esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Electricity Consumption Behavior Classification Based on CLB-BER  
The electricity usage behavior classification model framework is illustrated in Figure 3. The framework comprises three primary steps: (1) data collection and preprocessing; (2) the classification of electricity consumption behavior; (3) construction and tuning of the BERT model.  
Firstly, relying on smart meters, a substantial volume of customer electricity usage time-series data is collected and processed to address missing values and outliers. Specifically,  
Figure 3: Model framework for classifying electricity consumption behavior.  
for the missing values present in the data, various methods are employed to impute them, such as linear interpolation, moving average, or other filling methods applicable to time-series data, ensuring data continuity. Outliers in the data, often resulting from sensor failures or data transmission errors, are detected and handled using statistical methods or machine learning algorithms to ensure accurate data correction. Secondly, the improved CLUBS clustering algorithm is applied to classify the electricity consumption behavior. Finally, the pre-trained BERT is introduced and fine-tuned by clustering labels. Then, the intelligent classification model is successfully developed.
## 6 Investigating Several RL-Based Routing Methods
### RLRC  
[PERSON] et al. in [63] have presented a RL-based routing protocol for clustered network (RLRC), which consists of two main steps: K-Harmonic Means (KHM) clustering algorithm and RL-based routing algorithm. In RLRC, each vehicle employs a digital map and a positioning system to find out its speed, position and direction at any moment. Each electric vehicle shares this information through hello messages with adjacent vehicles. This information is applied in the clustering operation. The KHM-based clustering algorithm categorizes electric vehicles into \(k\) clusters. In the clustering process, the cluster head nodes (CHs) are determined with regard to bandwidth and residual energy. In the data transmission process, each cluster member node transfers its data directly to the corresponding CH. Then, the CH forwards the data to its neighboring CH through a RL-based routing algorithm. In the routing operation, the Sarsa-Lambda learning technique has been used to choose the most suitable path between CHs. In this issue, the whole network expresses the learning environment, and each node is the agent. The state set includes neighboring CHs, and the action set represents the choice of a next-hop CH. Also, the reward value is computed with regard to the link status parameter, which is obtained based on the bandwidth factor and the inverse link duration (ILD). Figure 18 shows the learning structure in RLRC. In this approach, the Q-value is renewed by hello messages and the route request (RREQ) messages. CHs periodically broadcast hello messages to their neighboring nodes. This message contains the highest Q-value obtained for sending data through this neighboring node to the destination.  
Figure 17: Learning structure in ECTS.
# Current Trends in Orthognathic Surgery in Poland--A Retrospective Analysis of 124 Cases
###### Abstract  
The analysis aims at assessing the current trends in orthognathic surgery. The retrospective study covered a group of 124 patients with skeletal malocclusion treated by one team of maxillofacial surgeons at the University Hospital in Zielona Gora, Poland. Various variables were analysed, including demographic characteristics of the group, type of deformity, type of osteotomy used, order in which osteotomy was performed and duration of types of surgery. The mean age of the patients was 28 (ranging from 17 to 48, SD = 7). The group included a slightly bigger number of females (59.7%), with the dominant skeletal Class III (64.5%), and asymmetries were found in 21.8% of cases. Types of osteotomy performed during surgeries were divided as follows: LeFort I, segmental LeFort I, BSSO, BSSO with genioplasty, LeFort I with BSSO, LeFort I with BSSO, LeFort I with BSSO and genioplasty, segmental LeFort I with BSSO, isolated genioplasty. Bimaxillary surgeries with and without genioplasty constituted the largest group of orthognathic surgeries (49.1%), and a slightly smaller percentage were one jaw surgeries (46.7%). A statistically significant correlation was found between the type of surgery and the skeletal class. In patients with skeletal Class III, bimaxillillary surgeries were performed significantly more often than in patients with skeletal Class II (57.5% vs. 20.0%; \(p\) = 0.0002). The most common type of osteotomy in all surgeries was bilateral osteotomy of the mandible modo Obwegesser-Epker in combination with Le Fort I maxillary osteotomy (42.7%). The order of osteotomies in bimaxillary surgeries was mandible first in 61.3% of cases. The longest surgery was bimaxillary osteotomy with genioplasty (mean = 265 min), and the shortest surgery was isolated genioplasty (mean = 96 min). The results of the analysis show a significant differentiation between the needs of orthognathic surgery and the types of corrective osteotomy applied to the facial skeleton.  
orthognathic surgery; dentofacial deformities; Le Fort I osteotomy; genioplasty; bimaxillary surgery; bilateral sagittal split osteotomy +
Footnote †: journal: Applied Sciences
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 6 Infrastructure Control Policy
### Local Policy: Reinforcement Learning Scaling Policy
#### 6.2.1 Learning the Optimal Policy  
At each decision time \(i\), the RL agent has to decide which action to take. Specifically, being in state \(s\), the agent relies on its _policy_ to pick an action. A policy \(\pi:\mathcal{S}\rightarrow\mathcal{A}\) is a function that associates each state \(s\) with an action \(a\in\mathcal{A}(s)\). Ideally, the agent relies on the _optimal_ policy \(\pi^{*}\), which minimizes the long-term discounted cost, and satisfies the Bellman optimality equations (see [14]). Unfortunately, we cannot explicitly determine the optimal policy without a complete and accurate system model formulation. The presence of unpredictable dynamics in the system forces the agent to _learn_ the policy to use at run-time.  
Algorithm 1 illustrates the general RL scheme: the \(Q\) functions are first initialized (setting all to zero will often suffices) (Line 1); then, at each step \(i\), the agent chooses an action \(a_{i}\) based on the current estimates of \(Q\) (Line 3), observes the incurred cost \(c_{i}\) and the next state \(s_{i+1}\) (Line 4), and updates the \(Q\) function based on what it just experienced during step \(i\), that is, the tuple \((s_{i},a_{i},c_{i},s_{i+1})\) (Line 5).  
Different RL techniques differ for the actual learning algorithm adopted, and on the assumptions about the system. For example, the well-known Q-learning algorithm is a _model-free_ learning algorithm which requires no knowledge of the system dynamics, and only learns by direct experience. By doing so, Q-learning often requires much time for convergence, and thus cannot be easily adopted in real systems, as we also have shown in [41] for the simple case of a single DSP operator in isolation. For this reason, we present a _model-based_ approach, which basically improves its estimates of the entire system dynamic over time, and accordingly updates the \(Q\) function.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 6 Implementations and Use Cases
### New Mobile Edge Applications
#### 6.2.4 Robotics and Industry  
5G can be used to automate the work conducted in factories using robotic devices and real-time big data analysis at the mobile edge within the factory. Robust 5G networks provide technology advancements critical to factory automation such as high availability, low-latency, and resilience against attacks as provided by a dedicated slice of the network [122]. 5G and mobile edge computing enable the automation of critical applications, such as quality inspection of products [123]. Automated factory systems can then leverage DL to manage the offloading of computational tasks to the local edge network in energy- and resource- efficient ways [122; 124]. In the medical sphere, 5G paired with robotics can enable remote medical examination or surgeries with ultra-low-latency remote control with tactile feedback to remote surgeons.
# Tree-Structured Regression Model Using a Projection Pursuit Approach
## 2 Regression Tree and Projection Pursuit
### Projection Pursuit  
Projection pursuit is a method of finding interesting low-dimensional projections of high-dimensional data by optimizing a predetermined criterion function, called a projection pursuit index. This idea originated in [22], and the authors of [23] coined the term \"projection pursuit\" as a technique for exploring multivariate data. It is useful in an initial data analysis. The method can also be used to reduce multivariate data to a low-dimensional but \"interesting\" subspace.  
The projection pursuit classification tree [24] is a classification tree that uses projection pursuit indices to separate classes. The usual tree-structured classification finds a rule to separate the data into two groups using impurity measures that determine the degree of purity in the two groups in terms of classes. A projection pursuit classification tree, on the other hand, finds a rule to separate classes into two groups. This rule uses the best projection to separate the two groups of classes with various projection pursuit indices for separating classes. One class is assigned to only one final node, and the maximum depth of the projection pursuit classification tree is at most the number of classes. Therefore, the projection pursuit classification tree constructs a simple but more understandable tree for classification. The projection coefficients of each node represent the importance of the variables in separating the classes in each node. The behaviors of the projection coefficients in each node are useful in exploring how to separate classes. This approach is extended to the regression tree.
## 3 Experimental Results and Discussion
### Engineering Problems
#### 3.7.1 Parameter Estimation for Frequency-Modulated (FM) Sound Waves  
Frequency modulation is a complex multimodal problem. The parameter vector is defined as at \(X=[a_{1},\omega_{1},a_{2},\omega_{2},a_{3},\omega_{3}]\) and the fitness function used in the analysis is the sum of the square errors between the estimated wave and the target wave and is given as follows:  
\[F(\overset{\rightarrow}{X})=\sum_{t=0}^{100}(y(t)-y_{0}(t))^{2} \tag{37}\]  
where  
\[y(t)=a_{1}\cdot\sin(\omega_{1}\cdot t\cdot\theta+a_{2}\cdot\sin(\omega_{2} \cdot t\cdot\theta+a_{3}\cdot\sin(\omega_{3}\cdot t\cdot\theta))) \tag{38}\]  
\[y_{0}(t)=(1.0)\cdot\sin((5.0)\cdot t\cdot\theta-(1.5)\cdot\sin((4.8)\cdot t \cdot\theta+(2.0)\cdot\sin((4.9)\cdot t\cdot\theta))) \tag{39}\]  
The minimum value that needs to be attained is 0. The parameters in the aforementioned equation are defined in the range [\(-6.4\), \(6.35\)] and \(\theta=2\pi/100\).  
Table 9 shows that GSLEO-AEFA discovered superior solution and PSO and DE followed with competitive accuracy. In comparison to other optimizers, GSLEO-AEFA has performed exceptionally well.
## 4 Experiments  
### Comparative Methods  
In order to validate the effectiveness of DeepSeek-R1 reasoning models in the field of vulnerability detection, this paper investigates five typical LLMs for comparative analysis. These models include the following:To assess the effectiveness of DeepSeek-R1 in software vulnerability detection, a comparative analysis involving five representative LLMs is conducted. These models are selected to cover a diverse range of capabilities in terms of reasoning ability, efficiency, and general language understanding. The details of the compared models are summarized as follows:  
* Claude-3.5-Haiku: Claude-3.5-Haiku is optimized for high-throughput applications, offering extremely fast response times and low computational cost. Its advantages lie in scenarios that demand rapid, large-scale inference with minimal latency. However, its reasoning capabilities are relatively shallow, and it currently only supports textual inputs, limiting its effectiveness on tasks that require multi-step logical inference.
* GPT-4o-Mini: GPT-4o-Mini is a lightweight variant of OpenAI's multimodal model family, emphasizing low-cost deployment and efficient multimodal handling. It demonstrates strong performance in general-purpose natural language tasks. Nonetheless, its performance degrades on lengthy or structurally complex code, and it struggles with maintaining reasoning consistency over long contexts.
* O3-Mini: O3-Mini is optimized for fast response and efficient reasoning, exhibiting strong performance in STEM-related tasks, especially those involving structured problem solving. It balances speed and reasoning quality well, making it suitable for tasks requiring intermediate logical depth.
* DeepSeek-V3: As a high-performance open-source model, DeepSeek-V3 stands out in code generation, mathematical reasoning, and programming-oriented tasks. It is particularly effective in logic-intensive scenarios and can produce high-quality code snippets. Despite its strength in text-based tasks, its capabilities in multimodal reasoning and language adaptability remain limited.
* DeepSeek-R1: DeepSeek-R1 leverages reinforcement learning and advanced optimization techniques to enhance its reasoning capabilities. It excels in mathematical reasoning, program analysis, and complex logic tasks. Although it currently lacks robust multimodal processing and may occasionally mix Chinese and English in its outputs, DeepSeek-R1 is well-suited for high-precision tasks requiring structured analytical thinking.
## 4 Discussion
HTPLA as printed has poor temperature stability, loosing significant stiffness at temperatures not much above 50 \({}^{\circ}\)C. The HTPLA can be heat treated for higher temperature stability. Actually, several polymers available on the market for 3D printing are capable of withstanding high temperatures, for example, PolyCarbonate (PC) and ABS. However, as mentioned before, ABS is not as easy 3D printable as PLA: 3D printing ABS requires a heated bed and a heated chamber in order to avoid warping and layers delamination, caused by an uncontrolled cooling. Moreover, 3D printing ABS generates toxic gasses and their uncontrolled release in hospitals or emergency clinics should be avoided. On the other hand, PC is the commercial 3D printing filament with the highest heat resistance. This feature is both a benefit and a problematic characteristic, as it is even more prone to warping and splitting than ABS filament. As such, this material needs high temperatures in order to extrude and have proper layer adhesion. PC filament will typically flow well in the range of 290-310 \({}^{\circ}\)C, nozzle temperatures much higher than the low-budget 3D printers' working temperatures, which are limited to 250-270 \({}^{\circ}\)C. On the platform side, optimal bed temperature for layer adhesion and curing should be kept between 145-150 \({}^{\circ}\)C. Even with PC, the 3D printer building volume should be closed in order ot avoid uncontrolled release of toxic gasses and forced air recirculation should be provided to the building volume. Other FDM High Temp filaments, such as PolyEther Ether Ketone (PEEK) and PolyEtherImide (PEI), require higher printing temperature, higher building volume temperature, and stricter cooling control systems. HTPLA by ProtoPasta, at the moment, is the only 3D printing filament capable of offering the easiness of printing of PLA combined with high temperature resistance. Finally, comparing to [12], our method diminishes the wastage of material since the model is printed in multiple pieces, minimizing the use of support pillars for unsupported areas. Furthermore, printing the model in multiple pieces speeds up the printing step. The present study must be seen in light of some limitations: to date, we printed only bony models; therefore, the impact of the mechanical features of HTPLA (brittleness, stiffness) was not investigated since it was not relevant at this phase of the study. To this purpose, the research is ongoing and more data will be available from the use of cutting guides or customized tools during surgery. Moreover, we tried only the steam sterilization that is easy, low-cost, and very effective to our purpose; additional research should be conducted to investigate the possible changes of the model with other methods of sterilization. Finally, prospective randomized clinical studies are advancing to assess the effectiveness of this technology, in terms of risk-benefits and cost-benefits ratio, especially in pediatric orthopedics and traumatology.
## 1 Introduction
### Stereo Limitation  
Due to the temporal convergence of multi-temporal images taken on staggered orbits with slight off-nadir angles, the PlanetScope constellation represents an interesting alternative to known commercial missions for generating 3D surface models with very high temporal and fairly high spatial resolutions to study, for example, glacier dynamics, e.g., [15]. However, the mission is not designed for stereo evaluation by default. This option results purely from the extremely high temporal repetition rate of the cubesats, which capture the same area from slightly different perspectives several times a day/week. The limitations in the stereo capability result from rather short baselines between potential image pairs due to small off-nadir angles, small footprints, and large across-track overlaps. Ref. [17] reported base-to-height ratios in the range of 1:10 to 1:100, where photogrammetry strives at ratios in the order of 1:1. A small baseline causes narrow intersection angles of two visual lines representing image features and intersecting in an object point, thus reducing the depth information precision. On the other hand, stereo image matching becomes much easier due to similar perspectives and fewer occlusions, especially in mountainous areas, e.g., [18]. Both allow for a certain amount of compensation, which can enable the creation of a reliable DEM using highly redundant image information from multi-temporal images, even if the stereo configuration is not optimal.  
In the following, we provide a literature review of studies that have utilized PlanetScope data for DEM generation. Subsequently, we present our methodology, detailing the _Planet4 Stereo_ workflow based on the ASP. In the Experiments Section, we apply _Planet4 Stereo_ to generate 3D surface models for both large- and small-scale areas. The accuracy potential of the pipeline is evaluated by comparing the generated models with reference DEMs.
## 2 Materials and Methods
### Statistical Comparison of Models  
The cells and intercellular spaces were randomly divided into five groups. These groups were then used to model, test, and validate the models based on CNN in \(k\)-fold cross-validation with \(k=5\). For the RBNN-based model, the features and parameters for microstructural elements were obtained according to Section 2.2.2; likewise, as for CNN models, \(k\)-fold cross-validation was implemented.  
This process was repeated one hundred times to evaluate the robustness of the method, calculating for this purpose, the confusion matrices; Figure 8 shows the basic form of the confusion matrix for binary classification.  
A confusion matrix is one of the most commonly used techniques in the machine learning community and contains information about the actual and predicted ratings obtained by a classification system. A confusion matrix has two dimensions: real and predicted classes. Each row represents the instances of a real class, whereas each column represents the cases of a predicted class. In the case of a binary classification, each cells contains: \(TP\) (True Positive), correctly identified; \(TN\) (True Negative), correctly rejected; \(FP\) (False Positive), incorrectly identified; and \(FN\) (False Negative), incorrectly rejected.  
Some performance measures can be defined from the information contained in a confusion matrix, among them _precision, recall, accuracy_, and _f-measure_. These measures are determined by the number of classification errors and hits made by the classifier, as expressed by Equations (7)-(10).  
* Accuracy: This measures how many observations, both positive and negative, were correctly classified and it is defined by Equation (7). \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\] (7)  
Figure 8: Example of confusion matrix for binary classification.  
Figure 7: Learning transfer for Convolutional Neural Network (CNN) AlexNet.  
* Recall: This measures how many observations out of all of the positive observations were classified as positive (see Equation (8)). \[Recall=\frac{TP}{TP+FN}\] (8)
* Precision: This measures how many observations predicted as positive are, in fact, positive (see Equation (9)). \[Precision=\frac{TP}{TP+FP}\] (9)
* F1-score: This combines precision and recall into one metric (harmonic mean, see Equation (10)). \[F_{1}-score=2\times\frac{Precision\times Recall}{Precision+Recall}\] (10)
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 2 Numerical Calculations
### DC SQUID Noise  
#### 2.1.1 1/f Noise  
1/f noise, also known as flicker noise, can significantly limit the performance of practical devices at low frequencies. In SQUID devices, 1/f noise primarily arises from two sources: fluctuations of the critical current \(I_{0}\) in the Josephson junctions and the motion of flux lines (vortices) trapped in the body of the SQUID or in the superconducting input circuit. The SQUID directly induces changes in the stray magnetic flux coupled to the SQUID magnetic ring, which is generated by vortices moving with a radial motion spectral density \(S_{\mathrm{r}}(f)\), and this generates flux noise, expressed as \(S_{\mathrm{\Phi}}=S_{\mathrm{r}}\big{(}\partial\Phi_{\mathrm{a}}/\partial r \big{)}^{2}\), where \(r\) is the radial distance of the vortex from the SQUID hole. The surface spin fluctuation is a dominant source of 1/f noise in superconducting devices. This fluctuation is influenced by the physical dimensions and layout of the washer geometry.  
While 1/f noise in low Tc SQUIDs generally does not pose a serious issue, the increasing demand for higher measurement accuracy in the field of magnetometry drives researchers to pursue designs that minimize this noise even further.