esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Graphical Representation of LDPC Codes  
LDPC codes can be represented in a graphical way through a commonly named bipartite graph, commonly known as the Tanner graph [2]. As the trellis diagram gives an efficient graphical representation of a convolutional code behavior, so does the Tanner graph in providing an efficient graphical representation of how a LDPC encoder and decoder work.  
The nodes in a Tanner graph are classified in variable nodes and check nodes. In the drawing of the Tanner graph, the following rule must be respected: An edge connects a variable node \(i\) to a check node \(j\) when the corresponding element \(h_{ij}\) in the parity-check matrix \(H\) is a 1. From this it may be deduced that there is a check node for each of the \(m=n-k\) check equations, and that there is a variable node for each of the \(n\) code bits \(c_{i}\).  
As far as irregular LDPC codes are concerned [4], the parameters \(d_{l}\) and \(d_{r}\) specify the maximum degrees of the nodes distributions in their Tanner graphs, defined, e.g., in [17]. In particular, the polynomial \(\lambda(x)\) (\(\rho(x)\)) defines the variable (check) nodes edge-perspective degree distribution:  
\[\lambda(x)=\sum_{i=2}^{d_{l}}\lambda_{i}x^{i-1} \tag{1}\]  
\[\rho(x)=\sum_{j=2}^{d_{r}}\rho_{j}x^{j-1} \tag{2}\]  
A LDPC code rate can be expressed as [18]:  
\[r(\lambda,\rho)=1-\frac{\int_{0}^{1}\rho(x)\mathrm{d}x}{\int_{0}^{1}\lambda(x )\mathrm{d}x}=1-\frac{\sum_{j=2}^{d_{r}}\rho_{j}/j}{\sum_{j=2}^{d_{l}}\lambda_{ j}/j} \tag{3}\]  
The edge-perspective degree distributions \(\lambda(x)\) and \(\rho(x)\) are related to the node perspective degree distributions \(L(x)\) and \(R(x)\) as follows [9]:  
\[L(x)=\sum_{i=2}^{d_{l}}L_{i}x^{i} \tag{4}\]  
\[R(x)=\sum_{j=2}^{d_{r}}R_{j}x^{j} \tag{5}\]  
being,  
\[L_{j}=\frac{\lambda_{j}}{j\int_{0}^{1}\lambda(x)}=\frac{\lambda_{j}/j}{\sum_{ i=2}^{d_{l}}\lambda_{i}/i} \tag{6}\]  
\[R_{j}=\frac{\rho_{j}}{j\int_{0}^{1}\rho(x)}=\frac{\rho_{j}/j}{\sum_{i=2}^{d_{ r}}\rho_{i}/i}. \tag{7}\]
## 4 Results
### Importance of Input Variables to AGB Upscaling Model  
Evaluation of the importance of different variables (i.e., NDVI, P, Rs, Ta, and elevation) to the upscaled AGB with a 5 km spatial resolution is important to understand the effect of variables on the performance of the GBRT models for AGB upscaling. The importance of variables was described by the increased percentage of RMSE relative to the baseline RMSE for which all the variables, including NDVI, P, Rs, Ta, and elevation, were used to develop the GBRT-based AGB upscaling model, indicating to what extent the RMSE increases when a variable is not involved in the model. The larger the value of the increased RMSE is, the more important the variable is to the AGB upscaling model. The GBRT models with five combinations of input variables and MODIS-derived reference AGB with a 5 km spatial resolution during 2009-2011 were trained independently to achieve the best model performance for each combination itself. RMSE values of the GBRT-based upscaled AGB relative to the MODIS-derived reference AGB with a 5 km spatial resolution during 2012-2013 for the five combinations were subsequently calculated to determine the relative importance of each variable. Figure 6 demonstrates that NDVI and P are the two most important variables (32.9% and 26.5%, respectively) in the AGB upscaling model. These results fully reflect the relationship between AGB and NDVI, as well as AGB and P in the TRHR, which is strongly consistent with most study results [30; 31; 32]. [PERSON] et al. [76] demonstrated that in 92.5% of the TRHR area, the vegetation was significantly affected by the severity of the drought during 2000-2018. Resulting from the soil water availability, the strong relationship between AGB and P is important in promoting plant production in dry grassland biomes [77]. In addition, Rs, Ta, and elevation show a relatively low (5.3%, 6.9%, and 4.6%, respectively) importance to the AGB upscaling model. Although the contribution  
Figure 5: Maps of (**a**) MODIS-derived reference average AGB and (**b**) AVHRR-derived estimated average AGB with 5 km spatial resolution by GBRT model in the validation set during 2012-2013.  
of these variables is small, the best estimations can only be acquired using all five variables, because they are important for plant photosynthesis [78].
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Faults in WSNs
#### 3.1.6 Out of Bounds  
The range of sensed data varies beyond the range of the sensing circuit. This fault is discussed by [15].  
The mathematical representation of out-of-bounds fault is Equation (7).  
\[x^{J}>\theta\sigma x^{J}<\theta_{1} \tag{7}\]  
where \(x^{J}\)- is the data-variable threshold for the sensed value.  
\(\theta\) and \(\theta_{1}\)--established the range of data variables.
## 6 PLGA Nanoparticles Functionalization
### Post-Transcytosis NP-Brain Interaction  
As PLGA NPs are able to reach the brain, targeted delivery systems are needed so that NPs can enter the brain cells [97]. As already mentioned, CNS endothelial cells overexpress several receptors, including Tfts, and thus, another promising strategy to improve drug transport to the brain is using monoclonal antibodies (mAbs) to target Tfts. Indeed, Tfts are overexpressed in the brain capillary endothelium and have been demonstrated to undergo endocytosis (receptor mediated transcytosis) through the BBB [187]. Thus, antitransferrin receptor monoclonal antibodies such as OX26 are being used for BBB crossing.  
In fact, OX26 ability to recognize and bind to cells that express the TfR, such as the BBB endothelial cells, has been well described [188]. [PERSON] et al. [109] proposed PEG-PLGA NPs functionalized with two mAbs to deliver encapsulated antiamyloid iA\(\beta\)5 peptide into the brain for AD treatment. On the one hand, PEG-PLGA NPs were conjugated with OX26 mAb to bind to the TfR and cross the BBB; they were conjugated with DE2B4 mAb to bind to the A\(\beta\) peptide, the major constituent of AD plaques, thereby acting as a targeting ligand.  
The resulting PLGA NPs, of mean size of 150-170 nm, were compatible with the parenteral route. The in vitro uptake of PEGylated-PLGA NPs (without mAbs attached) was significantly lower when compared with the uptake of the immune NPs, thereby confirming OX26 ability to increase the cellular uptake of NPs. Furthermore, PLGA NP cellular uptake increases with the density of surface-immobilized antibody [189], explaining the increased cellular uptake of NPs from 8% (with OX26) to 14% (with OX26 and DE2B4). Thus, the formulation of PLGA NPs conjugated with these two antibodies is a promising system to protect antiamyloid peptides from proteolytic degradation and to increase their uptake in the brain. However, the mechanism of internalization of PLGA immune NPs by brain capillary endothelial cells needs to be elucidated, and future work is required to confirm that these NPs are efficient for the treatment of AD in transgenic models. Moreover, as already mentioned, the usage of large proteins such as TfR antibodies can result in problems like synthesis procedure, stability and immunological response [157].
# Particle Swarm Optimization-Based Unconstrained Polygonal Fitting of 2D Shapes
## 3 Unconstrained Polygonal Fitting  
### Problem Formulation  
We assume a binary image \(I\) that represents a 2D shape \(S\). A pixel \(p\) of \(I\) belongs either to the foreground \(S\) (\(I(p)=1\)) or to the background (\(I(p)=0\)). The area \(A\) of the 2D shape is given by  
\[A(S)=\sum_{p\in S}I(p). \tag{1}\]  
We also assume a closed polygonal curve \(P\) with \(N\) vertices \(P_{i}\), \(i\in\{1,...,N\}\), which belong in the 2D space (not necessary on the boundary of \(S\)). The binary image \(I_{P}\) is also defined so that \(I_{P}(p)=1\) at points \(p\) that are inside any of the polygonal curve \(P\) and \(I_{P}(p)=0\), otherwise. Then, the Intersection over Union \(IoU(P)\) metric between the 2D shape \(S\) and \(P\) is calculated as follows:  
\[IoU(P)=\frac{\sum_{p\in S}I_{P}(p)}{\sum_{p\in I}max(I(p),I_{P}(p))}. \tag{2}\]  
Essentially, \(IoU(P)\) is the percentage of the 2D shape points that are under polygonal curve \(P\) divided by the number of 2D points that belong to shape \(S\) either polygonal curve \(P\). The optimal solution of Unconstrained Polygonal Fitting problem is the closed polygonal curve \(P\) that maximizes the \(IoU(P)\).  
\[P^{*}=argmax_{P}IoU(P) \tag{3}\]
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 4 Data Processing
### GNSS and Meteorological Data Processing
#### 4.2.2 Meteorological Data Processing  
First, configure the IP address and port number of the MQTT Broker for the weather instrument and specify the publication topic as \"/sensor/YL8210M200004\". On the subscription side, the data collection and storage system uses the MQTT protocol to connect to the same broker address with authenticated credentials. It receives the weather instrument's JSON-formatted data (including SN code, temperature and humidity, pressure, wind speed, PM2.5, and other parameters). The system automatically timestamps each data entry (format: YYYY-MM-DD HH:MM:SS) using the getting() function, then parses  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Field** & **Data Type** & **Primary Key** & **Introduction** \\ \hline
1 & ID & Int & Not Null & Marking \\
2 & B & Double & Not Null & Latitude \\
3 & L & Double & Not Null & Longitude \\
4 & H & Double & Not Null & Elevation \\
5 & StationName & Varchar & Not Null & Site name \\
6 & Time & Datetime & Not Null & Data collection time \\
7 & X & Double & Not Null & Horizontal position \\
8 & Y & Double & Not Null & Vertical position \\
9 & Sx & Double & Not Null & Horizontal displacement velocity \\
10 & Sy & Double & Not Null & Vertical displacement velocity \\
11 & Sh & Double & Not Null & Perpendicular displacement velocity \\
12 & Ax & Double & Not Null & Horizontal displacement acceleration \\
13 & Ay & Double & Not Null & Vertical displacement acceleration \\
14 & Ah & Double & Not Null & Perpendicular displacement acceleration \\ \hline \hline \end{tabular}
\end{table}
Table 2: GNSS database table design.  
the receiving data time with JSON data and stores it in a MySQL database table. The meteorological database table is shown in Table 3.
## 2 Operating Principle and Structure
### Fabrication  
The fabrication process of the sensor is shown in Figure 3. The main materials required for making the sensor are PVDF film, silicone rubber, PET film, and Printed Circuit Board (PCB) et al.  
1. Make the backing by reverse mould, weld output signal line to signal preprocessing circuit board, and print substrate though the 3D printer.
2. Paste the backing and the film together, as shown in Figure 3a. Attach the backing to the specified position on the substrate, and then paste the PVDF film on the backing with PSA. The two ends of the film are drawn from the through holes to the back of the substrate, and then bent in the same direction.
3. Paste the signal preprocessing circuit board as shown in Figure 3b. Apply an appropriate amount of silver glue to the electrode on the back of the signal preprocessing circuit board, and then combine it with the substrate so that the two electrodes are respectively attached to both ends of the film. After the silver glue is solidified, fix them with the instantaneous adhesive.
4. Cover the protective layer on the PVDF surface, as shown in Figure 3c.  
The fabricated sample was shown in Figure 3d whose size was 9 mm \(\times\) 6 mm \(\times\) 2.75 mm.
## 5 Discussion
### Ablation Studies
#### 5.5.3 Effect of the Attention Block  
Our proposed Att-ResBiGRU model incorporates an attention mechanism that directs focus toward crucial aspects of the learned spatial-temporal features before classification. This mechanism is vital in achieving position invariance by selectively reweighting signals and suppressing noise.  
We conducted an ablation experiment to verify the attention block's significance. We created a modified version of the model that lacked the attention block while keeping the convolutional and residual BiGRU modules intact. This modified model served as the baseline for comparison.  
We evaluated both models on all three benchmark datasets. As shown in Table 11, the Att-ResBiGRU model with the attention block achieved superior performance across all datasets. The F1-score improvements were 0.38% for Opportunity, 1.37% for PAMAP2, and 1.00% for REALWORLD16. These results demonstrate the effectiveness of the attention mechanism in enhancing the model's recognition accuracy.  
Our Att-ResBiGRU model efficiently extracts informative features from wearable sensor data by combining convolutional blocks, a residual BiGRU block, and an attention mechanism. This combination effectively captures spatial relationships, temporal dependencies, and crucial aspects of the data, leading to superior performance in activity recognition tasks.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 4 Congestion Control Schemes for RPL
### Schemes Forwarding Packets via a Single Parent
#### 4.1.3 Residual Energy-Aware Congestion Control  
[PERSON] et al. propose an energy- and congestion-aware routing protocol for smart grid AMI networks [18]. This scheme uses a new metric called ECRM which considers the residual energy as well as ETX and the queue utilization factor of a neighbor node. Each node calculates its own rank by adding a constant MinHopRankIncrease to its parent's rank. Each node \(k\) calculates a metric \(R_{Pro}(p_{k})\) for its parent \(p_{k}\) as follows.  
\[R_{pro}(p_{k})=Rank(p_{k})+ETX(k,p_{k})+\alpha Q(p_{k})+\beta E_{res}(p_{k}) \tag{4}\]where \(Q(p_{k})\) is the queue utilization factor, \(E_{res}(p_{k})\) the residual energy of \(p_{k}\). The design parameters \(\alpha\) and \(\beta\) are set to high values to avoid congested nodes and energy-constrained nodes, respectively. This scheme performs the parent change procedure when the residual energy is lower than a residual energy threshold or the queue utilization is higher than a predefined threshold.  
To evaluate performance, the authors compared their scheme (using ECRM) to the energy-aware scheme (ELPS) proposed by [PERSON] et al. [38] using Cooja simulator. They used the beacon-enabled 802.15.4 MAC protocol. Simulation results show that ECRM outperforms ELPS in terms of the average power consumption and packet delivery ratio.
# An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
## 4 Experimental Results and Analysis
### Performance Comparison  
The graph of Figure 4 depicts the error margin in the three algorithms, i.e., 2-Opt++, NN, and \(R\&R\) algorithms, for the small category. It is evident that the 2-Opt++ algorithm outperforms the two other algorithms most of the time, and \(R\&R\) performs comparatively better than NN. However, it is also worth noting that, in some cases, the \(R\&R\) algorithm yielded results that were even worse than NN, and the variance in the error margin from \(R\&R\) was also considerably higher. On the other hand, the 2-Opt++ algorithm produced better and more consistent results and never exceeded the acceptable margin of 7% for problems belonging to the small category.  
Figure 5 provides a graphical comparison of the time consumed by the \(R\&R\) and 2-Opt++ algorithms in the small category. The results highlight the superiority of our proposed algorithm in terms of the computational time. It is pertinent to mention that the computational time consumed by the 2-Opt++ algorithm is impressive even for considerably larger problems. The computational time performance was improved further after applying the graph compression technique. The graph clearly showed an abrupt increase in the time taken by the \(R\&R\) algorithm for problems with an increasing number of nodes.  
Figure 4: Error comparison of 2-Opt++ algorithm, \(R\&R\), and NN in small TSP instances.
## 4 Results and Discussion
### Societal and Behavioral Parameters  
[PERSON] and [PERSON] [12] measured several parameters to assist in the creation of a forecast model for the adoption of EVs in the future. The weighted average values of the indicators were determined using Equations (7) and (9). The results of the poll were analyzed concerning the status of the transportation sector, the level of social acceptance of EVs, and the infrastructure needed for charging. The authors reported that one car is owned by 25% of the sample, and the same proportion owns two cars. According to 8.1% of respondents, there are more than six cars at home, which is consistent with research by [PERSON] [15]. The results show that roughly 5.8 people are living in each Hail household on average, which is in line with the data reported by [PERSON] et al. [59]. According to [PERSON] et al. [60], there were 7.3 family members in the Qassim region of the KSA. The data reported by [PERSON] [15] are consistent with the average car ownership of 3.15 cars per household, which suggests a strong reliance on personal vehicles for mobility purposes. The weighted average monthly income of the households polled, measured in terms of economic characteristics, is SR 13,350. Just 1.4% of respondents reported receiving a car from their employer, whereas 98.6% of respondents said they had a personal vehicle. The average amount of time spent driving each day is 2.1 h, which indicates a high usage of personal vehicles for everyday commuting and other activities. In terms of money, the respondents pay SR 235 for fuel on average every week.  
In terms of the travel distances, the average daily journey length is roughly 77.55 km, indicating a significant daily reliance on private vehicles for transportation. On highways, the average driving speed rises to 116.5 km/h, whereas in urban areas, it is approximately 66.95 km/h. A greater proportion, 47.1%, stated that they are considering purchasing an EV in the future. This suggests that interest in EVs is expanding, which is consistent with research by [PERSON] [15]. To obtain more data about citizens' behavior regarding EVs and their CSs in Hail, see [PERSON] and [PERSON] [12].
# A Literature Survey on Offline Automatic Algorithm Configuration
## 2 Literature Review
### Model-Free Algorithm Configuration Methods  
Model-free methods are generally based on heuristic rules, and choices for parameter vectors to be investigated are often selected by randomness or experimental design techniques. Although they are faster in execution than model-based tuners, they have very limited extrapolation potential. Research on this topic started in the early 1990s [15; 16] and has lately been gaining momentum. Meta-EAs and ParamILS are in the scope of model-free algorithm configuration methods.
## 1 Introduction
La Paz and Hanoi ranked third with 87% waste collection efficiency [49; 50; 51]. Ulaanbaatar maintained the fourth rank with 85% collection efficiency [52]. Lahore and Rabat claimed the fifth rank with 84% waste collection efficiency [53]. Figure 3: Waste collection efficiency (%) in Low–Middle-Income countries/cities. Figure 2: SAARC countries waste sector assessment: (a) Map of SAARC countries showing selected cities for waste assessment; (b) Assessment of cities against waste management indicators. The low-middle-income cities/countries are further categorized into four groups based on waste collection efficiency [20; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92]. Seventeen countries fall in Group-A with >75% waste collection efficiency, 18 in Group-B with >50% collection efficiency, 17 and 2 in Group-C and Group-D with >25% and \(\leq\)25% waste collection efficiency, respectively, as depicted in Table 1 and Figure 4.  
Long-term planning for the waste management sector depends on factors such as waste generation rate, waste composition, waste transportation cost, resource recovery potential, and disposal methodology--uncertainty conditions due to unreliable waste data held responsible for poor planning in developing nations. Basically, the foundation of the new plan and strategy is based on the accuracy of data related to waste composition and generation rate, which strengthens the decision-makers in overcoming the uncertainties in the system [93]. In this paper, the study of Lahore city is discussed in detail, including the chronological efforts and strategies made by the government to strengthen the SWM sector. In addition, it also examines the current state of solid waste management in Lahore city and ascertains the reasons for new initiatives. This study will also help the readers and policymakers to have reliable information for future efficient planning of the SWM system in the country. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **Waste Collection Efficiency (\%)** & **Low–Middle-Income Counties/Cities** \\ \hline Group-A & 76–100\% & Tehran, Beirut, Tashkent, Kyiv, La Paz, Hanoi, Ulaanbaatar, Lahore, \\  & & Rabat, Phnom Penh, Delhi, Bishkek, Nay Pyi Taw, Dakar, Tunis, \\  & & Colombo, San Salvador, Jakarta, Tegucigalpa, Manila, Harare, \\ Group-B & 51–75\% & Accra, Managua, Honiara, Yamoussoukro, Nairobi, Sao Tome, \\  & & Kathmandu, Algiers, Cairo, Port Vila, Bhutan, Dili \\  & & Lunda, Port-au-Prince, Vientiane, Palikir, Abuja, Port Moresby, \\ Group-C & 26–50\% & Dushanbe, West Bank \& Gaza, Yaounde, Apia, Dodoma, Maseru, \\ Group-D & 1–25\% & Nouakchott and Porto-Novo \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Waste collection efficiency (%) in low–middle-income countries/cities. Figure 4: Waste collection efficiency map of low–middle-income countries/cities.
# LPCP: An efficient Privacy-Preserving Protocol for Polynomial Calculation Based on CRT
## 4 Concrete Construction for Two-Party LPCP
In the third communication round, \(\mathcal{S}\) sends the final ciphertext \(C_{F}\) and hash signature \(hash_{3}\) to \(\mathcal{R}\). \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]0 \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]1 \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]2 \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]3 \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]4 \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]5 \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]6 \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]7  
Dec: In the decryption phase, after receiving the message \((C_{F},hash_{3})\), receiver \(\mathcal{R}\) checks whether \(\mathcal{H}(C_{F})\) equals the hash value \(hash_{3}\). If verified, \(\mathcal{R}\) continues the decryptionprocess. Otherwise, \(\mathcal{R}\) outputs \(\bot\) to abort the execution. \(\mathcal{R}\) obtains the random element \(r\) from the trapdoor permutation and computes the multiplicative inverse of \(r\) that satisfies \(rr^{-1}=1\) mod \(N_{2}\). In the last communication round, \(\mathcal{R}\) shares the result \(Output\) with \(\mathcal{S}\). \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]8 \[C=(r(p^{-1}pm_{q}^{q}+q^{-1}qm_{p}^{p})+r^{\prime}N)\bmod T\]9  
At the end, \(\mathcal{S}\) and \(\mathcal{R}\) obtain the result of polynomial function \(F(m_{1},m_{2})\) without revealing their inputs \(m_{1}\) and \(m_{2}\), which achieves the goal of secure two-party computation.