esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
Notably, the SSIM provides a measure closely aligned with human perception as it assesses the quality of digital images relative to the original image considering factors such as luminance, contrast, and structure [27; 28]. The SSIM/PSNR values are obtained through a new methodology involving subjecting a set of frames representing the video to varying noise levels, simulating fluctuations in channel conditions during video transmission. This methodology yields the average percentage gain in the SSIM of one encoder relative to the other. In summary, this article's contributions include:  
* Utilization of the SSIM for temporal video quality evaluation, aligning closely with human perception;
* Development of a novel methodology assessing SSIM/PSNR values through frame-by-frame analysis under varying noise levels, simulating channel condition fluctuations during video transmission;
* Consideration of temporal variations enabling the generation of quantitative data for more accurate analysis of technology performance regarding video quality, aiding professionals and researchers in technology selection;
* Identification of the most efficient techniques in reducing quality degradation, facilitating prediction and optimization of video quality, particularly for streaming ultra-resolution videos. The subsequent sections of this document are structured as follows: Section 3 elucidates the methodology and metrics employed to derive the results, Section 4 showcases the outcomes attained in this investigation, and Section 5 deliberates upon the findings. The final insights are encapsulated in Section 6.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Renewable Energy Sources
#### 4.6.2 Pv-Net  
The \"PV-NET\" project targeted at promoting the use of RES and in particular the PV panels. It involves participants from six countries and it aimed at developing the most optimum PV net metering model, meaning that for each end user the net consumed electrical energy has been calculated by subtracting the energy produced by the photovoltaic systems. These net metering schemes have been optimized according to the area of application and to local pricing techniques [113].
# For Patients with Stroke, Balance Ability Affects the Leg Extension Angle on the Affected Side
## 2 Materials and Methods
### Statistical Analysis  
The average results from 10 strides were analyzed. Prior to analysis, the Shapiro-Wilk test was used to assess the normality of the data distribution. First, differences in the leg extension angle and the increment of velocity between the affected and non-affected sides were compared with the paired t-test. Consecutively, the relationships between the leg extension angle on the affected side and BBS, FMA-LL, MI-LL, velocity increments on the affected side, and gait velocity were investigated with Pearson's or Spearman's rank correlation coefficient. In addition, a multiple linear regression was performed to determine factors related to the leg extension angle on the affected side. A stepwise procedure was utilized, with variables included in the model at a significance level of \(p<0.05\) and excluded from the model at \(p>0.10\). To avoid multi-collinearity, one of the two factors with a high correlation (>0.8) or a high variance inflation factor (>3.33) was excluded from the later analysis [42]. In addition, a path analysis based on the exploratory regression analysis was used to confirm the relationship among the extracted factors, leg extension angle, and gait speed. The exploratory regression analysis was conducted sequentially to identify the factors related to gait speed according to the methods described above. The leg extension angle and the increment of velocity on the unaffected lower limb were also included in the exploratory analysis. In path analysis, the model fit adequacy was determined using the chi-square test (a non-significant chi-square value indicates a good fit); comparative fit index (CFI, >0.95 indicates a good fit); goodness-of-fit index (GFI, >0.90 indicates a good fit); and root mean square error of approximation (RMSEA, <0.05 indicates a good fit) [43].  
Statistical Software R 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria) and AMOS 28.0 for Windows (IBM Japan, Tokyo, Japan) were utilized for all statistical analyses. Statistical significance was set at \(p<0.05\), and values are expressed as average \(\pm\) standard deviation.  
Figure 1: Position of inertial measurement units (IMUs). IMUs were fixed to the sacrum, the bilateral anterior thigh, and shank.
## 3 Energy Harvesting
### Summary of Energy Harvesting
Advances in nanotechnology have further increased the effectiveness of t-TENGs. Researchers have, for instance, applied nanostructured surfaces to textile fibers to boost their contact area and, thus, their triboelectric effect [92]. Additionally, methods such as electrospinning can produce nanofiber-based TENGs with a highly textured surface, promoting greater charge density and, therefore, higher energy output [41]. Meanwhile, [PERSON], [PERSON], and [PERSON] [34] integrated triboelectric nanogenators (TENGs) with 3D printing--using polymers such as PDMS (polydimethylsiloxane) and PVDF (polyvinylidene fluoride) for flexible TENGs, as well as solid materials such as ABS (acrylonitrile butadiene styrene) and PLA (polylactic acid)-to address issues of scalability, cost, and design flexibility.
## 4 Design of Multipoint Displacement Monitoring Algorithm for Bridge Structure
### The Method of Establishing the Space-Time Relationship of Image Sequence Data
#### 4.2.2 Target Tracking and Displacement Calculation
Since the collected image has less data than the video and does not pursue timeliness and according to the object extraction method described in Section 3.1, the displacement of all points on the image need not be calculated, but the offset of each pixel on the extracted contour line for pixel level image registration must be calculated. Thus, the Horn-Schunck algorithm [50] was selected. The Horn-Schunck algorithm belongs to the dense optical flow with the best accuracy. The objective function of the Horn-Schunck algorithm is as follows:  
\[\underset{u,v}{\text{min}}E(u,v)=\iint\left[\left(T(x,y)-f(x+u,y+v)\right)^{2 }+\lambda\cdot(u_{x}^{2}+u_{y}^{2}+v_{x}^{2}+v_{y}^{2})\right]dxdy \tag{5}\]  
In order to facilitate the calculation, the approximation method in the sparse optical flow is used to approximate the data items linearly to obtain a new objective function:  
\[\underset{u,v}{\text{min}}E(u,v)=\iint\left[\left(f_{x}u+f_{y}v+f_{t}\right)^ {2}+\lambda\cdot(u_{x}^{2}+u_{y}^{2}+v_{x}^{2}+v_{y}^{2})\right]dxdy \tag{6}\]  
The definite integral of the above formula can be rewritten into discrete form. Then, the corresponding Jacobian iterative formula can be obtained by the super relaxation iterative method (SOR):  
\[u=\overline{u}-f_{x}\frac{f_{x}\overline{u}+f_{y}\overline{v}+f_{t}}{a+f_{x} ^{2}+f_{y}^{2}} \tag{7}\]  
\[v=\overline{v}-f_{y}\frac{f_{x}\overline{u}+f_{y}\overline{v}+f_{t}}{a+f_{x} ^{2}+f_{y}^{2}} \tag{8}\]  
According to Equations (11), (14), and (15), optical flow vectors \(t_{i}\) to \(t_{i+1}\) can be calculated, and all time \(u_{i}\) and \(v_{i}\) can be summed to calculate the displacement of the pixel point.
## 3 Results
### LiDAR Based Structural Variability  
The descriptive statistics (mean, standard deviation SD, minimum, maximum and coefficient of variation or CV) of the two LiDAR-derived parameters extracted at 275 m pixel size are shown in Table 1. The mean tree height (H\({}_{\text{mean}}\)) followed an expected trend across the vegetation types with an increasing value from savannas (3.6 m) to indigenous forests (6.2 m), and then forest plantations (11.9 m). Savannas in the Lowveld are dominated by relatively small trees as indicated by the low H\({}_{\text{mean}}\) CV (0.2) and with a range of mean tree height varying between 1.2 and 8.4 m. As expected, savanna was the most heterogeneous vegetation type with the lowest mean CC (22.3%) and the highest CC CV (0.6), while the maximum CC only reached 70%. The maximum H\({}_{\text{mean}}\) was 14.2 m and 21.4 m for indigenous forests and forest plantations, respectively, in the iSimangaliso St-Lucia site. The mean CC for plantations and indigenous forests was higher than savannas at around 50% with a CV varying between 0.3 and 0.5. Plantations follow a typical growth cycle starting from bare ground up to a closed tall canopy, with height and cover varying significantly across stands of different ages. However, the CVs for both H\({}_{\text{mean}}\) and CC in the indigenous forests were higher than expected (0.4 and 0.5, respectively), and were possibly due to two types of forest of different heights (i.e., Northern Coastal Forest and Mangrove Forest). Hence, the data exhibited a bimodal histogram, with two peaks for H\({}_{\text{mean}}\) at the 4-5 m and 7-8 m and for CC at the 30-40% and 80-90% (Figure 4a,b). In addition, the wide range of H\({}_{\text{mean}}\) and CC for indigenous forests may be exacerbated by bordering pixels in the Dukuduku forest fragmented landscape. The combined LiDAR datasets (all three vegetation types) cover a wide range of CC (0 to 90%) and height (1 to 21 m). However, the savanna samples largely dominate the dataset with 83.7% coverage, while indigenous and plantation forests make up 6.0% and 10.3% of samples, respectively.
# AVSS: Airborne Video Surveillance System
## 5 AVSS Features
### Video Rate Adaptation (Fleet Platform)  
The users or the administrator should be able to inspect the area even when an abnormal situation is detected by the system. Therefore, the UAVs should have the ability to provide video stream to the users or administrator on request. However, since the bit rate of a video is quite high, we employed a video rate adaptation to fully utilize the network.  
The surveillance module reports the video stream information to GCS whenever there is a request of video stream service. Therefore, we can get an entire topology of the networks including the route of video streams in real-time. Based on the information and the positions of UAVs, we can estimate a capacity of each link from the distance [17]. In addition, the video streams are prioritized based on the event score. We consider a set of video requests \(x=1,...,N\) and links \(1,...,L\) Let the capacity of link be \(i\) is \(C_{i}\), and a feasible set of rates of request \(x\) is \(v_{x}\). Additionally, the priority is denoted with weights \(w_{ij}\). The sum of the weights of all video streams is following:  
\[w_{i}=\sum_{j}w_{ij}. \tag{3}\]  
Then we divide \(v_{x}\) by \(w_{x}\) and get \(v^{\prime}_{x}\). We adopt a max-min fair (MMF) allocation [18] with weighted value of rate, \(v^{\prime}_{x}\). MMF allocation defines that a vector of rates \(v_{x}{}^{\prime}\) is max-min fair if no individual rate \(v^{\prime}_{x}\) can be increased without decreasing any other rate equal or smaller. We modified progressive filling procedure to find a set of values of \(v_{x}\) as follows:  
1. \(v^{\prime}_{x}\gets 0\) for all \(x\in N\),
2. Increase all \(v^{\prime}_{x}\) at the same speed for all demands, until some link saturated; \(c_{l}=\sum_{x}v^{\prime}_{x}\), for all \(x\) passing by link \(L_{l}\),
3. Remove saturated links, \(l\) from **Step 2**, and demands using those links,
4. Go back to **Step 2** until no demand left,
5. Set the rate \(v_{x}\) by multiplying corresponding \(w_{x}\) to obtained rate \(v^{\prime}_{x}\).  
In short, the video rate of each stream has a guaranteed fairness from the centralized control.
## 3 Experimental Method  
In order to validate the present numerical simulation, an experimental setup to investigate the smoke flow field and heat transfer characteristics of 3 \(\times\) 3 pin-fin heat-sink array in a rectangular channel is built. The experimental setup displayed in Figure 4. The relevant experimental setup, procedure and heat-loss estimation are similar with those reported in the authors' previous work [28]. The test section is made of a straightener and a rectangular channel with the 20 mm-thick Plexiglas cover and 20 mm-thick Bakelite bottom base. The configuration and dimensions of test section and positions of measured points are shown in Figure 5. In total, 9 pin-fin heat sinks are situated in the rectangular channel with \(X_{T}^{*}(=S_{T}^{*}/L)=1.5\) and \(X_{L}^{*}(=S_{L}^{*}/L)=2.0\). The Sample 5 heat sink shown in Table 1 is selected for the experiment herein. These pin-fin heat sinks are made of aluminum 6061. Each heat sink has a corresponding film heater fixed on its bottom surface. The wall heat flux supplied to each film heater from the electronic power is 0.49 W/cm\({}^{2}\) for 143 liter/min flow rate. Fifth thermocouple (Model: T-TT-30, OMEGA Engineering Inc., Stamford, CT, USA) for measuring the wall temperatures are embedded on the bottom surfaces of the center-column heat sinks. The ambient temperature and the air temperature at the channel inlet are also monitored by other thermocouples. Besides, local air velocity is measured by a hot-wire anemometer (Model: IEL VA-20, I Denshi Giken Co., Ltd., Ebina, Japan). Uncertainties in parameters are estimated by using the root-sum-square method of Moffat [29]. The experimental data obtained herein revealed that the uncertainties in the Reynolds number and the Nusselt number (as shown in Equations (10), (12) and (14)) were \(\pm 5.91\%\) and \(\pm 6.83\%\), respectively.  
Figure 4: Experimental setup.
## 2 Materials and Methods
### Study Design  
All tests were classified as either off-ice or on-ice tests. The study was divided into two parts. In part one, all subjects performed the off-ice tests including the countermovement jump test (CMJ), the standing long jump test, the single-leg (left and right) lateral jump test, the single-leg skate simulation test (Skate SIM), the 30 m off-ice sprinting test, and the 30 m off-ice inline skating test. All off-ice tests were performed on the indoor field of the local training center. In part two, all subjects performed the 30 m on-ice skating test and the specific overall skating performance test for ice hockey (SOSPT) on an International Ice Hockey Federation (IIHF) conformed standard hockey rink in the local training center.  
The testing procedure used a standardized testing sequence for all subjects. The testing day started by measuring anthropometric data (body height and leg length) and collecting personal data (age, playing position, and training experience). The leg length was used to standardize the Skate SIM. After preparing the equipment (glove, stick, ice skates, and inline skates) all subjects started with a general warm-up (8 min jogging) followed by a 15 min specific warm-up including mobilization, four maximal countermovement jumps and 20-30 m run-ups. Participants were divided in two randomized groups, all subjects performed the CMJ (including body weight measurement), the standing long jump test, the lateral single-leg jump test, and the Skate SIM (first group and vice versa) as well as the 30 m off-ice sprinting test and 30 m off-ice skating test (second group and vice versa). After completing the off-ice tests, all subjects returned to the ice rink (8 min jogging) and prepared for the on-ice tests. Forty minutes after finishing the off-ice tests all subjects performed a 15 min specific warm-up on-ice including forward and backward skating, ice hockey specific maneuvers such as stops, turns, and curves in all directions, as well as four 20 m run-ups. For the on-ice tests, all subjects first performed the 30 m on-ice sprinting tests followed by the SOSPT (Figure 1). All subjects performed all tests within one testing day. The subjects of the study were familiar with two intensive training sessions per day; therefore, fatigue should not have influenced performance in any tests.
## 4 Distribution Method for Observation Scheduling in the AEOS Constellation
### The Decentralized Framework of PI
Apparently, if \(c\) is the neighbor of \(s\), or \(H[s,c]=1\), we have \(u_{sc}=\tau_{r}\); otherwise, \(u_{sc}\) is equal to maximal timestamp \(u_{hc}\) where \(H[s,h]=1\). The timestamp list \(\mathbf{u}_{s}\) records the latest message exchange time, and the lists \(\mathbf{Q}_{s}\) and \(\mathbf{Z}_{s}\) together represent a schedule solution. These three lists can be continuously updated through local communication with other satellites. In particular, suppose \(Z_{sg}=c\in S\). Then, AEOS \(s\)_believes_ that at least at time \(u_{sc}\), target \(g\) is scheduled to satellite \(c\) and the significance value of \(g\) with regard to \(c\) is \(Q_{sg}\). Initially, we set \(\mathbf{a}_{s}=\emptyset\), \(Z_{sg}\rightarrow\infty\), and \(Q_{sg}=0\) for each AEOS \(s\) and every target \(g\). Generally, as shown in Figure 5, the decentralized PI framework contains two phases: _target inclusion_ and _consensus and target removal_; they are both running concurrently on each satellite. Specifically, the task inclusion phase determines whether to add a target to its current target sequence, and different significance values may result for a specific target at the end of this phase. Thus, the second phase is required, which has two parts: (i) _consensus_, where a consensus significance value list is reached among all satellites, and (ii) _task removal_, where each satellite removes the problematic targets from its current sequence. Note that only the consensus part exchanges messages among satellites using ISLs, but the other procedures are performed independently. The above two phases are executed iteratively. When no change can be made to the obtained sequences for a period of time, the PI algorithm running on each satellite ends. Figure 5: The framework of the PI algorithm.
## 4 Experiments
### Experimental Results and Analysis
#### 4.5.3 Model Interpretability with SHAP  
To better understand the decision-making process of the IGWO-MALSTM model, SHAP (SHapley Additive exPlanations) was employed (implemented using the SHAP Python package, version 0.41.0) to analyze feature importance. As shown in Figure 10, features from the most recent time step (e.g., 9_High, 9_Open, 9_Low) have the most  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Models** & **MSE** & **MAE** & **MAPE** & **R\({}^{2}\)** \\ \hline IGWO-MALSTM & 2.63 & 1.091 & 3.70\% & 0.997 \\ DE-MALSTM & 3.12 & 1.254 & 4.01\% & 0.995 \\ GA-MALSTM & 4.05 & 1.615 & 5.26\% & 0.993 \\ BO-MALSTM & 4.74 & 1.667 & 6.04\% & 0.993 \\ \hline \end{tabular}
\end{table}
Table 8: Comparative performance of IGWO and other optimization algorithms in MALSTM hyperparameter tuning.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Data** & **Models** & **MSE** & **MAE** & **MAPE** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{rebar and HRC futures} & LSTM & 4.52 & 1.587 & 4.47\% & 0.993 \\  & IGWO-LSTM & 3.14 & 1.383 & 2.96\% & 0.994 \\ \cline{1-1}  & MALSTM & 5.37 & 1.982 & 3.89\% & 0.992 \\ \cline{1-1}  & IGWO-MALSTM & 2.63 & 1.091 & 3.70\% & 0.997 \\ \hline \end{tabular}
\end{table}
Table 9: Ablation study results on Rebar and HRC futures data (Shanghai Futures Exchange, December 2020–March 2023).  
significant impact on the model's prediction of closing price. This indicates the model's reliance on recent price movements. In contrast, technical indicators such as MACD, DEA, and DIF show relatively low contribution, suggesting that the model prioritizes raw price information over derived indicators. Moreover, the color distribution reveals that both high and low values of a feature can influence predictions in different directions, highlighting the nonlinear nature of the model's decision boundaries.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
###### Abstract  
Current tourism route recommendation systems often overemphasize popular destinations, thereby overlooking geographical accessibility between attractions and the experiential coherence of the journey. Leveraging multidimensional attribute perceptions derived from tourist reviews, this study proposes a Spatial-Semantic Integrated Model for Tourist Attraction Representation (SSIM-TAR), which holistically encodes the composite attributes and multifaceted evaluations of attractions. Integrating these multidimensional features with inter-attraction relationships, three relational metrics are defined and fused: spatial proximity, resonance correlation, and thematic-sentiment similarity, forming a Tourist Attraction Multidimensional Association Network (MAN-SRT). This network enables precise characterization of complex inter-attraction dependencies. Building upon MAN-SRT, the Tourism Sentiment Chain (TSC) model is proposed that incorporates geographical accessibility, associative resonance, and thematic-sentiment synergy to optimize the selection and sequential arrangement of attractions in personalized route planning. Results demonstrate that SSIM-TAR effectively captures the integrated attributes and experiential quality of tourist attractions, while MAN-SRT reveals distinct multidimensional association patterns. Compared with popular platforms such as \"Qunar\" and \"Mafengwo\", the TSC approach yields routes with enhanced spatial efficiency and thematic-sentiment coherence. This study advances tourism route modeling by jointly analyzing multidimensional experiential quality through spatial-semantic feature fusion and by achieving an integrated optimization of geographical accessibility and experiential coherence in route design.  
theme classification; viewpoint extraction; multidimensional correlation relationships; attraction association network; tourism route recommendation
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 3 System Model, Problem Formulation and Proposed Scheme
### Proposed Scheme
FBS 1 in Figure 3. If the averaged value of accumulated RSSI of uplink pilot transmitted from the switched off FBS is larger than \(\delta\), i.e., Equation (7) is satisfied, the corresponding FBS is turned-on, cf. FBS 2 in Figure 3.  
```
1: Set \(\mathbb{A}=\mathbb{N}\) and \(\mathbb{I}=\emptyset\)
2:repeat
3: Accumulate LOC\({}_{i}(t)\) for \(M\) time instants
4:If (6) is satisfied for active FBS \(j\)then
5:\(\mathbb{A}=\mathbb{A}\setminus\{j\}\) and \(\mathbb{I}=\mathbb{I}\cup\{j\}\)
6:If \(\mathbb{I}\in\left[\sum\limits_{i=1}^{K}R_{i,t}(\mathbb{A})\right]<R_{thr}\)then
7:\(\mathbb{A}=\mathbb{A}\cup\{j\}\) and \(\mathbb{I}=\mathbb{I}\setminus\{j\}\)
8:If (7) is satisfied for inactive FBS \(k\)then
9:\(\mathbb{A}=\mathbb{A}\cup\{k\}\) and \(\mathbb{I}=\mathbb{I}\setminus\{k\}\)
```  
**Algorithm 1** On-Off power control of femto-cell base station (FBS) based on the level of contribution (LOC)  
Figure 3: Procedure of proposed on–off power control scheme, LOC: level of contribution.
## 4 Discussion
### Synchronized Walking Avaatar and Audio Cues on Gait Guidance
The real-time synchronized system offered personalized gait guidance by continuously adapting to each participant's unique walking pattern. By providing real-time feedback, the system ensured that the guidance was closely aligned with the individual's specific gait characteristics, enhancing the effectiveness of gait correction and rehabilitation. Furthermore, the study observed improvements in gait parameters such as step length, cycle time, and walking speed, indicating that the combined use of visual and auditory cues could contribute more efficiently to gait rehabilitation. These findings suggest that incorporating such a system into rehabilitation protocols may lead to better outcomes for elderly individuals undergoing gait rehabilitation. Moreover, this approach has the potential to improve the overall quality of life for elderly patients, enabling them to maintain greater independence and engage more fully in daily activities.