esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)
### Control Unit  
The control unit is activated by the control signal, which is one of the primary inputs to the filter architecture. The control unit of the HEVC DBF has a finite state machine to administer all the operations to handle the deblocking filtering process. When the control signal is not active, all the filtering process is deactivated and, thus, the modules are turned off. Hence, the DBF architecture consumes less power. When the enable signal is active, the control unit triggers the state machine and activates the filtering process in five stages. Five stages--memory read, parameter computation, filter determination, filtering, and memory write--are managed by the state machine during the filtering process. The pixel data are fetched from the external memory, which is outside the filter architecture, after the finite state machine is enabled. Then, 4 \(\times\) 4 blocks (128 bits) of pixel data are fetched for each clock cycle from the external memory. Figure 4a depicts the sequencing of data fetch from the external memory for a largest coding unit (LCU), which is a 64 \(\times\) 64 block, whereas Figure 4b,c depict the sequencing of data fetch from the external storage for a 16 \(\times\) 16 block. The state machine activates the filter unit and the BS calculation unit by generating control signals once four 4 \(\times\) 4 blocks of pixel data are in place.  
Figure 4: (a) Sequence of read for an LCU; (b,c) sequence of read for a 16 \(\times\) 16 block.  
Figure 3: Architecture of V-DPEDBF for HEVC [40].  
The 16 \(\times\) 16 luma block's vertical edges V1 and V2 are filtered in parallel in accordance with the determined BS value. Blocks 1 through 8 of the filtered pixel data are then transposed and stored in the internal memory. The filter unit is triggered once more to filter the edges V3 and V4 once the twelfth block of data is accessible. Blocks 9 through 16 of the vertically filtered data are eventually transposed and stored within the PEDBF architecture. After vertical filtering, the horizontal filter is applied by reading the pixel data that are stored within the PEDBF. The internal RAM receives the appropriate signals from the control unit to perform the required operation. H1 and H2 horizontal edges are parallel-filtered and then the filtered data are transposed. The frame memory, which is located external to the architecture, is subsequently written with the filtered data. The horizontal edges H3 and H4 are parallel filtered in a similar manner. After that, they are written to the external frame memory after being transposed. The Chroma Cb and Cr blocks filter the vertical edges (V5 and V6) first, followed by the horizontal edges (H5 and H6) using the same process. The filtered pixel data are subsequently saved in the external storage as 4 \(\times\) 4 blocks, i.e., 128 bits for each clock cycle.
## 5 Simulation Model with Human Behavior
### Simulation Model
#### 5.2.2 Service Decision Process  
The process of deciding on a service action is shown in Figure 11. The agent first checks whether it already has an offer for its service on the market; if it does not and it is not in the process of providing the service, it decides to create an offer. If it already has a set offer, it first calculates the average price of the same type of service offers. It then checks whether the price of its offer is higher than the average price multiplied by the price weight \(w_{price}\). If it is higher, the agent decides to delete the offer and immediately moves to the next step in creating an offer. Otherwise, it checks whether the offer has lasted longer than the time for pending offers \(t_{offerPending}\) and decides to either delete the offer and set a new offer, or to do nothing.  
Figure 11: Flowchart of the service action decision-making process.  
The action of creating a service offer is performed by an agent by first determining the price offered for the service and then publishing the service on the market. When setting the price, the agent first checks whether there is an offer for other services of the same type. If such an offer exists, it multiplies the average price of the existing service offer(s) by the coefficient \(k_{existOffer}\) and saves the average service price. If no current offers of the same type exist, the agent checks the saved previous average service price and multiplies it by the coefficient \(k_{previousOffer}\). If the agent does not have the previous average price (e.g., at the beginning of the game), it multiplies its account balance by the coefficient \(k_{emptyOffer}\). The selection of an offer for deletion is the same as in the service action decision-making process; the agent checks whether its offer has lasted longer than the time of the pending offer \(t_{offerPending}\) and deletes its offer if it meets this condition. Immediately after the offer is deleted, it repeats the described offer creation process.
# Utilizing Graphite Waste from the Acheson Furnace as Anode Material in Lithium-Ion Batteries
###### Abstract  
This study investigates the potential of graphite waste (GW) from the Acheson furnace as a sustainable and cost-effective anode material for lithium-ion batteries (LIBs). Conventional anode materials face challenges such as energy-intensive production processes and reliance on virgin graphite resources, leading to high costs and environmental concerns. GW from the Acheson furnace, which already possesses high carbon purity (98.5%-99.9%) and crystallinity (93.5%), offers a promising alternative by eliminating the need for graphitization and extensive purification. Through spheronization and carbon coating, GW was successfully optimized to achieve electrochemical properties comparable to commercial anode materials (CAM), including an initial Coulombic efficiency of 85.1% and a specific capacity of 348.9 mAh/g. These findings suggest that GW from the Acheson furnace represents a viable pathway toward cost-effective and environmentally friendly LIB anodes.  
graphite waste; Acheson furnace; spheronization; carbon coating; lithium-ion battery; anode
## 2 Related Work
The results achieved a packet delivery rate of 89.8% for the normal operation phase and 95.7% for the network formation phase. The authors in [30] developed a library for the implementation of a LoRa mesh network protocol. The protocol maintains a routing table in every node that is updated through the routing messages. Tests were carried out using the proposed LoRa mesh library with ESP32 microcontroller boards. Three different topologies were evaluated to obtain PDR and latencies and control overhead. The nodes did not need to connect to a gateway to communicate among themselves. The validity of the proposal was demonstrated. There have also been attempts to provide mesh solutions compatible with LoRaWAN specifications. Specifically, the authors in [31] presented a hybrid infrastructure that joined LoRaWAN and LoRaMesh to provide side management as a service. While the LoRaWAN and LoRaMesh networks were able to reach a coverage of 2.35 km and 600 m, respectively, the proposed hybrid solution attained coverage of 3 km for one-hop transmissions. This solution was also able to provide service to up to 1023 devices. Furthermore, [PERSON] et al. implemented a relay node to enable multi-hop LoRaWAN transmission [32]. This device, called e-node, operates transparently and is compatible with the LoRaWAN standard. The proof of concept prototype has been validated for industrial applications at the highest data rates available in the LoRaWAN specification. The results of the tests under consideration included link quality metrics (SNR and RSSI) and delay measurements. A comparison of the existing proposals on hybrid LoRaWAN implementations has been provided in Table 1. The solution presented in this work differs from the former approaches in that it is fully compliant with LoRaWAN and also allows for coexistence and simultaneous operation in star and mesh topologies with flexible topology modifications. Topologies can be autonomously changed with a topology manager that evaluates the state of the network through the physical link quality metrics included in LoRaWAN frames, allowing for a dynamic network configuration.
## 2 Material and Methods
### The Optimization Model
#### 2.1.1 The Fitness Function  
The fitness function corresponds to the objective function of the problem, which in this type of problem corresponds to the total cost of a given solution, represented by \(Cost\ (S)\) in Equation (3). Considering the work of Goncalves and Resende [33], with the necessary adaptations and inclusion of new specificities, the layout was defined by the coordinates of the centroid (\(x_{i}\), \(y_{i}\)), and the horizontal (\(w_{i}\)) and vertical (\(l_{i}\)) dimensions of each zone \(i\) which, in turn, are sized according to the layout dimension and, consequently, depend on the number of areas, \(N_{z}\). Each solution corresponds to the location of \(M\) areas in \(R\ (\geq M)\) positions [33]. \[Cost\ (S)=\sum_{i}^{M}C_{a}(i,S(i))+\sum_{i}^{M}\sum_{j}^{M}[C_{g}(i,j)\ D(i,j)] \tag{3}\]  
\[C_{a}(S(i))=Z\sum_{i}^{M}N_{i} \tag{4}\]  
where the first term represents the sum of all costs, \(C_{a}\) (units: m or J-m/s depending on the type of occupational risk) related to the shared zones between the productive areas, which are used for moving materials, products, and people. This term takes into account the general occupational risks (without units) or ergonomic risks (Joule) given by a factor \(Z\) imputed to each common zone, \(N_{i}\). The second term is the sum of all costs \(C_{g}\) (units: m or J-m/s depending on the type of occupational risk) related to the occupational risk loads existing in each location area \(S(i)\), whether in the case of general (without units) or ergonomic risks (Joule/second), taking into consideration the Euclidean distance between two zones \(d(ij)\) (Equation (5)), which can be considered in meters (\(m\)) as a practical aspect. The fitness function aims to determine the minimum cost in consonance with the objective function. \[D(i,j)=\sqrt{\sum_{i}^{j}\bigl{(}x_{i}-x_{j}\bigr{)}^{2}+\bigl{(}y_{i}-y_{j} \bigr{)}\bigr{)}^{2}}i,j\in M \tag{5}\]The objective function (Equation (6)) minimizes the total cost, in terms of occupational risks, using the appropriate distance norm. Thus, the objective function is given by Equation (5):  
\[Min\ Cost\ (S)=\sum_{i}^{M}C_{a}(i,S(i))+\sum_{i}^{M}\sum_{j}^{M}[C_{g}(i,j)\ d(ij)] \tag{6}\]  
The model uses the additional parameters \(P_{i,j},Q_{ij}\), which are binary variables used to model the non-overlapping constraints, where:  
If \((P_{ij},Q_{ij})\) is equal to (0,0), then the facility \(i\) is forced to move to the right of \(j\);  
If \((P_{ij},Q_{ij})\) is equal to (1,0), then the facility \(i\) is forced to move to the left of \(j\);  
If \((P_{ij},Q_{ij})\) is equal to (0,1), then the facility \(i\) is forced to move to above of \(j\);  
If \((P_{ij},Q_{ij})\) is equal to (1,0), then the facility \(i\) is forced to move below of \(j\). \(M_{x}\) _and_\(M_{y}\) are parameters that aim to define the upper bounds on the horizontal and vertical planes between any two facilities, respectively. \(d_{ij}^{x}\) _and_\(d_{ij}^{y}\) are variables that represent the distances between the facilities \(i\) and \(j\) along the \(x\)- and \(y\)-axes, respectively.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods
### Performance Comparison Experiments  
The performance comparison results of the ICSF and mainstream alternative algorithms in both single fusion tasks and joint fusion and registration tasks are presented in Table 4. In this performance comparison experiment, 100 sets of images were randomly selected from the FLIRv2 dataset for validation. The resolution of these images is 1800 \(\times\) 1600. To ensure consistency in the experimental setup, the proposed algorithm and five other mainstream alternative algorithms were deployed on the Raspberry Pi 5 platform, with the experimental environment and configuration parameters matching those of the previous experiments. The mutual information (MI) metric was used to evaluate the quality of image fusion and registration, while the computational cost (GFLOPs) and the number of parameters (\(N_{p}\)) were employed to assess the computational and storage consumption of the algorithms. Additionally, the frames per second (FPS) metric was utilized to quantify the real-time processing speed of the algorithm deployment.  
The proposed algorithm achieved the best fusion quality within its category and the second-best computational cost, reaching over 10 FPS in actual deployment on the Raspberry Pi 5. ICSF achieved the optimal balance between fusion quality, computational cost, and speed in the registration and fusion category, with relatively fewer parameters. The algorithm was processed at over 8 FPS during the actual deployment on the Raspberry Pi 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Preprocessing Methods**} & **[EMAIL_ADDRESS] (M3 FD)** & **[EMAIL_ADDRESS] (LLVIP)** \\ \cline{2-3}  & **+** & **+** \\ \hline MIX & 69.5 & 38.3 \\ ReCoNet & 66.6 & 50.7 \\ CDDFuse & 71.2 & 57.2 \\ SuperFusion & 68 & 55.9 \\ PSFusion & 71.4 & 57.4 \\ Ours & **72.5** & **58.4** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of object detection experiments with the proposed algorithm and mainstream alternative algorithms as preprocessing methods on M3 FD and LLVIP datasets. **Bold** and underlined values represent the best and second-best results, respectively. Here, a “\(+\)” indicates that larger metric values are better.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multirow{2}{*}{**Salt, Plasticizer**} & \multirow{2}{*}{**Solvent**} & \multirow{2}{*}{**T\({}_{\text{g}}\)(\({}^{\circ}\)C)**} & & & \multicolumn{2}{c}{**Stability vs.**} & \multirow{2}{*}{**Reference**} \\  & & & & & & & **Li\({}^{+}\)/Li (V)** & \\ \hline
**PEO** & LiTFSI & ACN & \(-40\) & \(4\times 10^{-7}\) & \(0.40\) & \(4.8\) * & Cheng [18] \\ \hline \multicolumn{8}{c}{**Polyether-Based**} \\ \hline
**PEO, epoxy resin** & LiClO\({}_{4}\) & none & n/a & \(\sim\)\(10^{-6}\) & n/a & n/a & Changzhi [19] \\ \hline
**DGEPEG, TGEG** & LiClO\({}_{4}\) & none & \(0\) & \(\sim\)\(10^{-5}\) & n/a & n/a & Wu [14] \\ \hline
**DGEPEG** & LiDFOB & none & \(-43\) & \(8.9\times 10^{-5}\) & n/a & \(4.5\), LFP & Cui [7] \\ \hline
**DGEPEG** & LiTFSI, LiBF\({}_{4}\) & none & \(-50\) & \(1.1\times 10^{-4}\) & \(0.23\) & \(5.5\), LFP and & \\  & & & & & & & NMC & Nair [10] \\ \hline
**DGEPEG, PEGDA** & LiTFSI & none & \(-37\) & \(5.3\times 10^{-5}\) & n/a & \(4.7\), LFP & Duan [20] \\ \hline
**PTHE, ECC** & Ca(NO\({}_{3}\))\({}_{2}\) & none & \(-7\) & \(\sim\)\(10^{-4}\) & \(0.36\) & n/a & Wang [16] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermal and Electrochemical properties of Epoxide-Based Solid Polymer Electrolytes.
## 4 Comparative Experiments
### Simulation
#### 4.1.3 Cycle Number Selection  
In theory, the more training repetitions that are used, the more detailed the study of data characteristics is, and the higher the classification accuracy is. There are two ways to select cycle times: a value can be set based on experience, or a minimum value for the total classification error can be set, and the program will select the number of training cycles. The optimal cycle times for different data sets or different network initial values are often different. Figure 13 shows the experimental results for the optimal cycle times based on the learning rate for the data set of the University of Pavia.  
As seen in Figure 13, when the number of network training cycles is less than 350, the classification accuracy increases with the increase in the number of cycles. When the number of cycles is greater than 350, the classification accuracy increases with the number of cycles, but the rate of increase has obviously slowed. The greater the number of cycles, the greater the classification accuracy, but the training time increases correspondingly. Therefore, it is necessary to consider the two aspects of classification accuracy and training time when selecting the number of cycles.  
Figure 12: The classification accuracy of different network depths varies with training cycles.
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 3 Materials and Methods  
### Dataset Collection  
In the pursuit of developing a privacy-preserving dialogue model framework based on FHE and attention mechanisms, the initial step involves the aggregation of one or more datasets for the purpose of training and evaluation. The selection of datasets must be closely aligned with the research objectives, necessitating not only a sufficient volume of data to ensure the learning of complex linguistic patterns but also high-quality annotations to guarantee effective model training. The sources of datasets in this study are primarily categorized as follows:  
Figure 2: Illustration of the transformer architecture.  
1. Public datasets: **Cornell Movie-Dialogs Corpus**: Comprising over 200,000 conversations extracted from 617 movies, this dataset includes a range of topics from everyday dialogues to emotional expressions and context-specific communications. It is utilized to test the model's capability in processing everyday conversations and emotional understanding. **Ubuntu Dialogue Corpus**: Focused on technical support dialogues, this dataset contains approximately one million conversations from the Ubuntu forums. Its technical nature makes it ideal for assessing the model's performance in handling technical terminologies and complex queries. **Stanford Question Answering Dataset (SQuAD)**: A reading comprehension-based question-answering dataset, SQuAD's questions are formulated based on Wikipedia articles. It tests the model's ability to understand and respond to specific questions based on given text passages. **Twitter Customer Support Dataset**: Comprising customer service dialogues on Twitter across various industries and topics, this dataset is used to evaluate the model's performance in handling real-time, informal interactions and customer service inquiries. **Medical Dialogue Dataset**: Focused on medical consultations, this dataset contains dialogues between doctors and patients, allowing for an assessment of the model's accuracy and privacy protection capabilities in handling professional medical information and sensitive health data.
2. Synthetic data: To enhance the diversity of data, it might also be necessary to generate synthetic data using natural language processing techniques. Such data can assist the model in learning dialogue patterns specific to certain scenarios.
# Thermal Hydrolysis of Sewage Sludge: A Case Study of a WWTP in Burgos, Spain
## 2 Materials and Methods
### Electric Power Generation  
Equation (17) is used to express the electric power generated from the alternators at a set degree of loading.  
\[\text{EP}_{\text{eng-gross}}=\text{ N}_{\text{eng}}*\text{ DL }*\text{ EP}_{\text{eng-nom}} \tag{17}\]  
where EP\({}_{\text{eng-gross}}\) and EP\({}_{\text{eng-nom}}\) are the gross energy values generated by the engines and the rated electrical power at full load per engine (kW). The operation of the engines was assumed to be under continuous mode, with loading levels below 90% regarding the nominal value. This criterion was selected based on reliability considerations as a warrant for long term performance and maintenance costs [46].  
The operation of THP within the WWTP brings, as a consequence, the installation of a series of new additional equipment and an increase in electricity demand. This additional demand is denoted as \"_additional self-consumption_\", and it penalises the gross production of electric power. This extra demand is associated with the post-thickening section, the thermal hydrolysis unit, the boiler, and nitrification-denitrification via nitrite. In contrast, the modifications in operating conditions experienced by the digesters and the dewatering section result in a decrease in energy requirements, and this reduction is denoted as \"_consumption savings_\". These savings positively affect the gross production of electricity due to the decrease in the number of digesters needed and the lower volume of digestate being dewatered. The electrical energy consumption associated with the treatment of centrates has also been considered. This treatment is performed via nitrite for Scenarios 2-4 (when THP is considered) and via nitrate when conventional digestion is evaluated (Scenario 1). The higher energy demand is explained by the presence of air blowers and all accessory equipment for nitrification-denitrification via nitrite, with electricity demand being directly linked to the NH\({}_{4}\)\({}^{+}\) content in the centrates.  
Net electric power generation (EP\({}_{\text{eng-net}}\), kW) is calculated using Equation (18); this value is used to estimate the percentage of self-consumption (%SC, %), using Equation (19).  
\[\text{EP}_{\text{eng-net}}=\text{ EP}_{\text{eng-gross}}-\text{EP}_{\text{ additional}}+\text{EP}_{\text{saved}} \tag{18}\]  
\[\text{\%SC }=\biggr{(}1-\frac{\text{PE}_{\text{eng-net}}}{\text{PE}_{\text{eng- gross}}}\biggr{)}*100 \tag{19}\]  
where EP\({}_{\text{additional}}\) (kW) is the additional self-consumption based on the new equipment needed, and EP\({}_{\text{saved}}\) (kW) is the consumption savings based on specific modifications in the operating conditions of the plant.
# Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation
## 2 State of the Art
### Integration of Cloud-Enabled Robot Systems
One of the attractive fields for the cloud-based application is SLAM, which is computationally expensive to run massively within the robotic platform at every point of the unknown environment. In the case of multiple robots, SLAM can be shared between agents for faster and more accurate mapping. In [31], the authors reported a framework for grid-based FastSLAM implementation. Within this approach, the Handoop cluster (called DAvinCi server) is engaged for map estimation, while a distributed ROS architecture provides sensory data and communication among the robot agents and server. Similarly to our solution, this cloud service acts as the master node, which maintains the list of publishers--ROS nodes on robots. The visual SLAM system C\({}^{2}\)TAM [32] is in line with this approach. The cloud service is realized as a distributed framework serving expensive map optimization and storage. As a consequence, the robot on-board computers have increased autonomy since their role is reduced to a light camera tracking clients.