and the muscle-tendon system will act more as a shock absorber absorbing forces than as a spring [55]. Therefore, the effects on the maximum power of the lower body may be different depending on the magnitude of AEL [56]. The analysis of Rs and NRs indicated that nine boxers from the AEL group exceeded the technical error threshold for peak power. Similarly, eight boxers from the AEL group exceeded the technical error threshold for relative peak power, while for the CMJ group, only one boxer exceeded the technical error threshold for peak power and relative peak power. This is similar to the findings reported for the propulsive impulse of the CMJ. Consequently, it is likely that the increase in propulsive impulse directly contributes to the improvements found in peak power of the CMJ [4]. On the other hand, the execution of CMJ with BM alone may be an insufficient stimulus to improve lower body muscle power in amateur boxers. It is important to note that although improvements in punching and jumping performance were observed, the present study did not directly assess neuromuscular variables such as rate of force development, electromyographic activity, or muscle architecture. Therefore, any explanations regarding underlying mechanisms--such as enhanced motor unit recruitment, increased stiffness, or SSC efficiency--should be considered speculative. These hypotheses are grounded in previous findings from similar interventions, but further research is needed to confirm the specific neuromechanical adaptations induced by submaximal AEL in amateur boxers. The present study expands the evidence on submaximal AEL by demonstrating that a low-load protocol (10-20% of body mass), applied using dumbbells, is effective even in amateur boxers with no prior AEL experience for improving lower-limb muscle power and punching force. Future studies should consider including direct neuromuscular and biomechanical assessments, such as electromyography or motion capture, to more accurately identify the mechanisms responsible for these adaptations. Additionally, different volumes and intensities of AEL loading should be explored to determine the optimal stimulus for maximizing physical performance in athletes.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 4 Methodology for Inter-Comparisons
It has been applied successfully to SLSTR level 1 and L2 sea surface temperature (SST) data (see Section 7). Tandem data can also be used to evaluate the measurement noise (independent uncertainty). Since the two satellites are observing the same scenes and nearly identical conditions, a direct comparison of the measurement noise is possible. This has been done to compare the SRAL-A and B altimetry precision over land ice and to analyze the impact of measurement noise on climate records for mean sea level or chlorophyll concentration. The triple-collocation methodology (see e.g., [27, 28]) is another powerful tool, which can be leveraged to analyze measurement uncertainty in the context of tandem operations. In this approach, we introduced a third independent dataset of measurements \(L_{\text{C}}\), and we used the observed standard deviations of the three different pairs to establish the uncertainty associated with random effects for each sensor. Based on the method given in [27, 28], but updating the notation to be compatible with the GUM and metrological standards, the uncertainty associated with independent effects for sensor A can be evaluated through:  
\[u(\varepsilon_{\text{A}})=(\bigvee(\sigma^{2}_{\text{AB}}+\sigma^{2}_{\text{AC }}-\sigma^{2}_{\text{BC}}))/2, \tag{2}\]  
where \(\sigma_{ij}\) denotes the observed standard deviation of the differences between the pair of observations from datasets \(i\) and \(j\). The uncertainties associated with independent effects for measurements B and C are obtained using similar formulas as Equation (2). This methodology was used to compare SRAL sea state measurements to buoys and ERA-5 model data, see [19].
## 4 Discussion
### Uncertainty Analysis  
Using tower-based spectral measurements combined with flux measurements is an efficient way to investigate SIF upscaling and SIF-GPP links. In this study, the result that upscaling SIF\({}_{\text{inst}}\) to the daily scale reduces the effects of latitude on the SIF\({}_{\text{ins}}\)-SIF\({}_{\text{daily}}\) and SIF\({}_{\text{ins}}\)-APAR\({}_{\text{daily}}\) relationships agree with the results of the SIF-GPP correlations in [27]. Therefore, although the SIF and APAR data at different latitudes were obtained from simulations rather than measurements, this does not affect the analysis on the influence of latitude on SIF-GPP variations. In addition, the results of the SIF-GPP correlations indicate that the slope of the GPP-SIF relationship was much higher for C4 crops, which is consistent with the results in [19], demonstrating that this phenomenon is due to similar fluorescence efficiency but a large difference in LUE between C3 and C4 crops.  
There are some uncertainties and limitations in this study. First, the PAR-based SIF upscaling method considered that the diurnal SIF is only driven by diurnal PAR variations. Although diurnal variations in FPAR and fluorescence efficiency are much smaller compared to the diurnal variation in PAR, it also affects the diurnal cycles of SIF [20; 56] and, thus, brings some errors in the PAR-based predicted SIF values and upscaled SIF\({}_{\text{daily}}\). Second, the GPP data at the XTS site were simulated using the SCOPE model by measuring or retrieving the main driving variables (e.g., Cab, LAI, and V\({}_{\text{cmax}}\)) for GPP simulation. The measuring and retrieving uncertainties bring some errors for the parameterization of the SCOPE model and, thus, for the accuracy of the GPP. More GPP observations corresponding to SIF measurements should be collected to support the results in this study.
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### Sap Flow Data and Transpiration Estimation
#### 3.3.2 Transpiration Estimates from Sap Flow Measurements
Figure 10: Transpiration (\(\tau\)) calculations in the ASWP site based on the measurements in nodes 2014, 2034 and 2134, from 11 July 2016 (7/11/16) to 11 October 2016 (10/11/16). (**a**) Transpiration rates in mm/h based on a 10-min interval; (**b**) Transpiration rates in mm/day based on a 24-h interval. Figure 10b shows the daily \(\tau\) values, which is consistent with previous studies that have similar geographic and climatic characteristics to the ASWP site [(60; 61; 88; 89)]. The maximum, minimum and average values of daily \(\tau\) during the specified period of time are 4.49 mm, 0.44 mm and 2.01 mm, respectively.
# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 3 Lead-Time Prediction Based on NHPSO-JIVAC-SVM  
### Data and Preparation  
This paper presents a hybrid artificial intelligence (AI) model to predict the lead time in the shipyard block process. As shown in Table 2, we applied it to two datasets collected from a shipyard's block assembly and a pre-outfitting process to evaluate the proposed model. The assembly and pre-outfitting processes consisted of information from 4779 and 4198 blocks. Each dataset was split into training and test data. Eighty percent of each dataset, 3823 and 3358 data points, was used to train data individually, and 20% of each dataset, 956 and 840 data points, was used to test data separately.  
The target value (label) of each dataset was the lead time. A part of the original dataset is shown in Figure 4a,b.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Dataset** & **Prediction Target** \\ \hline
1 & Assembly process performance data & Lead time \\
2 & Pre-outfitting process performance data & Lead time \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data collection.  
Figure 4: Original dataset of (**a**) block assembly process and (**b**) block pre-outfitting process.
## 3 GNSS Data Collection with Android-Based Smartphones
### General Setup Configuration with External Antennas  
Figure 1 and Table 1 depict the general external antenna setup to assess the observation quality of the GNSS data collected by two Android-based smartphones, namely, Samsung Galaxy S20 (S20) and Google Pixel 4 (GP4). The duty-cycling settings of all smartphones were disabled during the experiment. Hence, we were able to observe continuous carrier-phase observations without discontinuity [9]. The GP4 and S20 smartphones are able to track dual-frequency GPS L1 + L5, Galileo E1 + E5a, QZSS L1 + L5 and BDS B1 code and phase observations. The receivers and antennas are placed in a radio frequency (RF) shielding box to prevent them from receiving GNSS signals other than from the intended re-radiating antenna. The direct GNSS signals are collected from a rooftop active low-cost antenna, Swift GPS500, which costs a few hundred USDs, and then re-radiated via a passive antenna inside the RF shielding box. A signal amplifier is connected in between the rooftop antenna and re-radiating antenna to reduce the effect of signal attenuation, see Figure 1. The smartphones are also benchmarked against survey-grade Trimble NetR9 receivers + Zephyr-2 antennas and low-cost wlox ZED-F9P receivers + patch antennas that are also placed inside the RF shielding box. The main purpose of this benchmarking is to assure that no GNSS signal leakage is experienced in the RF shielding box that could potentially worsen the RTK performance. Note that L2 rather than L5 observations were tracked by the Trimble and wlox receivers, respectively. For wlox, we did so since, at the time of writing, there was no support for L5 observations, whereas for the Trimble NetR9 receivers we made use of an old firmware version that was possibly not able to track the GPS/QZSS L5 observations [25]. We also note that the analysed smartphones are all restricted (at the time of writing) to L5 rather than L2 observations.  
The Trimble NetR9 and low-cost wlox receivers logged GPS + Galileo + QZSS + BDS measurements at 1-s data interval (cf. Table 1), and the smartphones logged the observations through the Geo++ RINEX Logger vers. 2.1.6. At the time of collecting this data, we found that a maximum timespan of about 17 h and 15 h can be collected for S20 and GP4, respectively. This was limited by a battery-saving mechanism preventing them to operate for longer timespans. The Geo++ RINEX logger developers found a similar issue on e.g., the Xiaomi Mi8 and Huawei P20X smartphones [26].
## 4 Results
### Wildfire Geography  
Relative number of fires (RNF, i.e., fires per 100,000 ha of forests) has the highest value in the middle of the central Siberia and in the southern part of the larch range (Trans-Baikal area, Figure 3a). As discussed further, that was related to the soil moisture content and air temperature (Figures S2-S4; Table 1).  
Geographically, extreme large burns occurred mostly in the southern and northern parts of the larch range (Figures 3b and 4). It is known that in the southern part of the larch range, fires were mostly caused by humans; whereas in the northern part, fires were mostly caused by lightning [13].  
Along the geographic latitude, the burned area and the number of fires are changing synchronously (Figure 4). Both variables reach maximum values within the southern and northern parts of the larch range, whereas minimum values are observed within mid-latitudes (55\({}^{\circ}\)-59\({}^{\circ}\)). The latter refers to forests fragmentation and better fire suppression in this territory. At the same time, mean burned area is increasing in the northward direction, having maximum values between 65\({}^{\circ}\) and 71\({}^{\circ}\), which was attributed to the higher values of available \"fuel\". Furthermore, wildfires are not actually suppressed in those remote areas.  
A sharp decrease is observed at higher latitudes, referring to the lower insolation which led to insufficient fuel dryness.  
Figure 2: Temporal dynamics of summer (JJA) (**a\({}_{1}\),a\({}_{2}\)**) air temperature, (**b\({}_{1}\),b\({}_{2}\)**) precipitation, (**c\({}_{1}\),c\({}_{2}\)**) soil moisture (0–7 cm), (**d\({}_{1}\),d\({}_{2}\)**) SPEI drought index, and (**e\({}_{1}\),e\({}_{2}\)**) terrestrial water content. Abbreviations: Rng—total larch range within the permafrost zone; North, South1, and South2—areas 1, 2, and 3 on Figure 1, respectively. All trends are significant at \(p<0.05\).  
Figure 3: Maps of **(a)** relative number of fires (RNF) and **(b)** large (200 ha \(<\) burned area \(\leq\) 10,000 ha) and extreme fires (burned area \(>\) 10,000 ha) within the larch range. Large fires are indicated by grey color. The largest number of fires was observed in (1) the middle of the Central Siberia; and (2) in the Trans-Baikal area. Extreme fires were mostly located in the southern and northern parts of the larch range. Study period is 1996–2022.
## 8 Conclusions  
The aim of this study was to quantitatively assess carbon neutrality performance by examining carbon emissions (CE) and carbon reduction (CR) throughout the production stage of a building's entire life cycle, leading to the following conclusions.  
1. The A1-A3 ZCBI was introduced to evaluate carbon neutrality performance by assessing A1-A3 CE and A1-A3 CR during the production stage.
2. To analyze the A1-A3 CR and A1-A3 ZCBI, an EPD database was created for 797 building materials, sourced from 1832 EPD-certified products manufactured in Korea.
3. As EPD-certified products exhibit various A1-A3 CE values depending on the manufacturer or model within the same product group, the lowest, average, and highestA1-A3 CE values were identified for each group. This distinction facilitated decision-making in product selection and analysis of A1-A3 CR and A1-A3 ZCBI.
* To analyze the proposed A1-A3 ZCBI, reference buildings were created based on four classification systems: region, building purpose, structural form, and structural scale.
* The A1-A3 ZCBI analysis of major construction materials showed that the A1-A3 ZCBI ranged from 8.84% to 65.24% for apartment houses and from 8.58% to 55.40% for business facilities. Additionally, the ZCBI across all stages based on A1-A3 CR ranged from 2.65% to 19.56% for apartment houses and from 1.77% to 11.42% for business facilities.
* Finally, the key materials contributing to the top 99% of cumulative carbon emissions during the production stage (A1-A3 CE) were identified. For both apartment houses and business facilities, these materials, ranked by impact, were ready-mixed concrete, steel, cement, and masonry.  
Conceptualization, S.W.; Methodology, S.W.; Resources, S.T.; Writing--Original Draft Preparation, S.W.; Writing--Review and Editing, S.T.; Visualization, S.W.; Supervision, S.T.; Project Administration, S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data availability Statement: Data are available on request from the corresponding author.  
This work was supported by the research fund of Hanyang University (No. HY-2023-3021).  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 7 Discussion on User Studies and Suggestions for User-Centric Task Assistance
### Discussion on User Studies
#### 7.1.2 Evaluation for the Gesture Interaction  
For the 3D virtual object matching task concerning different gesture interactions, the hand gesture interaction demonstrated faster task completion time than the multi-touch interaction. The faster task completion is due to the concurrency control of the interaction (selection, translation, rotation, etc.) and intuitiveness of using a real hand to perform the task. Since the hand gesture-based interaction provides the same metaphor as the user performs typical tasks in the real environment, the user can simultaneously perform the translation and rotation without a separate mode change through the selection of the menu using their hand. On the other hand, in the case of the multi-touch interaction, whenever a control object is selected through the touchpad of smart glasses or the transformation needs to be changed, this method requires a lot of interaction with the gizmo. Furthermore, the user must change the mode to select and manipulate the control object whenever a different transformation is required.  
Interviews also showed that interaction with hand gestures was more natural and effective. However, it was uncomfortable to wear another sensor for detecting hand gestures. On the other hand, the interaction using the multi-touch interaction made sophisticated tasks difficult because the mode had to be converted sequentially and frequently (for example, in order of selection \(\rightarrow\) object translation \(\rightarrow\) object rotation) to complete a specific interaction. As a result, it was found that the satisfaction level was lower than that of the hand gesture.  
There was no statistical difference between different gesture interactions in 2D interactions, such as selection. Interestingly, when performing the 2D-based tasks, some users moved their heads to specific locations and then clicked menus rather than moving the cursor with the touchpad.