esa-sceva/satcom-chunk-collection huggingface dataset:
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 5 CoSiWiNeT Evaluation
### Predicted Frequency Offset
#### 5.2.1 Network Trace 1  
The raw frequency offset and filtered frequency offsets by KF, LS, and CoSiWiNeT for trace 1 are shown in Figure 11. The higher variability in raw frequency offset can be seen through multiple spikes though out the duration of trace 1. The visual inspection confirms that all the algorithms manage to curb the raw frequency offset variations.  
To quantify the individual performance of algorithms, a box-plot representation of raw input and outputs of algorithms is shown in Figure 12. The IQR of KF, LS, and CoSiWiNeT 3.2449, 16.7254, and 2.3331 compared to 478.2144 of a raw frequency offset indicates that CoSiWiNeT performs better than KF and LS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Trace 1** & **Trace 2** & **Trace 3** & **Trace 4** \\ \hline Raw & 0.1112 & 0.0538 & 0.1343 & 0.2577 \\ \hline KF & 0.0088 & 0.0051 & 0.0309 & 0.2543 \\ \hline LS & 0.3604 & 0.0287 & 0.3532 & 0.2112 \\ \hline CoSiWiNeT & 0.0053 & 0.0030 & 0.0322 & 0.2515 \\ \hline NTP & 0.0092 & 0.0047 & 0.0313 & 0.2473 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of synchronization algorithms using predicted clock offset (coefficient of variation).  
Figure 11: Trace 1—Comparison of predicted clock frequency offset.
## 4 The Application of Edge Computing System
### Energy Management System  
Studies on EMS primarily focus on objectives such as optimizing energy consumption, enhancing demand response, and improving user satisfaction. Ref. [206] examines the impact of IoT and EC technologies on energy management in smart cities, proposing an approach that enables instant energy control and long-term savings by processing data collected from IoT devices using deep learning techniques. Ref. [207] developed a customizable home energy management system (HEMS) middleware platform that is based on microservice architecture and low-cost hardware. This system, utilizing an edge computing approach, minimizes cloud dependency by performing energy management closer to the user while also achieving high performance with low latency and efficient resource utilization.  
In the context of EMS, various strategies have been proposed to optimize both energy supply and demand, as well as improve efficiency and user satisfaction. For example, ref. [208] focuses on optimizing both supply-side energy allocation and demand-side load response in smart grids using privacy-preserving distributed algorithms. This approach aims to balance energy distribution across the grid while safeguarding users' privacy. Ref. [209] presents an EC architecture that integrates unified learning and deep reinforcement learning (DRL) algorithms to optimize energy consumption in building energy management systems, aiming to improve energy efficiency.  
To address energy cost reduction in residential settings, ref. [210] developed an IoT edge-based energy management system and proposed a middleware architecture for HEMS. This system adopts a load-shifting and reinforcement-learning approach, performing planning based on energy production, consumption, and device runtime. Ref. [211] proposes a three-tier edge-cloud collaboration-based residential energy management (ECCREM) architecture to reduce energy costs and stabilize demand fluctuations. Using Stackelberg and Lyapunov-based pricing algorithms, this architecture optimizes energy demand and aims to increase user satisfaction by up to 20% with priority-based scheduling strategies.
## 3 Numerical Solution
### Time Integration
\begin{split}\dot{\mathbf{q}}_{e,i+1}=\dot{\mathbf{q}}_{e,i}+\Delta t \mathbf{A}^{-1}[&-\mathbf{K}(\Delta t\theta\dot{\mathbf{q}}_{e,i}+ \mathbf{q}_{e,i})-\mathbf{C}\dot{\mathbf{q}}_{e,i}\\ &+\theta\mathbf{p}_{ext,i+1}+(1-\theta)\mathbf{p}_{ext,i}]+\mathbf{A}^{-1 }\Delta t\mathbf{r}_{e,i+1},\end{split}  \tag{24}\]  
\[\mathbf{q}_{e,i+1}=\mathbf{q}_{e,i}+\Delta t\theta\dot{\mathbf{q}}_{e,i+1}+\Delta t(1- \theta)\dot{\mathbf{q}}_{e,i}. \tag{25}\]  
In (24), \(\mathbf{A}\) is  
\[\mathbf{A}=\mathbf{M}_{e}+\Delta t\theta\mathbf{C}+\Delta t^{2}\theta^{2} \mathbf{K}. \tag{26}\]  
Equations (21), (22), (24) and (25) can be collected together and written in the following way:  
\[\dot{\mathbf{q}}_{i+1}=\dot{\mathbf{q}}_{i}+\Delta t\mathbf{B}\mathbf{F}_{i+1}+\mathbf{B} \Delta t\mathbf{r}_{e,i+1}, \tag{27}\]  
\[\mathbf{q}_{i+1}=\mathbf{q}_{i}+\Delta t\theta\dot{\mathbf{q}}_{i+1}+\Delta t(1-\theta)\dot {\mathbf{q}}_{i}, \tag{28}\]with  
\[\dot{q} = \begin{bmatrix}\dot{q}_{k}\\ \dot{q}_{e}\end{bmatrix} , \tag{29}\] \[\mathbf{q} = \begin{bmatrix}\dot{q}_{k}\\ \dot{q}_{e}\end{bmatrix} ,\] (30) \[\mathbf{r} = \begin{bmatrix}\mathbf{r}_{k}\\ \mathbf{r}_{e}\end{bmatrix} ,\] (31) \[\mathbf{F}_{i+1} = \begin{bmatrix}\theta\mathbf{F}_{k,i+1}+(1-\theta)\mathbf{F}_{k,i}\\ -\mathbf{K}(h\theta\dot{\mathbf{q}}_{e,i}+\mathbf{q}_{e,i})+\theta\mathbf{p}_{i+1}+(1- \theta)\mathbf{p}_{i}\end{bmatrix} ,\] (32) \[\mathbf{B} = \begin{bmatrix}\mathbf{M}_{k}^{-1}&\mathbf{0}\\ \mathbf{0}&\mathbf{A}^{-1}\end{bmatrix} . \tag{33}\]  
Equations (27) and (28) describe the motion of the entire system consisting of the block and the beam structure. The displacement vector for the entire system is \(\mathbf{q}\), and \(\mathbf{F}\) is the vector of all forces. The first two terms on the right-hand side of (27) represent the velocity at the next time-step when there is no contact force, and by denoting this velocity as \(\overline{\mathbf{v}}\), (27) is rewritten as  
\[\dot{\mathbf{q}}_{i+1}=\overline{\mathbf{v}}+\mathbf{B}\Delta t\mathbf{r}_{i+1}. \tag{34}\]
###### Abstract  
Grammatical error correction (GEC) has become increasingly important for enhancing the quality of OCR-scanned texts. This small-scale study explores the application of Large Language Models (LLMs) for GEC in German children's literature, a genre with unique linguistic challenges due to modified language, colloquial expressions, and complex layouts that often lead to OCR-induced errors. While conventional rule-based and statistical approaches have been used in the past, advancements in machine learning and artificial intelligence have introduced models capable of more contextually nuanced corrections. Despite these developments, limited research has been conducted on evaluating the effectiveness of state-of-the-art LLMs, specifically in the context of German children's literature. To address this gap, we fine-tuned encoder-based models GBERT and GELECTRA on German children's literature, and compared their performance to decoder-based models GPT-4o and LLama series (versions 3.2 and 3.1) in a zero-shot setting. Our results demonstrate that all pretrained models, both encoder-based (GBERT, GELECTRA) and decoder-based (GPT-4o, LLama series), failed to effectively remove OCR-generated noise in children's literature, highlighting the necessity of a preprocessing step to handle structural inconsistencies and artifacts introduced during scanning. This study also addresses the lack of comparative evaluations between encoder-based and decoder-based models for German GEC, with most prior work focusing on English. Quantitative analysis reveals that decoder-based models significantly outperform fine-tuned encoder-based models, with GPT-4o and LLama-3.1-70B achieving the highest accuracy in both error detection and correction. Qualitative assessment further highlights distinct model behaviors: GPT-4o demonstrates the most consistent correction performance, handling grammatical nuances effectively while minimizing overcorrection. LLama-3.1-70B excels in error detection but occasionally relies on frequency-based substitutions over meaning-driven corrections. Unlike earlier decoder-based models, which often exhibited overcorrection tendencies, our findings indicate that state-of-the-art decoder-based models strike a better balance between correction accuracy and semantic preservation. By identifying the strengths and limitations of different model architectures, this study enhances the accessibility and readability of OCR-scanned German children's literature. It also provides new insights into the role of preprocessing in digitized text correction, the comparative performance of encoder- and decoder-based models, and the evolving correction tendencies of modern LLMs. These findings contribute to language preservation, corpus linguistics, and digital archiving, offering an AI-driven solution for improving the quality of digitized children's literature while ensuring linguistic and cultural integrity. Future research should explore multi-modal approaches that integrate visual context to further enhance correction accuracy for children's books with image-embedded text.
## 5 Experimental Implementation and Results
### Experimental Hybrid A* Results for Map2  
The velocity of each wheel is shown in Figure 11a, and then inverse kinematics is computed for generating the total mobile robot speed, as shown in Figure 11b. Then, Figure 12 presents the planned path (blue path) generated from algorithms and the actual  
Figure 9: The experimental paths that are generated by using different path planning techniques in Map1. The blue line is the planned path, while the red line is the actual path. (**a**) The path planned by using the PRM technique. (**b**) The path planned by using the RRT technique. (**c**) The path planned by using the RRT* technique.  
path (red Path) traveled by the mobile robot. The experimental path of the mobile robot in Map2 can be presented as a series of images, as shown in Figure 13.  
The generated path has a travel distance = 2.47 \(\times\) 10\({}^{2}\) cm, an average velocity of 0.1106 m/s, a travel time of 23.62 s and a maximum acceleration of 0.192 m/s\({}^{2}\). The real final point is varied from the planned as the absolute error point on the X- and Y-axes and is 4.2 cm and 0.9 cm, respectively.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 2 Materials and Methods
### GNSS Satellites and Signals  
Following Chinese Chang'e-5 [28], we simulated a two-day period LLO with an inclination of 43.7\({}^{\circ}\) and an altitude of 200 km starting from 1 March 2023 00:00:00 (UTC). A simulation of the GNSS constellation was performed to generate signals propagating into space. The GPS constellation consists of 31 operational satellites assigned to 6 orbital planes. There are 6 satellites from the IIR block, 7 from the IIR-M block, 12 from the IIF block, and 12 from the III-A block [29], each with a different transmitter radiation pattern, which will be described later. BDS-3 contains 30 satellites, 24 of which are distributed in Medium Earth Orbit (MEO), Inclined Geosynchronous Orbit (IGSO), and Geostationary Orbit (GEO) [30]. GLONASS and Galileo were simulated according to their respective official documents. The spatial distribution of the GNSS is shown in Figure 1. Table 1 lists the frequency bands of each GNSS constellation used in this paper.  
Figure 1: Spatial distribution of each GNSS constellation.
# Measuring Facial Illuminance with Smartphones and Mobile Devices
## 2 Materials and Methods
### Calibration  
The ambient light sensor included in the device itself can be used to calibrate the system for each face and obtain the coefficient \(k^{\prime}\). The light that reaches the light sensor \(E^{0}_{f}\) is initially measured by situating the sensor in proximity to the subject's eyes (Figure 2 left), facing forwards. Next, the device is restored to its natural orientation, facing the user at a comfortable distance (Figure 2 right) and a calibration illuminance, \(E^{0}p\) is determined from the CCD of the device camera (Figure 2 right). The average value \(Y_{m}\) is obtained from the area around the eyes in the image of the face captured by the front camera of the device. The exposure system of the camera adjusts the exposure time, ISO value, and the \(f\)/# automatically when taking the image. With this calibration approach, using the light sensor to calibrate the camera images, all the values in Equation (4) are known except the constant \(k^{\prime}\) which is computed and subsequently can be applied to any other image captured when the device is used under any other illumination conditions for the same face-device combination.  
The proposed methodology was evaluated by using two mobile devices: Samsun S6 Edge Plus (smartphone) and Samsung Galaxy Tab S2 (tablet). Firstly, the illuminance values measured by the ambient light sensors were examined to know the limitation of the methodology proposed due to the changes in the estimation of the \(k^{\prime}\) value during the calibration procedure with the ambient light distribution. To provide a measure of the angular sensitivity of the sensors, illuminance measurements under three light sources with different spatial distributions of light were tested and compared with those obtained with a standard light meter (Konica Minolta Sensing T-10A) that incorporates a wide field cosine-weighted sensor. This test provides a measure of the angular sensitivity of the device sensors relative to that of the illuminance meter, which includes an integrating dome. Secondly, device camera calibration was performed in several face-device combinations with subjects with different skin reflectance to confirm the veracity of Equation (4).
## 6 Performance Analysis
### Data Rate/Bit Rate  
The data rate/bit rate is defined as the quantity of bits that are transferred during transmission between transmitter and receiver. We utilized Equation (3) for calculating the data rate of the LoRa. To calculate the data rate of LoRa, the input parameters, like CR, SF, and BW, are included in the equation. Figure 8 presents the data rate of LoRa from SF 7 to SF 12. The data rate is denoted in terms of bits per second (bps). It can be observed that in every SF, the data rate is in increasing at the point of BW 7 (62.5 kHz) and it exponentially raised after the BW 8 and reached maximum at the BW 10 (500 kHz). We can observe that increasing the SF7 is inversely affecting the data rate, as the data rate declines gradually from SF 7 to SF 12. In SF 7, the data rate of the LoRa touched 22,000 bps and in SF 12 the data rate is limited to 2000 bps. An increase in SF will lead to transmitting a low amount of data during transmission, so SF 7 is the optimal SF that needs to be considered for sending a large amount of the data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Node** & **Tx Power = 2 dBm** & **Tx Power = 5 dBm** & **Tx Power = 8 dBm** & **Tx Power = 11 dBm** & **Tx Power = 14 dBm** \\ \hline
1 & 1.75 mW & 1.76 mW & 2.04 mW & 3.25 mW & 1.95 mW \\
2 & 1.61 mW & 1.59 mW & 2.32 mW & 1.95 mW & 1.88 mW \\
3 & 3.01 mW & 3.02 mW & 2.88 mW & 1.95 mW & 1.88 mW \\
4 & 2.52 mW & 2.53 mW & 2.11 mW & 2.38 mW & 1.88 mW \\
5 & 2.03 mW & 2.04 mW & 2.04 mW & 2.09 mW & 2.48 mW \\
6 & 2.51 mW & 2.46 mW & 2.74 mW & 2.74 mW & 2.48 mW \\
7 & 2.17 mW & 2.18 mW & 1.83 mW & 2.74 mW & 2.33 mW \\
8 & 1.96 mW & 1.97 mW & 2.11 mW & 2.31 mW & 2.56 mW \\ \hline \hline \end{tabular}
\end{table}
Table 6: Energy consumption of nodes ‘3’.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Node** & **Tx Power = 2 dBm** & **Tx power = 5 dBm** & **Tx Power = 8 dBm** & **Tx Power = 11 dBm** & **Tx Power = 14 dBm** \\ \hline
1 & 2.11 mW & 2.11 mW & 2.53 mW & 2.81 mW & 2.71 mW \\
2 & 1.96 mW & 1.97 mW & 2.26 mW & 2.46 mW & 2.26 mW \\
3 & 1.82 mW & 1.83 mW & 2.81 mW & 2.09 mW & 2.86 mW \\
4 & 2.66 mW & 2.67 mW & 3.17 mW & 2.31 mW & 2.71 mW \\
5 & 1.89 mW & 1.90 mW & 1.89 mW & 2.46 mW & 3.08 mW \\
6 & 2.38 mW & 2.39 mW & 2.39 mW & 2.09 mW & 1.88 mW \\
7 & 2.31 mW & 2.32 mW & 2.53 mW & 1.96 mW & 2.86 mW \\
8 & 2.09 mW & 2.04 mW & 2.32 mW & 2.74 mW & 2.26 mW \\
9 & 1.89 mW & 1.90 mW & 3.10 mW & 2.60 mW & 3.31 mW \\
10 & 2.60 mW & 2.61 mW & 2.67 mW & 2.64 mW & 1.73 mW \\
11 & 2.03 mW & 2.04 mW & 2.60 mW & 2.45 mW & 2.18 mW \\
12 & 1.75 mW & 1.76 mW & 1.90 mW & 2.60 mW & 2.03 mW \\
13 & 2.80 mW & 2.81 mW & 1.76 mW & 3.10 mW & 2.78 mW \\
14 & 1.96 mW & 1.97 mW & 2.74 mW & 2.09 mW & 2.63 mW \\
15 & 2.03 mW & 1.97 mW & 2.46 mW & 2.52 mW & 2.48 mW \\
16 & 2.38 mW & 2.39 mW & 2.82 mW & 2.81 mW & 2.33 mW \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy consumption of nodes ‘4’.  
Figure 8: Data rate of LoRa from spreading factor (SF) 7 to SF 12.
# Deep Learning-Enhanced Spectrogram Analysis for
## 5 Discussion
### Future Directions  
Based on these findings, several avenues for future research can enhance the effectiveness of deep learning in biomedical signal classification.  
1. Real-time deployment: The model is implemented on mobile and embedded systems for real-time signal classification in clinical and remote settings [54].
2. Multi-modal Learning: Combining spectrogram-based classification with additional physiological data such as lung sound recordings, ECG, or respiratory motion signals to improve diagnostic accuracy [55].
3. Explainable AI (XAI): Integrating model interpretation techniques to enhance transparency and support clinician trust in AI-driven decision-making [56].
4. Federated Learning for Privacy-Preserving Training: Implementing decentralized learning techniques to train models across multiple institutions while preserving patient data privacy [57].
5. Adaptive classification strategies: Hierarchical or adaptive classification frameworks are introduced where model confidence scores determine classification granularity, refining the differentiation between sub-regions of anatomical structures [58,59].  
These advancements will not only enhance the applicability of deep learning models in clinical decision-making but also establish a foundation for personalized diagnostics, where AI can provide real-time insights tailored to individual patients.  
One limitation of this study is the lack of integration of explainable AI (XAI) techniques. Incorporating XAI methods, such as saliency mapping or Grad-CAM, can help visualize the spectral regions that contribute the most to classification decisions. This would enhance the model's interpretability and support clinical acceptance by providing transparency in decision-making. In future work, the investigation of explainability frameworks tailored for spectrogram-based biomedical signal classification is planned to uncover region-specific signal features.  
This study did not explicitly analyze the effect of signal duration on classification performance. Given that palpation and percussion signals may span different temporal lengths, variations in the time windows could introduce bias during feature extraction. In future work, the sensitivity to signal duration will be evaluated by varying the time window sizes and assessing the model performance on both full-length and truncated signals. This analysis can help standardize input representations for better robustness.
## 7 Performance Analysis
### Simulation Design
In addition, in order to evaluate the load balancing capability of the algorithm, we counted the load of links in the limited area during sun outage to analyze the impact of the detour path on the load of surrounding links.
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 3 Optimization Model
### Fleet Placement Problem
#### 3.2.3 FPP Objectives  
In the fleet placement problem, the objectives are to maximise the users coverage of a station and to minimise the maximum global walking distance between users and fleet stations. With these two objectives, the optimization model is formulated as follows:  
\[\max \sum_{i=1}^{n}\sum_{j=1}^{m}(c_{ij}\times p_{j})\] \[\min z=max_{i,j}(s^{\prime}_{i}\times c_{ij}\times d_{ij}) \forall i,j\] s.t. \[\sum_{i=1}^{n}s^{\prime}_{i}\leq f\] (C1) \[(w-r)c_{ij}\geq s^{\prime}_{i}(w-r)-(d_{ij}\times s^{\prime}_{i}) \forall i,j\] (C2) \[s^{\prime}_{i}\geq c_{ij} \forall i,j\] (C3) \[\sum_{i=1}^{n}c_{ij}=1 \forall j\] (C4) \[z\geq s^{\prime}_{i}\times c_{ij}\times d_{ij} \forall i,j\] (C5) \[z\geq 0 \eqref{eq:c_ij}\] (C6) \[c_{ij}\in\{0,1\} \forall i,j\] (C7) \[s^{\prime}_{i}\in\{0,1\} \forall i\] (C8) \[i\in\{1,\ldots,n\} \eqref{eq:c_ij}\] \[j\in\{1,\ldots,m\}\] (C9)  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Instance Parameters** & **Description** & **Type** \\ \hline \(n\) & Number of street nodes. & \(\mathbb{Z}^{+}\) \\ \(m\) & Number of buildings. & \(\mathbb{Z}^{+}\) \\ \(i\) & Index for street nodes & \(\mathbb{Z}^{+}\) \\ \(j\) & Index for buildings & \(\mathbb{Z}^{+}\) \\ \(S\) & Set of street nodes (potential stations) & \(S=\{s_{1},s_{2},\ldots,s_{m}\}\) \\ \(B\) & Set of buildings (housing users) & \(B=\{b_{1},b_{2},\ldots,b_{m}\}\) \\ \(P\) & Set of population of buildings & \(P=\{p_{1},p_{2},\ldots,p_{m}\}\). \\ \(D\) & Set of walking distances between street nodes and buildings. & \(D=\{d_{11},d_{12},\ldots,d_{j}\) \\  & & \(:d_{ij}\in\mathbb{R}^{+}\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: FPP instance parameters.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Decision Variables** & **Description** & **Type** \\ \hline \(C\) & Set of state of buildings & \(C=\{c_{11},c_{12},\ldots,c_{mn}\},c_{ij}\in\{0,1\}\) \\ \(S^{\prime}\) & Set of state of street nodes & \(S^{\prime}=\{s^{\prime}_{1},s^{\prime}_{2},\ldots,s^{\prime}_{m}\},s^{\prime}_{i} \in\{0,1\}\) \\ \(z\) & maximum global walking distance & \(\mathbb{R}^{+}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: FPP variables.  
C denotes a constraint. Constraint 1 denotes that the number of stations cannot exceed the provided number \(f\) of fleet stations. Constraint 2, 3, and 4 restrict the model to consider any building \(b_{j}\) to be covered by one station and to be calculated only once for user coverage. The building \(b_{j}\) is covered _iff_ there is at least one active station in its proximity (the proximity is defined by \(w-r\)). Constraint 5 finds the maximum walking distance of the active station \(s_{i}\) from all buildings \(b_{j}\) that it covers. Constraint 6 impose the global walking distance to always be positive. Constraints 7 and 8 indicate that there are only two states for street nodes (active: 0, inactive: 1) and buildings (not covered: 0, covered: 1). Finally, constraints 9 and 10 denote the domains of indices \(i\) and \(j\) accordingly.