esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
To test the feasibility of our deep learning receiver approach, each model was evaluated for their respective receiver operations effectiveness, for both Rician and 3 GPP LOS models, before also being evaluated jointly for the achievable end-to-end performance. This section  
Figure 3: GNN architecture for LDPC decoding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Search Space** & **Converging** & **Best** \\ \hline Iterations & \(\{1,3,5,7\}\) & 5 & 7 \\ \hline Hidden Layers & 1–8 & 6 & 8 \\ \hline Aggregation & \{Sum, Mean\} & - & Mean \\ \hline Optimizer & \{SGD, ADAM, LAMB\} & - & ADAM \\ \hline Learning Rate & \(\{\,10^{-2},10^{-3},10^{-5}\,\}\) & - & \(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: GNN hyper-parameter search space.  
will thus be divided into subsections focusing on the coded bit LLR extraction via OFDM demodulation, the LDPC decoding, and finally the end-to-end performance.  
As mentioned in the previous section, each modulation scheme utilizes a variable number of OFDM symbols to reduce the number of padding bits required when varying the employed subcarrier modulation scheme and its associated bits per subcarrier. QPSK utilizes 22 symbols, 16-QAM utilizes 14 symbols, and 64-QAM utilizes 8 OFDM symbols. A comparison of the computational complexity regarding other proposed models can be seen in Table 4. The latency and throughput of the models will be reliant on the optimization of the processor completing the ML accuracy, so BER and model complexity are the reported comparative results.
## 4 Experiments and Results
### Evaluations Metrics  
We used the competitive performance metric (CPM) [28] score for the evaluation of the algorithm, which is a metric used in the LUNA16 challenge list. The score calculates average sensitivity at seven predefined false positives per scan (FP/scan) indices, i.e., 0.125, 0.25, 0.5, 1, 2, 4, and 8, on a free receiver operation characteristic (FROC) curve. Currently, an FP/scan is widely used in clinical practice in the range of 1 to 4.
# Preparation of an Asymmetric Membrane from
## Materials and Methods
### Membrane Performance of the Permeate Flux and the Antifouling Experiment  
The permeability and rejection experiments were carried out with CA membranes using cross-flow filtration. The membranes had an effective area of 22.9 cm\({}^{2}\). Membranes were pre-compacted with a 0.15 MPa for 30 min. After that, the transmembrane pressure (TMP) was lowered to the operating pressure of 0.1 MPa. The pure water flux (J, L\(\cdot\)m\({}^{-2}\cdot\)h\({}^{-1}\)) was calculated by Equation (4):  
\[J=\frac{V}{A.t} \tag{4}\]  
where \(V\) (L) represents the volume of permeate water, \(A\) (m\({}^{2}\)) the effective membrane area, and \(t\) (h) the permeation time.  
The resistance against fouling and the flux recovery ratio (FRR) of the prepared membranes were evaluated as follows: After measuring the pure water flux (\(J_{w1}\)), 500 ppm BSA solution as the protein foulant model was replaced and the permeate flux of BSA solution was recorded (\(J_{P}\)). After permeation of the BSA solution for 60 min, the fouled membranes were rinsed by distilled water for 20 min on a shaking table (150 rpm, 25 \({}^{\circ}\)C). Then, the flux of the cleaned membranes (\(J_{w2}\)) was measured again for 30 min by using Equation (2). After measuring the pure water flux, the BSA solution flux test and pure water flux were measured once again. The FRR was evaluated as exhibited by Equation (5), to determine the antifouling characteristics of the membranes.  
\[FRR(\%)=\left(\frac{J_{w2}}{J_{w1}}\right)\times\,100 \tag{5}\]A UV-vis spectrophotometer (PerkinElmer, Waltham, Massachusetts, USA) was used to measure the BSA concentrations in the feed and the permeate solutions at a wavelength of 280 nm. The BSA rejections (\(R\)) were determined by Equation (6):  
\[R(\%)=\left(1-\frac{C_{p}}{C_{f}}\right)\times 100 \tag{6}\]  
where \(C_{p}\) and \(C_{f}\) (mg/mL) are the BSA concentrations in the permeate and feed solution, respectively. The measurements of the permeation and rejection were repeated for three different membranes to obtain accurate results and minimize the experimental error. To describe the fouling resistance, the total fouling ratio (\(R_{t}\)), reversible fouling ratio (\(R_{r}\)), and irreversible fouling ratio (\(R_{ir}\)) were calculated using the following equations:  
\[R_{t}(\%)=\left(1-\frac{J_{P}}{J_{w1}}\right)\times 100, \tag{7}\]  
\[R_{r}(\%)=\left(\frac{J_{w2}-J_{P}}{J_{w1}}\right)\times 100, \tag{8}\]  
\[R_{ir}(\%)=\left(\frac{J_{w1}-J_{w2}}{J_{w1}}\right)\times 100=R_{t}(\%)-R_{r}( \%). \tag{9}\]
## 3 Background and Previous Related Work
### DLT-Based ITS Related Works
For devices with fewer resources, the proof-of-work consensus technique is replaced with proof-of-reputation. With this setup, only the user with the greatest reputation value can pack resource transactions. The blockchain serves as the repository for the reputation values. Each entity's reputation value is calculated by factoring in both its recent and past achievements. The players are rewarded by a game-theory-based process. The study in [64] details a blockchain-based system that safeguards the anonymity of automobile drivers when they look for and reserve parking spots in advance. The usage of blockchain technology is proposed in this proposal as a means of avoiding the drawbacks of centralization. The blockchain cannot function without the cooperation of parking lot owners even though they may not trust one another. They may include information on parking deals, for instance. Then, the blockchain will serve as a permanent record of all open offers. The private information retrieval (PIR) approach is utilized alongside the blockchain to conceal drivers' whereabouts. When using PIR, drivers can look for parking information in the blockchain without disclosing their intended destination. After receiving a parking offer through the blockchain network, the driver can confirm the reservation with the parking owner in a way that protects their anonymity by using a short, randomizable signature. However, the trusted authority will be able to identify the genuine drivers and take appropriate action, if necessary, because of this signature's short length and randomizability. It is also proposed that drivers have the option of making payments anonymously rather than using conventional card payment systems, which could potentially expose their personal information. A comparative view of these works is presented in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Decentralized** & **DLT Type** & **Consensus Algorithm** & **Resources** & **Contributions** \\ \hline [PERSON] et al., 2019 [58] & Consortium Blockchain & Proof of Work (PoW) & High & Blockchain-based data management using smart contract \\ \hline [PERSON] et al., 2019 [59] & Public Blockchain & Proof of Work & High in RSU, Low in IVs & Blockchain-based trust management and data exchange using smart contract \\ \hline [PERSON] et al., 2019 [61] & Consortium Blockchain & Proof of Work (PoW) & High in Edge Layer, Low in Vehicles Layer & Vehicle data trading blockchain based \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A Comparative Perspective of DLT-Based Previous Works.
# The Effect of Green Areas on Urban Microclimate: A University Campus Model Case
## 1 Introduction
In contrast, this study contributes a more nuanced analysis by incorporating species-level variation (broadleaf, conifers, shrubs, and ivy) and focusing on functional traits such as Leaf Area Index (LAI), canopy density, and surface albedo. Refs. [55; 56] emphasized the role of vertical canopy gradients and leaf-sapwood ratios in regulating plant respiration and energy exchange, while ref. [57] highlighted how spatial variation in LAI correlates with thermal performance in urban parks. Furthermore, studies by [58; 59] stressed the importance of vegetation morphology and structure in seasonal temperature variation yet lacked species-level field assessments across varying microclimatic conditions. By integrating in situ surface temperature measurements and plant-specific morphological data, this study offers a localized yet transferable model for understanding vegetation-climate interactions in humid subtropical zones. In doing so, it addresses a significant gap in the literature by moving beyond generalized vegetation classifications and emphasizing the need for plant-specific, trait-based approaches in designing effective urban greening strategies [60; 61; 62]. This study aims to examine the impact of different types of green areas (broadleaved trees, coniferous trees, shrubs, and vines) on urban temperature regulation at Recep Tayyip Erdogan University Zihni Derin Campus, located in Rize. Surface temperature, humidity, ambient temperature, and wind speed measurements were conducted over a one-year period under various climatic conditions (August, October, January, and April) and at different times of the day (09:00 AM, 01:00 PM, and 05:00 PM). Surface and ambient temperature measurements were taken using an infrared thermometer, humidity measurements were conducted with a hygrometer, and wind speed measure ments were taken with an anemometer. Within the scope of the study, the temperature regulation effect of each type of green area was quantitatively assessed, and a regression analysis was conducted to develop a predictive model capable of estimating the cooling impact of these areas under different environmental conditions. This model highlights the strong cooling effects of broadleaved and coniferous trees while also demonstrating the complementary roles of shrubs and vines. These findings provide a scientific foundation for mitigating urban heat island effects, improving urban living comfort, and supporting sustainable urban design. By emphasizing the importance of green areas in urban planning, this study encourages the enhancement of plant-based designs with the potential to improve the microclimate. Thus, this study fills a critical knowledge gap by providing a trait-based, species-specific approach to urban microclimate regulation, particularly in humid subtropical contexts.
## 4 Bridge Degradation Identification and Model Update Results
### Analysis of Crack Detection Results
#### 4.2.2 Display of Particle Swarm Optimization Results and Crack Detection Results  
As shown in the numerical results on the far left of Figure 16, two parameters were learned and iterated using the particle swarm optimization algorithm. In the density calculation, the optimal values for adjusting the distance threshold between neighboring points and the sparsity threshold were found to be 1.6147 and 0.2368, respectively. Furthermore, after inputting these parameter values into the crack detection program, relatively accurate crack detection results were obtained (the detection results for areas with a higher probability of crack presence exhibited a red-blue distribution similar to the standard values).  
Figure 15: Results of the crack detection program based on different tuning parameter for sparsity threshold: (**A**) Results with a parameter selection of 0.5, (**B**) Results with a parameter selection of 1.0, (**C**) Results with a parameter selection of 2.0.  
Furthermore, a three-dimensional graph related to the parameters of a particle swarm optimization (PSO) algorithm, often referred to as a parameter sensitivity analysis graph, is typically used to visualize the impact of algorithm parameters (such as the number of particles, inertia weight, learning factor, etc.) on the optimization results (such as the optimal solution, convergence speed, computational time, etc.). This three-dimensional graph is usually composed of two parameters (algorithm parameters) and one indicator of the optimization result, forming a cube. Each face of the cube represents a specific value of one parameter, while the body of the cube represents the optimization result under that parameter combination. Consequently, this study further conducted parameter sensitivity analysis through the drawing of the three-dimensional graph related to PSO algorithm parameters.  
Figure 17 presents the three-dimensional graph related to the parameters involved in this study. By observing the data distribution, it was found that the data points exhibit a normal distribution in most areas, with some areas showing clustering, such as (0.8, 1.5, 250). This indicates that the dataset is relatively stable but with strong correlations or outliers. In future research, further data analysis and validation are needed to confirm the validity of these points.
## 3 System Design
### Multicast Tree Generation Procedure
#### 3.5.4 Multicast Routing Information Distribution  
After calculating the up-to-date SPT, MMC will generate the multicast routing entries and distributes them to the related ASR or CR in the SPT. Notice that only MMCs have this authority. Entries installation is very crucial for that too many entries may run out of the routers memory, even cause router paralysis.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 6 The 6G Network Infrastructure to Manage V2X Application Lifecycles
### Cooperative Adaptive Cruise Control (CACC)  
Cooperative adaptive cruise control (CACC) represents a pivotal advance in vehicular communication, aiming to enhance safety and driving convenience. It is a sophisticated vehicular communication mechanism that seeks to bridge the gap between the host vehicle (HV) and the leading vehicle (remote vehicle--RV). By ensuring a safe and adaptive distance, CACC paves the way for smoother vehicular maneuvers and anticipates undetectable behaviors, such as sudden accelerations or decelerations. The CACC use case is versatile, with applicability spanning across various terrains and settings, including urban, rural, and highway environments. The primary objective of the HV in the CACC scenario is to modulate its speed efficiently, aligning its reactions to the behaviors of the RV. Notably, this function retains its efficacy even when not all vehicles ahead can communicate status messages. Two user stories are associated with the CACC scenario:  
* ACC with Status Messages: Incorporating status messages from surrounding vehicles into the existing adaptive cruise control (ACC) framework facilitates enhanced behavioral adaptation.
* ACC with Control Information: This variant of ACC leverages specific control messages from the RV's system, offering preliminary insights into acceleration or deceleration patterns, thereby refining ACC's adaptability.  
The defined service level requirements associated with the CACC scenario are the following:Range: The practical operational distance is determined to be 800 m, considering vehicle speeds and the possible gaps between vehicles.
* Data Transmission: Vehicles are required to regularly transmit kinematic and positional data, with an average data size of 300 bytes.
* Vehicle Density: The system is engineered to accommodate a wide range of traffic scenarios, supporting densities of up to 10,000 vehicles per square kilometer.
* Positional Accuracy: The focus is on precise longitudinal positioning, with a defined accuracy threshold of 0.5 m (X\(\sigma\)).
* Standardization and Regulation: To establish inter-vehicle distances, the CACC framework requires compatibility among various original equipment manufacturers (OEMs), standardized protocols, and regulatory supervision.  
The CACC use case highlights the transformative power of V2X communication in reshaping vehicle safety and the driving experience. As vehicle networks become increasingly complex, such innovations will be crucial in influencing the trajectory of ITS in the future.
## 4 Experimental Results
### Deletion Strategies  
In machine unlearning, the deletion techniques specify which data points are chosen to be eliminated throughout the unlearning procedure. These deletions strategies are  
* Random deletion strategy--A random portion of the training data is chosen using this method to be eliminated. The idea is to simulate a situation where a random user requests that data be deleted. This simple method evaluates the model's ability to extract random patterns from the dataset. For example, randomly deleting images from various classes or arbitrary removal of a subset of numbers (e.g., 100 random photos) [5]. 2. Class-based deletion strategy--This method aims to remove all data points that belong to a specific class (or category). This facilitates the evaluation of the model's ability to retain knowledge of other classes despite forgetting entire datasets. It is useful for removing entire categories from the dataset, such all images of a particular item or all evaluations that express a particular emotion. Examples are removing all images of cars, all images of the number seven, and all reviews that are labelled as \"positive\" [29]. 3. Feature-based deletion strategy--With such an approach, data points are identified instead of labels or classes. For instance, the data in the UCI Adult dataset may not contain any data points with certain demographic features (such as gender or level of education). This type of method is truly useful with tabular datasets (e.g., deleting data belonging to a certain demographic class) and can be used as a guard to comply with data privacy laws. Examples are to wipe away all those data items with the attribute \"education\" set to \"Bachelors\" or a value of \"White\" for the attribute \"race\" [29].
## 5 Applications
### Dynamic Monitoring Model  
The dynamic monitoring model is achieved by incorporating monitoring data such as values acquired from the distributed fiber-optic monitoring systems throughout a time interval into the 3D geological model. Figure 20 shows the compression deformation of the units (sand and silty clay) at a depth interval of 30-40 m throughout the period from 18/7/2019 to 22/9/2020. The target layer exhibits low compressibility with an average vertical deformation of 0.97 mm from July 2019 to September 2020 (annual report on geological safety monitoring of underground space resources at Tongzhou monitoring station in Beijing, 2020), which is much lower than that of upper silty clay.  
Figure 19: The upper (23) and lower silty clay units (43) with Es values larger than 15 derived from the 3D property model.  
Figure 20: Monitoring data (vertical compression) and a preliminary dynamic model of target units at one monitoring site in the study area.  
Taking the above Es data and thickness variations into consideration, the layer of sand and silty clay at depths between 30 m to 40 m seemed to be suitable for underground construction from the aspect of vertical stability in the study area. This will be further explored in an ongoing study using 3D geological models as a framework to construct a dynamic monitoring model in the Tongzhou area.
# A Comparative Analysis of Arabic Text Steganography
## 5 Evaluation Criteria
### Embedding Capacity  
The amount of hidden information in the cover text is called its embedding capacity [117]. This criterion is calculated by applying the following equation.  
\[\text{Embedding Capacity}=\frac{\textit{Secret bits}}{\textit{Cover bits}}\ \times\ 100\]  
Text steganography methods can improve the embedding capacity by increasing the embeddable/usable cover characters (embeddable positions) [56; 118], bits per location [115], and compression techniques [43], as well as merging more than one text feature [114]. The embeddable character/location refers to a character/location with the ability to use in the embedding process. Bits per location indicates the hiding amount per location.  
Arabic text has many features such as kashida, dots, diacritics, and so on, increasing the capacity by merging more than one feature. Besides, compressing the secret bits using compression algorithms reduces the hidden bits amount. This paper evaluates the capacity of Arabic text steganography methods by analyzing the bit per location and the compression techniques used.
## 3 The Methodology Used for the Implementation
### Input Data Analysis  
After the word processing procedures described in the previous subchapter, we obtained the data set prepared for analysis. To perform a more detailed analysis of the data before using to train the models, we used the Wordnet [20] utility in the NLTK package. Specifically, Wordnet was created by Princeton to be able to give meaning, definitions, synonyms, and antonyms to words. Thus, we used Wordnet to mark the synonyms in our data set. This analysis of the meaning of words reduces the number of different terms in the feature matrix and helps analyze the degree of informational load of each term used in model training. The resulting data set was further transformed with a Vectorizer Count [21], which converts a collection of texts to a matrix of token counts and placed in the final form. It was used to further train the models based on it.  
The Bag-of-Words principle [22] is first used, where we extract the main features from the text document with processed posts. This principle is used to emphasize the degree of appearance of words, which means that we are more interested in the occurrence of the terms in the document and not their position. In order to be able to use the most used words as features in the model training, we create an array of such characteristics. The elements of the feature matrix must be clear and concise, as the use of ambiguous features in the training process is not recommended.  
Because the number of terms obtained for the feature set is very large, a compromise must be made so that we only use terms that carry a strong information load. Therefore, in the matrix mentioned above we aggregate the 5000 most used terms. In order to test the ratio between resources and processing time, we tried to run with 6000 and 7000 such terms, respectively, but the performance was poorly improved, resulting in the fact that the remaining added terms did not bring much information in decision making under the algorithm classification.  
Figure 4 shows the occurrence distribution of the first 10 political terms in our data set, which helps in the features analysis for the training of the models.  
Figure 4: Distribution of the first ten political-legislative terms according to their occurrence.  
Further analyzing the rest of the terms, we notice their political and legislative nature. In addition, there are notorious terms debated by the two political orientations, usually in opposition, such as war/peace and debt/prosperity. Besides the political and legislative words, terms such as irresponsible, unacceptable, excuses, and immorality also stand out in the data set.  
Figure 5 depicts the number of occurrences of the selected terms, in correlation with Figure 4.  
The initial data set with the 86,000 tweets that was processed, resulting in the matrix of terms analyzed in this subchapter, are further divided into smaller sets, used for training, testing, and validation. First, 80% of the input data are used to train the models. Afterwards, the next step is to run a test using 90% of the data set, to compare performance.