esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Statistical Shape Models to Optimize TKA Implant Design
## 2 Materials and Methods
### Validation of the SSM  
Once the SSM was built, a series of tests allowed to ensure that it statistically represents the population well. As the use of SSMs in medical applications is limited, most of these specific validation tests were developed by [PERSON](r), Leuven, Belgium. Some preliminary tests are briefly summarized below:  
* The Vectorization Errors Test quantifies the error introduced by preprocessing each patient's bone before it can be used for a SSM (all bones included in the SSM must have the same mesh).
* The Normality Test analyzes whether the population is normally distributed.
* The Compactness Test analyzes how well the modes of variation of the SSM capture the geometrical variation. If the SSM is very compact, the SSM can describe the geometrical variation observed in the input population in a few virtual modes.  
Since the focus of this paper is on the use of SSMs for TKA design, the following two tests are described in more detail.  
* The Convergence Test analyzes the impact of adding additional patients. If convergence is achieved, additional scans no longer have an impact on the SSM. If convergence is not achieved, this test can be used to calculate the impact of adding new scans to enable the SSM to better capture the input population.  
To perform this test, the input data are used to build two independent SSMs. For example, with a population of 100 patients, 50 patients are used to build one SSM; the remaining 50 patients are used to build a second SSM. The RMS error between these SSMs is computed. An increasing amount of datasets is used to compute both SSMs. At a certain point, the populations begin to overlap. For example, with an input population of 100 patients, if 70 patients are used to build the SSMs, 20 patients are used in both models (overlap of 20). The optimal training size is reached when the RMS error reaches the value zero before the populations begin to overlap. This would mean that two models built with independent populations are similar.  
* The Leave-one-out Test analyzes how well the SSM describes patients who are not part of the input population. To perform this test multiple SSMs are generated, leaving out each input patient in turn. For example, with an input population of 112 patients, 112 different SSMs are built with 111 patients. Each of those 111 SSMs is built with an increasing number of modes. The average RMS deviation between the SSMs is computed.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 4 Results
### Assessment of GPP in Different Vegetation Types  
In the real world, vegetation is typically a mixture of different types of vegetation. Traditional GPP estimation methods require prior knowledge of the vegetation composition in the target area, which can be challenging in complex and diverse vegetation environments [54]. Six representative vegetation types were selected to assess the model's applicability in different vegetation types, namely, grasslands (GRA), croplands (CRO), everyone needleleaf forests (ENF), permanent wetlands (WET), deciduous broadleaf forests (DBF), and open shrublands (OSH). Figure 7 shows the R\({}^{2}\) and RMSE of different GPP products across various vegetation types. SA-LGBM performs nearly as well as or better than other GPP products across all vegetation types, except in the WET type where its performance is not as prominent. Notably, in regions with relatively low vegetation productivity, such as the OSH type, the predictive capability of all GPP products is not outstanding [40; 55]. However, SA-LGBM significantly outperforms other products and demonstrates good spatial generalization capability.
# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 2 Literature Review
### Framing Theory
#### 2.2.3 News Framing Analysis Methods  
[PERSON] [40] noted that news framing analysis is cross-disciplinary, covering discourse, its construction, and reception. Although framing theory originated in sociology, it has been developed into a method for studying news texts in the field of communication. [PERSON] [36] believed that framing analysis is better at revealing the internal logic and essential characteristics of news production compared to other social science research approaches. The above three categories also correspond to the three major areas of media communication research: media content, news production, and media effects.  
News framing research methods are diverse and rich, including but not limited to content analysis [41], discourse analysis [42], narrative analysis [43], etc. This paper will adopt the content analysis method used by most researchers, which starts from the media content, i.e., the news text itself, and reveals the dominant frames hidden behind news reports and their operating mechanisms by conducting in-depth discussions on aspectssuch as keywords, themes, narrative structures, and emotional tendencies in the text [44]. Different news organizations may generate multiple frames due to differences in their own interests, ideologies, or audience positioning, shaping the public's perception of and response to social phenomena with these frames. For example, in the reporting of public health issues, Chinese media usually prioritize national and collective interests, emphasizing the proactive measures taken by the government in responding to public health crises, the efficiency of policy implementation, and the overall layout of epidemic prevention and control. In contrast, Western media have a more diverse perspective. In addition to focusing on government policies and measures, they place more emphasis on reporting the operating mechanisms of the public health system, civil rights, information transparency, and public reactions to policies [45].  
[PERSON] [35] believed that news frames have the following four functions: providing problem definitions, interpreting event causes, offering moral evaluations, and suggesting solutions, corresponding to four types of frames: problem definition frames, event attribution frames, moral judgment frames, and solution frames. [PERSON] [46] divided the frames in news reports into thematic and episodic structural frames. [PERSON] [47] believed that news frames include issue-specific frames and generic frames. Issue-specific frames are necessarily related to specific issues and events, while frames that can cross issue boundaries and be used in multiple reports are called \"generic frames\". He defined seven generic frames: fact frame, responsibility frame, conflict frame, human interest frame, economic consequences frame, morality frame, and leadership frame.
## 6 Results and Discussion
### Experiment (B)  
In the second experiment, we selected the first dataset D1 and we split it into nine training subsets (as described in Section 5.1); we used the same test set to evaluate each of the nine models. We expected that increasing the size of the training set would correspond to an increase in the performance until they reached the same F-score obtained on the entire training set. Figure 7a confirms our assumption showing that, as the training percentage increases, so does the F-score of all four platforms. We can also see that the curves of Watson, Dialogflow, and Wit.ai grow quite quickly until we reach 40% of training instances and then fluctuate or grow slowly, whereas Luis rises steadily up until it reaches its maximum. Figure 7 shows the error rate for the different training set percentages. We can see that [PERSON] and Dialogflow have significantly higher performance using fewer training instances (10% and 20% of the whole training set) compared to Luis and Wit.ai. Moreover, we can observe that, starting from the model trained with 50% of the training instances and going up, the difference in terms of error rate gradually decreases.  
Finally, Figure 8 presents the number of the intentions which are correctly identified (ok), the ones which are incorrectly identified (ko) and those who were not found. In the calculation of the error rate, the \"not found\" intentions are considered alongside the wrong ones. An important difference among the platforms is that while Watson and Luis always provide a classification, Wit.ai and Dialogflow do not produce a specific prediction unless the confidence associated with the intention is over a certain threshold, therefore producing a higher number of \"not found\". The graph also shows a decrease of \"not found\" elements at the increase of the training set size.  
Figure 6: Results of the Post-hoc Conover Friedman test on the results of the tested platforms in Experiment (A). NS represents not significant.  
Figure 7: Results of Experiment (B) considering different percentages of the training set, in terms of F-Score (**a**) and Error Rate (**b**).
# Metallic Nanoparticles Obtained via \"Green\" Synthesis as a Platform for Biosensor Construction
## Materials and Methods
### Characterization of the Synthesized NPs  
Optical UV-visible spectra for characterizing the process of gNP formation from inorganic ions were obtained using a UV1650 PC spectrophotometer (Shimadzu, Kyoto, Japan).  
A scanning electron microscope (SEM-microanalyser REMMA-102-02, Sumy, Ukraine) was used for morphological analyses of the samples. The special cover film on the samples with a Butvar solution B-98 (Sigma, St. Louis, MO, USA) in 1.5% chloroform was formed using an ultrasound method. The distance from the last lens of the microscope to the sample (WD) ranged from 17.1 mm to 21.7 mm; the accelerator voltage was in the range of 20 to 40 eV; zooms were from 2500 to 10,000.  
An inverted fluorescence microscope (FM, Axio Lab. A1., Carl Zeiss, Esslingen, Germany) with excitation by a mercury vapor arc lamp (X-Cite series 120Q) was used for sample imaging. The filters set was composed of fluorescein isothi cyanate (green) and 4\({}^{\prime}\),6-diamidino-2-phenylindole (blue). In order to prepare the tested sample, a 10 \(\upmu\)L aliquot of a water-diluted probe was placed on a glass slide and covered with a cover slip (12 mm diameter).  
The Infrared spectra of gPdNPs were acquired by a Nexus 470 Thermo-Nicolet FTIR spectrometer (Thermo Fisher Scientific, Dardilly, France) using a single bounce attenuated total reflectance accessory (Spectra-Tech Thunderdome, Thermo Fisher Scientific) with a germanium crystal. Background was systematically recorded before the data acquisition of each sample (spectra from 4000 to 690 cm\({}^{-1}\), 256 scans, and resolution 4 cm\({}^{-1}\)). Spectra were obtained after depositing a drop on the crystal and after evaporation of water.  
A semi-quantitative express method was used for studying the effect of the synthesized gNPs on alcohol oxidase activity. 10 \(\upmu\)L aqueous NP solutions were dropped into the cells of a 96-well microplate and supplemented with 10 \(\upmu\)L chitosan solution (1 mg mL\({}^{-1}\)) in PB. After 15 min incubation for solid film formation, 15 \(\upmu\)L AO solution (0.5 U mL\({}^{-1}\)) in PB were dropped into the cells containing gNPs immobilized in chitosan films. 200 \(\upmu\)L \(o\)-dianizidine-based substrate for assay of AO activity were added to each cell, and after 2 min incubation at room temperature, the supernatants were transferred into glass tubes. The cells were washed twice with 200 \(\upmu\)L PB, and the supernatants were combined with the initial supernatants in glass tubes. 300 \(\upmu\)L of concentrated HCl were then added to each tube. As a control, AO solution was put into cells supplemented with chitosan, but without gNPs. AO activity was determined spectrophotometrically [60] and calculated as % relative to the control sample.
# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
###### Abstract  
This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN's exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO's intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66-70 g weight, 3-5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method's efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency.  
sustainable injection molding; backpropagation neural network; particle swarm optimization; Taguchi method; process parameter optimization
## 2 AI Techniques in Communication Networks
### Federated Learning for Privacy-Preserving Network Optimization
However, with FL, each network node can independently optimize routing based on its local knowledge (e.g., traffic load, network topology), and share the resulting model updates with the central server. This allows for more adaptive and responsive network management, where each device contributes to optimizing the network based on real-time data. Challenges and Considerations:  
While federated learning offers several advantages for privacy-preserving network optimization, it also introduces unique challenges. One of the key issues is dealing with non-IID (non-independent and identically distributed) data across different devices or nodes. In real-world networks, devices may generate heterogeneous data, leading to challenges in model convergence and performance. For instance, the data from one device might differ significantly from those of another device, making it difficult to train a model that generalizes well across all devices. Approaches such as personalized federated learning and federated transfer learning are being explored to address this issue by adapting the learning process to individual devices or device clusters [63]. Another challenge is ensuring the security and robustness of the FL process. Malicious devices could potentially participate in the learning process and submit false or harmful model updates. To mitigate this risk, secure aggregation methods are used to combine model updates in such a way that individual updates remain private and advanced techniques like differential privacy can be employed to add noise to updates, further protecting the privacy of devices [64]. Recent Research:  
Several studies have explored the application of FL in the context of network optimization. For example, [65] investigates using FL for optimizing routing in mobile ad hoc networks, demonstrating that FL can achieve near-optimal performance while reducing data transmission. Similarly, [66] applied FL to IoT networks for resource management, showing that FL can achieve lower latency and higher throughput with reduced communication overhead compared to centralized approaches. These studies highlight the growing interest in FL for optimizing decentralized networks while maintaining data privacy. In conclusion, federated learning is a promising approach for privacy-preserving network optimization. By enabling decentralized training and sharing of model updates, FL allows for collaborative learning in distributed network environments while preserving privacy and reducing data transmission costs. As the technology continues to evolve, future research will likely focus on addressing challenges related to data heterogeneity, security, and the efficiency of model aggregation, ensuring that FL can scale effectively in complex and dynamic network environments. Figure 2: Federated learning vs. centralized learning performance.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 2 Models
### Kalman Filter
#### 2.4.2 Unscented Kalman Filter  
Another variant of the Kalman filter is the unscented Kalman filter. This estimator does not assume linearity. Instead, it uses sigma points to describe the state's distribution, the sigma points being the vectors of possible states.  
These points are calculated as a function of the previous estimate, the covariance matrices (of the state and noises), and some parameters. Then, they are updated according to the non-linear model of the system. Each of these points is then used, together with measurements, to calculate the Kalman gain. From this gain, the error between the expected measurements and the real ones, the predicted state, the filtered estimate, and its covariance matrix can be determined.  
The UKF algorithm, in the case of additive noises, is described as follows [11; 17]:  
1. \(\mathcal{X}_{0}(k|k)=\hat{x}(k|k)\)
2. \(\mathcal{X}_{i}(k|k)=\hat{x}(k|k)+\left(\sqrt{\frac{n}{1-\mathcal{W}_{0}}P(k| k)}\right)_{i}\) \(i=\{1,2,...,n\}\)
3. \(\mathcal{X}_{i}(k|k)=\hat{x}(k|k)-\left(\sqrt{\frac{n}{1-\mathcal{W}_{0}}P(k| k)}\right)_{i-n}\) \(i=\{n+1,n+2,...,2n\}\)
4. Prediction:  
1. \(\mathcal{X}_{i}(k+1|k)=f(\mathcal{X}_{i}(k|k),u(k),k)\) (next sigma points)
2. \(\hat{x}(k+1|k)=\sum_{i=0}^{2n}[\mathcal{W}_{i}\mathcal{X}_{i}(k+1|k)]\)
3. \(P(k+1|k)=\sum_{i=0}^{2n}[\mathcal{W}_{i}(\mathcal{X}_{i}(k+1|k)-\hat{x}(k+1|k) )(\mathcal{X}_{i}(k+1|k)-\hat{x}(k+1|k))^{T}]+Q(k)\)
4. \(\mathcal{Y}_{i}=g(\mathcal{X}_{i}(k+1|k),u(k),k)\) (expected \"sigma\" measurements)
5. \(\hat{y}(k+1|k)=\sum_{i=0}^{2n}[\mathcal{W}_{i}\mathcal{Y}_{i}]\) (expected measurements)Filtering:  
1. \(e(k)=y(k)-\hat{y}(k|k-1)\)
2. \(P_{y,y}=\sum_{i=0}^{2n}\left[\mathcal{W}_{i}(\mathcal{Y}_{i}-\hat{y}(k|k-1))( \mathcal{Y}_{i}-\hat{y}(k|k-1))^{T}\right]+R(k)\)
3. \(P_{x,y}=\sum_{i=0}^{2n}\left[\mathcal{W}_{i}(\mathcal{X}_{i}(k+1|k)-\hat{x}(k| k-1))(\mathcal{Y}_{i}-\hat{y}(k|k-1))^{T}\right]\)
4. \(K(k)=P_{x,y}P_{y,y}^{-1}\)
5. \(\hat{x}(k|k)=\hat{x}(k|k-1)+K(k)e(k)\)
6. \(P(k|k)=P(k|k-1)-K(k)P_{y,y}K(k)^{T}\)  
In the algorithm:  
* \(n\) is the length of the state vector;
* \(\mathcal{W}_{i}\) is the weight of the \(i\)-th sigma point, i.e., its contribution to the calculation of the estimates and covariances;
* \(-1<\mathcal{W}_{0}<1\);
* \(\sum_{i=0}^{2n}\mathcal{W}_{i}=1\), that is \(\mathcal{W}_{i}=\frac{1-\mathcal{W}_{0}}{2n}\) for all \(i=\{1,2,...,2n\}\)
* \(\left(\sqrt{\frac{n}{1-\mathcal{W}_{0}}P(k|k)}\right)_{i}\) is the \(i\)-th column of the matrix \(\sqrt{\frac{n}{1-\mathcal{W}_{0}}P(k|k)}\), and the square root matrix is obtained from the Cholesky decomposition.
## 3 Classifications of Routing Protocols in UAV Networks
### Swarm-Based Routing Protocol  
Swarm-based routing protocols are inspired by the behavior of animals and nature, and are applied in solving various problems in UAV networks [6], especially in establishing communication between UAVs. These protocols are based on the behavior of insects such as fish, ants, bees, and particle swarms. In this paper, some prominent swarm-based routing protocols are described in detail below. The ant colony optimization-based polymorphism-aware routing algorithm (APAR) is proposed by the authors in [55]. The APAR algorithm is a powerful tool for solving optimization problems that can be applied in UAV network application models. In the investigation phase, the routing paths are determined based on the \"pheromone\" concentration and the travel time of the sent packets. In addition, this type of protocol takes into account the congestion on the network (measured by buffer occupancy and channel load) and the reliability of the routing paths (measured by mobility and connectivity) while making the selection. When a UAV wants to communicate with another UAV, for example, UAV (D), as shown in Figure 13, the UAVs communicate using the route request RREQ.ant and route reply RREP.ant to find the most connected routing path. The simulation results show that the APAR algorithm outperforms traditional algorithms in terms of packet delivery ratio, end-to-end delay, routing cost, and reliability in the environment. An improved ant colony algorithm based on fuzzy logic memory mechanism (FLM-IACO) to obtain lower-cost flight paths for multiple drones is presented by the authors in [56]. The proposed solution is a multi-UAV cooperative route planning model guided by mission requirements, considering low-altitude obstacles and enemy detection threats. In addition, a mixed pheromone structure delimiting infeasible regions is identified during the path-planning process. The reliability of the next nodes is evaluated using membership degrees and a layered expansion, and a correction strategy is used to refine the flight paths.
# Transparent Redirections in Content Delivery Networks
## 2 State of the Art
### Existing Transparent Redirection Mechanisms
In their experiments, they measured how the congestion window (CWND) and the slow start threshold (SSThresh) is affected, when a live TCP socket is migrated from one CDN surrogate to another one in various network conditions for long-lasting TCP sessions, e.g., VoD streaming. They applied delays and packet loss to simulate real-life scenarios. The TCP socket migration was achieved using the TCP_REPAIR flag [22], which puts the socket to maintenance mode, so the TCP parameters can be dumped and transferred to another server, where the session can be restored, thus allowing migration of negotiated parameters during the TCP handshake phase andcurrent sequence and acknowledgement numbers. The paper does not provide measurements of the delay required to perform the TCP migration itself. [PERSON], [PERSON], [PERSON] and [PERSON] in their paper [23] present Prism proxy for fast load balancing technique, where the TCP sessions established with the load balancer, upon reception of the request, are handed-off to a chosen backend server from the pool. The solution is designed for data center scale load balancing; however, it could be used for CDNs as a transparent redirection engine over greater areas. The Prism proxy uses the same TCP_REPAIR [22] flag to pass the sessions between the servers. The solution uses HTTP/1.1 and is capable of reusing existing TCP sessions for subsequent requests. The authors used mSwitch, which is a kernel software switch that can forward packets at a rate of over 10 Mpps on a single CPU core [24]. To perform a transparent redirection the CDN must be able to perform a TCP session handoff, otherwise, the redirection becomes visible to the end-user. All the related work mentioned above points out the challenges associated with the handoff procedure.