esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Significant Statistical Differences Among Individual Sculptures
These findings support the third hypothesis, which posits that figurative art evokes higher engrossment and total immersion than abstract art when presented in AR. These findings align with the recent research by [PERSON] et al. (2019) on users' aesthetic preferences while viewing abstract and figurative public art, which concluded that people generally tend to appreciate figurative art, i.e., artworks rich in concrete content. However, the variance in immersion scores among the sculptures requires deeper analysis, particularly considering their materiality, design, thematic differences and spatial context. Sculpture S7, categorized as modern contemporary art, represents a geometric object, a cube crafted from clay and black reflective materials. In contrast, S9 depicts an animal form, entirely made of clay, with a more naturalistic and lifelike design. These distinct characteristics, combined with the AR presentation context, possibly shaped the participants' experiences. The low immersion scores for S7 may be attributed to its minimalist and abstract aesthetic. The geometric abstraction of S7 may be perceived as less approachable or harder to interpret, limiting its capacity to immerse viewers. Conversely, S9's animal form may draw upon innate human tendencies to recognize and empathize with animate forms, enhancing engagement and immersion. The materiality of the sculpture, clay, likely contributed to its tactile realism, which may have further enhanced its perceived authenticity and appeal in the digital realm. On the other hand, the reflective black surfaces of S7 might have introduced challenges in AR presentation, such as glare or distortion, potentially diminishing the perceived quality of the sculpture. Furthermore, the spatial context of the experiment could have played a critical role. The placement and lighting of the sculptures within the AR environment may have affected how participants perceived them. For instance, S7's reflective surfaces might have interacted unfavorably with AR lighting, creating distractions, while S9's design may have been accentuated by the same environment, emphasizing its lifelike qualities. User familiarity with animal forms versus geometric objects may have further contributed to these differences, as figurative works often align more closely with intuitive aesthetic preferences. While the hypothesis suggesting that figurative art representation in digitized sculptures contributes to heightened immersion remains plausible, these results highlight the importance of considering additional factors, such as material properties, design complexity, and spatial context. Future studies could explore these aspects more comprehensively, potentially isolating the contributions of these variables to better understand how abstract and figurative art elicit varying levels of immersion in AR settings.
## 3 Results
### Introduction of Online Personalisation and Adaptive Modelling Algorithm
#### 3.4.2 Streaming Data  
The availability of real-time sensor data from the developed SmartHelmet prototype allows for streaming data, which is processed via an online algorithm (stream processing) to adapt the offline model [42]. The streaming data includes new \(\tilde{n}\) samples of measured sensor data (new input matrix \(\tilde{\mathbf{X}}\)) acquired from the SmartHelmet sensors, and new personal thermal comfort votes (new output vector \(\tilde{\mathbf{T_{C}}}\)) acquired through an interactive query provided by the developed SmartHelmet App.
# DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images
## 3 Experiments
### Discussion  
#### 3.4.1 Discussion on \(\alpha\) and \(\beta\) in the Loss Function  
In the loss function, the two hyperparameters \(\alpha\) and \(\beta\) are introduced to control the weights of the cross-entropy loss and dice loss, respectively. By tuning the values of \(\alpha\) and \(\beta\) on the GID dataset, it is observed that the model achieves optimal performance in terms of the IoU, F1-score, and recall when \(\alpha=1.0\) and \(\beta=0.7\). Table 6 presents the effects of different \(\alpha\) and \(\beta\) values on the experimental results. These findings suggest that dice loss plays a crucial role in addressing the imbalance between positive and negative samples, while cross-entropy loss facilitates model convergence.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **OA** & **P** & **R** & **F1** & **IoU** \\ \hline \(\alpha=1.0\), \(\beta=1.0\) & 96.01 & 94.53 & 94.63 & 94.86 & 90.22 \\ \(\alpha=1.0\), \(\beta=0.5\) & 95.92 & 94.38 & 94.68 & 94.83 & 90.16 \\ \(\alpha=1.0\), \(\beta=0.7\) & 96.21 & 94.29 & 95.19 & 94.50 & 90.46 \\ \(\alpha=0.5\), \(\beta=1.0\) & 96.66 & 94.81 & 94.79 & 94.75 & 90.03 \\ \(\alpha=0.7\), \(\beta=1.0\) & 96.35 & 94.83 & 94.81 & 94.82 & 90.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The influence of \(\alpha\) and \(\beta\) parameters on model performance indicators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **OA** & **P** & **R** & **F1** & **IoU** \\ \hline Baseline + SSAM + D-up & 96.62 & 93.74 & 91.33 & 92.53 & 87.86 \\ Baseline + DMLKA + D-up & 95.18 & 94.37 & 93.32 & 93.88 & 88.54 \\ Baseline + DMLKA + SSAM & 95.62 & 94.48 & 94.46 & 94.44 & 89.61 \\ Baseline + DMLKA + SSAM + D-up & 96.21 & 94.29 & 95.19 & 94.50 & 90.46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of ablation models’ metrics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **OA** & **P** & **R** & **F1** & **IoU** \\ \hline SE & 95.53 & 92.40 & 94.84 & 93.53 & 88.05 \\ ASPP & 95.46 & 93.14 & 93.91 & 93.45 & 87.89 \\ CBAM & 95.86 & 92.53 & 95.79 & 94.08 & 88.98 \\ DMLKA & 96.21 & 94.29 & 95.19 & 94.50 & 90.46 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The impact of replacing the DMLKA module on model performance.
# Spectrum Demand Forecasting for IoT Services
## 4 Spectrum Forecasting Model
### Share of Devices per Service and Technology  
Let \(S\) be the set of all IoT services identified in your country's market and \(S_{i}\in S\) be the subset of services in scenario \(\mathcal{X}_{i}\).  
Let \(T\) be the set all IoT technologies and \(T_{i}\in T\) be the subset of technologies in scenario \(\mathcal{X}_{i}\). The total number of devices per _gateway_ (sink node, base station or access point) \(u_{i}\) can be calculated adding for each service, the number of devices per gateway in the above mentioned peak case \(u_{i,s}\). This devices should be distributed among the different technologies in \(T_{i}\) according to distribution ratios \(\rho\) derived for the market study. In summary:\[u_{i}=\sum_{s\in S_{i}}\sum_{t\in T_{i}}u_{s,t,i} \tag{1}\]  
\[u_{s,t,i}=\rho_{s,t,i}u_{i} \tag{2}\]  
where \(u_{s,t,i}\) is the number of devices per gateway, for the service \(s\), the technology \(t\) and the scenario \(i\) in the peak case. Additionally \(0\leq\rho_{s,t,i}\leq 1\) is the fraction of devices that will use the technology \(t\) for the service \(s\) in the \(i\)-th scenario.  
When a wireless technology used in a given scenario, is one of cellular nature (either a traditional cellular network or LPWAN), the number of devices \(u_{i}\) is replaced by the number of devices per hectare \(\tilde{u}_{i}\).
## 3 Case Study
### Results and Analysis of Abnormal Stations  
In this subsection, the fluctuation threshold of the tap-in passenger flow during normal operation for all stations in the network is calculated according to the method proposed in Section 2.1. All abnormal stations affected by the disruption were identified and displayed with spatiotemporal diagrams. Some representative ordinary stations and transfer stations in the Beijing rail transit network were selected for a comparative analysis.
## 4 Discussion  
### Spectral Analysis  
Spectral analysis can provide material information related to the chemical composition and physical properties. The Vis/NIR reflectance of plants is mainly characterized by the absorption (or reflection) of leaf pigments, such as chlorophyll, carotenoids, water, proteins, starches, waxes, and structural biochemical molecules [57]. The spectral plot in Figure 3 shows a typical reflectance of plants [58]. Hence, in the current study, to distinguish Chinese cabbage grown at different temperatures, it was necessary to develop VI, PLS-DA, and LS-SVM models.
## 5 Parametric Study
### The Effect of Expansion Rate on Bearing Capacity  
Using the calculation parameters from the model test conducted in this paper, we analyzed the impact of the expansion rate on the bearing capacity envelope using the calculation procedures outlined in Sections 2.2 and 2.3. The results are presented in Figure 13. The ground heave height corresponding to different expansion rates is 0.004-0.011 m. It can be seen from Figure 13 that with the increase of expansion rate, the bearing capacity of a single pile decreases. In addition, when the vertical load is less than 0.9\(V_{\text{u}}\), the limit torque decreases rapidly with the increase of the expansion rate. When the vertical load is greater than 0.9\(V_{\text{u}}\), the limit torque is gradually reached. The reason is that for a single loaded (vertical force or torque) pile, as the expansion rate increases, the ultimate torque will decrease significantly, while the vertical bearing capacity does not change significantly. When the vertical load is greater than 0.9\(V_{\text{u}}\), the effect of negative friction has been eliminated, the vertical friction with different expansion rates on the pile is close to each other, and the range of variation of circumferential frictional resistance becomes smaller. Thus, the bearing capacity envelope is relatively close. On the contrary, when the vertical load is less than 0.9\(V_{\text{u}}\), the negative friction effect on the pile has not been eliminated, and the range of variation of circumferential frictional resistance becomes larger. Therefore, the corresponding bearing capacity envelopes vary significantly.  
Figure 12: Normalized results under different loads. (**a**) Vertical displacement. (**b**) Torsion angle.
# Coordination of Congestion and Awareness Control in Vehicular Networks
## 2 Congestion and Awareness Control
### Congestion Control  
Congestion control protocols are aimed at controlling the channel load through the adaptation of communication parameters such as the transmission power, packet transmission frequency, receiver sensitivity or transmission data rate. This paper implements as a reference scheme an Adaptive DCC (A-DCC) protocol that adapts the packet transmission frequency of each vehicle to achieve a target channel load. This adaptive approach has been discussed in ETSI [19] as a possible evolution of the reactive approach currently defined by ETSI that specifies the communication parameters to be used for each channel load level. The A-DCC approach maximizes the message throughput possible for any given vehicle density, and generally achieves lower packet reception intervals and tracking error than the reactive approach [20]. In addition, it reduces the steady state oscillations and instabilities. We have adopted A-DCC as the reference scheme given its proven superior performance compared to the existing DCC reactive control process. A-DCC is based on LIMERIC [21; 22], a linear adaptive congestion control protocol that adapts the packet transmission frequency of each vehicle based on the channel load it locally measures and a fixed target CBR (Channel Busy Ratio) level. \(CBR\) is defined as the fraction of time during which the channel is sensed as busy. LIMERIC defines a linear control adaptation process such that the packet transmission frequency of a vehicle at a given time instant, \(T_{f}(t)\), depends on its previous value plus the difference between the locally experienced \(CBR\) and the maximum allowed \(CBR\) (\(CBR_{max}\)) multiplied by \(\beta\), the proportional gain. The packet transmission frequency of a given vehicle at time instant \(t\) can be calculated with the following equation (adapted from [21]):  
\[T_{f}(t)=(1-\alpha)T_{f}(t-1)+\beta(CBR_{max}-CBR(t-1)) \tag{1}\]  
where \(\alpha=0.1\) and \(\beta=1/150\) following [21]. With an additional mechanism that establishes a maximum gain in Equation (1), the study in [21] showed that LIMERIC can provide high accuracy and stability in scenarios where all the nodes measure the same channel load.  
A-DCC includes a piggybacking scheme defined in ETSI DCC_NET at the Network layer inspired by [23]. This scheme enables vehicles to exchange their locally experienced CBR, and the maximum CBR experienced by its 1-hop neighbors. Each vehicle uses the information exchanged to identify the maximum CBR experienced by its 2-hop neighboring vehicles, \(CBR_{2 hops}\). \(CBR_{2 hops}\) is used as an input to the adaptation process in order to control (and limit) the load experienced by vehicles located at 2-hops. A-DCC therefore adapts each vehicle's packet transmission frequency based on the following control equation:  
\[T_{f}(t)=(1-\alpha)T_{f}(t-1)+\beta\Big{(}CBR_{max}-CBR_{2 hops}(t-1)\Big{)} \tag{2}\]  
where \(CBR_{2 hops}\) represents the maximum \(CBR\) experienced within 2 hops.
## 3 Results
### Staircase Planning  
#### 3.3.1 Staircase Locations on Each Floor  
The planning of the staircase location and type significantly affects the size and layout of habitable rooms and bathrooms in townhouses. The schematic diagram of the ground floor layout for a townhouse, as shown in Figure 9, divides the building's width (x-direction) into left and right sides and its depth (y-direction) into front, middle, and rear sections when facing the road from inside the house. Consequently, the single-story floor plan is divided into six zones: left front (LF), right front (RF), left middle (LM), right middle (RM), left rear (LB), and right rear (RB). The location of the staircase on each floor is calculated accordingly, and if there is an intermediate landing, its position is counted twice.  
Figure 8: The coefficient of determination between the main space and the floor of the main building.  
The positions of each floor in the case studies are shown in Figure 10. Regardless of the floor level, the highest proportion of staircase planning occurs at the rear-right (RB) position. This is especially evident on the first floor, where the number of cases with staircases planned at the rear-right (RB) position is the highest. This phenomenon can be attributed to Taiwanese customs, where the main entrance is traditionally placed on the left side (the \"dragon side\"), resulting in the main circulation path being on the left. Thus, positioning the staircase on the right side optimizes space. It is common for townhouses to have parking spaces on the first floor, requiring a deeper layout to accommodate vehicles, which pushes the staircase position to the rear.  
On the second floor, most spaces are used as living rooms, dining areas, and kitchens. If the layout is open and has good visibility, it creates a spacious-feeling interior, which enhances sale opportunities. This layout results in the highest number of staircases at the rear-right (RB) position. The second most common position for staircases is the rear-middle (RM) on the second and third floors. This is primarily due to the fact that townhouses, except for the first and last units and corner units, are mostly middle units. Middle units have a common wall on both the left and right sides, with lighting available only at the front and rear. Positioning the staircase in the middle allows for windows in the front and rear sections, which can be used as bedrooms [22], thus fulfilling the spatial needs of townhouses.
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 5 Evaluations
### Implementation  
#### 5.1.1 Software and Hardware Platform  
We implement QR\(-\)LoRa on the Software-Defined Radio (SDR) USRP N210 equipped with a UBVA daughter board. The transmitter and receiver operate in the unlicensed 433.92 MHz ISM band. The standard transmit power of USRP N210 is 15 dBm. The Tx/Rx antennas are a pair of circularly polarized helical antennas, which have 8 dBi gain, as shown in Figure 11. During the experiments, each packet of QR\(-\)LoRa consists of 10 upchirp, 2.25 downchirp and 17 QR\(-\)LoRa chirp. Furthermore, the parameters are configured as SF = 7, BW = 250 KHz and \(f_{s}\) = 1 MHz.  
Figure 11: Hardware platform.
# Proxy Re-Encryption Scheme for Decentralized Storage Networks
## 6 Evaluation
### Applications with PRE  
We introduced PRE for decentralized storage network scenarios, which are the fundamental components for lots of blockchain applications. It is possible to build a media store for movies and music based on blockchain. Once a user purchases the movie, he has the right to download the movie file content freely. A purchase record is marked on the blockchain, as evidence of the right to use from the intellectual property owner. It is publicly verifiable. The PRE re-key can be used as evidence. The evidence can be listed publicly and it is meaningful only to the purchaser who owns the secret key.
# A Survey on Shortest Unique Substring Queries
## 6 SUPS Queries
### RLE-Based Approaches  
[PERSON] et al. [11] considered the shortest unique palindromic substring when the input string is given in its \(RLE\) representation. They showed how to preprocess a given \(RLE(S)\) of length \(m\) in \(\mathcal{O}(m)\) space and \(\mathcal{O}(m\log\sigma_{RLE_{S}}+m\sqrt{\log m/\log\log m})\) time, where \(\sigma_{RLE_{s}}\) is the number of distinct runs of \(RLE_{s}\). Their work is the first space-economial for \(\mathsf{SUPS}\) problem and can answer queries in \(\mathcal{O}(\sqrt{\log m/\log\log m}+occ)\) time.  
Similar to the technique for solving the normal \(\mathsf{SUPS}\) problem which is discussed in the previous subsection, they compute _MUPS_s of \(S\) in the preprocessing step. Their idea is based on the fact that the number of \(MUPS\)s of any string \(\mathcal{S}\) is at most \(m\)[11]. They first compute the palindromic substrings whose center is the same as the center for some run in the string. These palindromic substrings are called _run-centered_ palindromes. In order to compute these substrings, they utilize [PERSON]'s algorithm [43]. This algorithm scans the string and constructs an array \(MaxPal\) of length \(2n+1\) in which the ith entry is the length of the maximal palindrome with center \(i\). Using this technique, they compute all the \(RLE\)-maximal palindromes of \(\mathcal{S}\) in \(\mathcal{O}(m)\) time and space.  
In order to improve the space complexity, they build a data structure which is a modified eertree of the input string to deal with the run-length encoded string instead of the original one. They prove that this new data structure, RLE-eertree (\(e^{2}rtre^{2}\)), has \(2m+1\) nodes which is linear to the length of the \(RLE(\mathcal{S})\). Thus, it takes less space complexity compared with the original eertree. The construction time for \(e^{2}rtre^{2}\) is \(\mathcal{O}(m\log\sigma_{RLE})\). They used this tree to compute all the \(MUPS\)s of \(\mathcal{S}\). Using the list of \(MUPS\)s, they present their algorithm for SUPS queries. Their technique is similar to [PERSON] et al.'s work [4] with the difference that the space complexity should be \(\mathcal{O}(m)\).  
In 2020, [PERSON] et al. [12] published an extension of their work by considering a variant of SUPS problem where a query interval is also given in a run-length encoded representation. Their technique is similar to their previous approach [11]. They used combinatorial properties of maximal palindromes and presented an \(\mathcal{O}(m)\) space data structure to answer queries in \(\mathcal{O}(\log\log m+occ)\) time [12].
## 3 Results and Discussion
### Impact Hammer Tests  
The impact hammer testing (practically simpler to set up) proves inadequate to perform the EMA correctly: indeed, in this case, acceptable values are hardly obtained for the three main parameters that indicate the reliability of the EMA results, namely modal phase collinearity (MPC), mean phase deviation (MPD), and modal participation (MP) [32; 33]. Overall results are not consistent with the modal parameters computed through numerical analysis nor with those estimated through shaker tests EMA, and thus they are not reported in the following. This trouble might be reasonably explained considering the effects of localized impact forces on thin CFRP panels: significant local deformations may prevent the panel mode shapes to be effectively excited. The possibility of achieving more satisfactory results by using a miniature impact hammer may be verified in future tests.
## 3 Field Data Acquisition  
### Data Measurement System  
The data measurement system was built to measure the torque and rotational speed of the rear wheel axle generated by the tractor during field operations. The torque acting on the left and right rear wheel axles was measured using two wheel torquemeters (MW_30 kNm, MANNER Sensortelemetrie GmbH, Spaichingen, Germany). The wheel torquemeter can measure up to 30 kNm. The wheel rotation speed was calculated by detecting 100 gear teeth attached to the axle using a two-proximity sensors (MP-981, ONO SOKKI, Yokohama, Japan), both for left and right wheels. Field data were measured with a sampling rate of 100 Hz during data acquisition (CRONOS compact CRC-400-11, IMC, Berlin, Germany).
## 4 Test Results Analysis
### Analysis of Test Results of Lignin Fiber-Time Composite-Improved Soil  
#### 4.2.1 Unconfined Compressive Strength Test  
Based on the test results for the solely lignin fiber treatment, an optimum content of 8% was identified. In order to reduce lime addition, a series of experimental tests were performed to study the effectiveness of lime and lignin fiber treatment. In the unconfined compressive strength test, four groups of soil samples were prepared for comparative analysis: untreated soil, soil improved with (0% to 10%) lignin fibers and 2% lime composite, and soil improved with 4% lime. To study the curing time's effect, the soil samples were tested after 1, 3, 7, and 14 days of curing.  
Figure 11 illustrates the relationship between the compressive strength of the composite-improved soils. As shown in Figure 11, the compressive strength of the improved soil significantly increased with the 2% lime treatment. Furthermore, as the lignin fiber content increased, the compressive strength of the composite-improved soil initially increased, peaked at 8%, and then declined. In comparison to the soil improved with 2% lime, the strength of the soil improved with a mixture of 8% lignin fiber and 2% lime showed a 39.32% and 49.66% increase after 7 days and 14 days, respectively. Therefore, the optimum lignin fiber content in composite-improved soil is determined to be 8%.  
In Figure 12, the compressive strength of the soil improved with the 8% lignin fibers and 2% lime mixture is depicted at different curing ages, in contrast to the soils improved with 2% and 4% lime. As shown in Figure 12, the compressive strength of the composite-improved soil at various curing ages surpasses that of the soil improved with 2% lime but falls short of the strength of the soil improved with 4% lime. After 7 and 14 days of curing, the compressive strength of the composite-improved soil reaches 92.40% and 76.84% of the strength of the soil improved with 4% lime alone.