esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Results of Problem 3 and CNr Made Only of Capacitors  
The Pareto front of Problem 3 is shown in Figure 14a. Among the 75 individuals of this group, only 10 satisfy the conditions of Section 3.1. As shown in Figure 14b, they can be roughly divided into two groups, one supplying \(\mathrm{P}_{\mathrm{L}}(0)\) around 1 kW and the other with \(\mathrm{P}_{\mathrm{L}}(0)\) around 3 kW.  
Among the latter ones, individual M gives the highest \(\mathrm{P}_{\mathrm{L}}(0)\) with the lowest \(\mathrm{P}_{\mathrm{L}\text{-}\mathrm{\SIUnitSymbolMicro}\text{-}\mathrm{u} \text{-}\mathrm{max}}\). Unfortunately, Figure 15 and Table 3 show that its \(\mathrm{xp}_{\mathrm{max}}\) is the shortest and so it is not suitable for a dynamic WPTS.  
Figure 14: **(a)** Pareto front of the improved solutions for Problem 3 and receiving side CN made only of capacitors and **(b)** Power vs. position. In panel **(a)** the green dots show the selected individuals in the analysis.  
Figure 13: Efficiency vs. position (Problem 2 and receiving side CNs made only of capacitors).  
The efficiency profile of individual M is reported in Figure 16 and has more or less the same behavior of the efficiency profile relevant to individual H plotted in Figure 13; in the aligned condition, the efficiency is lower than that of the other individuals, but it becomes the highest when the distance to the aligned position increases. For Problem 3, the maximum efficiency in the aligned condition is achieved by individual O, even if it is outperformed by other individuals obtained using the first or the second pair of OFs. All the analyzed individuals in terms of power profile are marked with green stars in Figure 14a.  
Considering the individuals with P\({}_{\rm L}\)(0) around 1 kW, the one marked with the \"L\" supplies the lowest power equal to 660 W, comparable with the performance of individual H considered in the Section 3.4; with respect to the later one, L has a longer p\({}_{\rm max}\) and approximately the same efficiency, but its P\({}_{\rm L,p\cdot u\cdot,max}\) is considerably higher and hence it requires a stronger control effort to regulate the output power.  
The p\({}_{\rm max}\) of the individual denoted with \"N\" is the longest among the total of the individuals resulting from the optimization process and results of 207 mm. This performance is obtained at the expense of a very high P\({}_{\rm L,p\cdot u\cdot,max}\), which reaches 4.48, and of a profile of efficiency that lies under the others for any position.
## 4 Discussion
The cell lines involved in dental remodeling are osteoblasts and osteoclasts that reshape the bone under the influence of cytokines, such as the nuclear factor kappa B ligand (RANKL), osteoprotegerin (OPG), transforming growth factor-beta (TGF-beta), and prostaglandin E2 (PGE2) [34]. The use of photobiomodulation [35] intervenes directly in the production of inflammatory mediators and in the greater recruitment of osteoblasts and osteoclasts [15; 22; 33]. Our results confirmed those that emerged in the pilot study conducted by [PERSON] et al., who showed a significant increase in the speed of the tooth movement in the PBM (48 days) compared to the control group (104 days) [27]. Compared to other dental movement acceleration systems that require greater surgical invasiveness, excellent practical skills of the clinician, greater time in appointments at the chair with the consequent expenditure of economic resources by the doctor, and the need for movements by the patient with consequent discomfort, this device can be used at home and the doctor can remotely control its correct use. In our study, the combined use of transparent aligners and OrthopulseTM has been proved as a fast and discreet system, aimed especially at busy adults with little time available for dentist appointments and who need not show that they are wearing the dental alignment device. This study included a total of 170 patients who received orthodontic treatments with and without PBM between January 2019 and December 2021. A possible limitation of this study is that patients' data were extracted retrospectively and therefore without a randomization process for assignment to a group. Second, the study evaluated the effect of PBM on the speed of dental movement and, in particular, on the reduction in the time required to resolve cases of dental crowding. A further extension of the study could investigate a possible reduction in the incidence of external root resorption and the perception of a painful orthodontic sensation. Several strengths of the study include a sufficiently large number of patients and their optimal compliance in following the dentist's instructions. Furthermore, the period of observation coincided with the outbreak of the COVID-19 pandemic, which in Italy caused a prolonged lockdown from March to May 2020 and a series of negative repercussions on private dental offices. The possibility offered by Invisalign(r) in monitoring patients' compliance through an app allowed the dentist to overcome problems related to the COVID-19 lockdown. The results of this study can be used to plan future or RCT studies to evaluate the effectiveness of PBM in accelerating time and improving the patients' overall quality of life.
## 5 Challenges and Actionable Pathways for AI Adoption in SMEs
### Accessible Catalog of Practical Applications: A Strategic Ecosystem Imperative  
SMEs would benefit significantly from access to a curated and regularly updated catalog of Generative AI applications, particularly those based on LLMs, that showcase real-world use cases and documented productivity improvements. Such a catalog should include sector-specific and cross-functional case studies, benchmarks, deployment guides, and integration templates--providing SMEs with actionable insights on how GenAI can enhance efficiency, reduce costs, and unlock new forms of innovation. At present, no widely recognized repository exists that systematically compiles and contextualizes successful GenAI applications tailored for SMEs. This absence creates a significant barrier, as many SMEs lack the internal capacity to explore the breadth of AI solutions or to translate abstract capabilities into operational use cases. A well-designed catalog would act as a practical bridge--demystifying AI by showing how similar-sized firms have applied it for marketing automation, customer support, internal documentation, compliance analysis, or knowledge management.  
Establishing and maintaining such a catalog should be viewed as an ecosystem-level priority for policy actors, industry alliances, and AI providers. Without this, SMEs will continue to face limited access to replicable adoption pathways--hampering both innovation scalability and cross-sector learning.  
_Illustrative Case Example:_ A mid-sized logistics SME in Spain integrated Mistral-7B via Hugging Face and FastAPI to automate client email summarization and route planning queries. This reduced human workload by approximately 40% and achieved a 12% increase in customer satisfaction within six weeks. Hosting was implemented through a GPU instance on Paperspace, reducing infrastructure costs by 35% compared to equivalent proprietary API services.  
As needs evolve, transitioning to open-weight LLMs [122], such as DeepSeek-R1, LLaMA, Mistral, or Falcon, offers SMEs greater autonomy and transparency. These models allow for self-hosting, fine-tuning, or deployment on private infrastructure, making them suitable for SMEs operating under strict compliance, cost, or customization requirements. This flexibility enables more sustainable, ethically guided, and strategically aligned AI adoption.
# Multiblock Analysis Applied to TD-NMR of Butters and Related Products
## 2 Material and Methods
### Data Processing
#### 2.3.1 PLS and SO-PLS  
PLS aims at finding links between a matrix of descriptors \(\mathbf{X}\) (\(N\times Q\)) and a matrix of responses \(\mathbf{Y}\) (\(N\times R\)). It decomposes the two matrices into a sum of \(K\) rank 1 products of scores by loadings, as:  
\[\mathbf{X}=\mathbf{TP}^{\rm T}+\mathbf{R}_{X}\quad\text{ and }\quad\mathbf{Y}= \mathbf{UQ}^{\rm T}+\mathbf{R}_{Y}, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **\#Samples** & **Mean \(F_{w}\) (\%)** & **Min \(F_{w}\) (\%)** & **Max \(F_{w}\) (\%)** \\ \hline Butter & 35 & 82.5 & 81.5 & 84.9 \\ Margarine & 3 & 79.6 & 78.3 & 80.6 \\ Concentrated fat & 12 & 99.4 & 98.1 & 99.9 \\ \hline \end{tabular}
\end{table}
Table 1: Dataset summary. \(F_{w}\) is the % _w/w_ of fat.  
Figure 1: Scheme of both signal measurements: FID (in light gray) and the signal actually measured (in dark grey).  
with the aim of covariance between columns of \(\mathbf{T}\) and of \(\mathbf{U}\) being maximal and columns of \(\mathbf{T}\) being mutually orthogonal. The PLS is usually followed by a classical linear regression between \(\mathbf{T}\) and \(\mathbf{Y}\), yielding a PLSR. The scores used in the regression step are called latent variables. When the \(\mathbf{Y}\) matrix contains only one column, PLSR yields an estimate \(\mathbf{\hat{y}}\) of \(\mathbf{y}\) according to:  
\[\mathbf{\hat{y}}=\mathbf{Xb} \tag{2}\]  
The vector \(\mathbf{b}\), called b-coefficients, is a vector of dimension \(Q\), as the variables contained in the rows of \(\mathbf{X}\). Consequently, it could be plotted as a signal. Interpretation of its shapes helps to analyze the relative importance of the \(\mathbf{X}\) features in the regression. For example, a positive peak in the b coefficients corresponds to an area where the signal is positively related to \(\mathbf{y}\). Another example is when all signals have similar shapes, b coefficients are shaped like the average signal. However, some more complex characteristics can also be analyzed using b coefficients, such as slopes, curvatures and even horizontal offsets, as explained in [25].  
SO-PLS is a multi-block regression method where the information is sequentially extracted from the different predictor blocks. Considering the case of two predictor blocks (\(\mathbf{X}_{1}\) and \(\mathbf{X}_{2}\)) used to estimate a \(\mathbf{y}\) response, the algorithm is as follows:  
1. The \(\mathbf{y}\) response is fitted to \(\mathbf{X}_{1}\) by PLS using \(K_{1}\) latent variables.
2. \(\mathbf{X}_{2}\) is orthogonalized with respect to the \(K_{1}\) scores extracted from the first block.
3. The \(\mathbf{y}\)-residual obtained from step 1 is fitted to \(\mathbf{X}_{2}\) by PLS using \(K_{2}\) latent variables.
4. The scores produced at steps 1 and 3 are then used in an MLR to predict the response \(\mathbf{y}\).  
A SO-PLS model contains as many b-coefficients as blocks used by the model.
## 5 Network Layer  
The network layer is mainly responsible for routing and addressing, which deals with determining the hop-by-hop data paths and ensuring network-wide connectivity, respectively. The network layer in the context of _IoT_ is, to some extent, comparable to the notion of opportunistic networking [100] as they face similar challenges: communication is among heterogeneous nodes and no preset links among them are assumed [101]. With the problem of spectrum contention described in previous sections, adaptations need to be made at this layer to protect systems from performance degradation.
# Development and Performance Evaluation of an Integrated Disc Cutter System for TBMs
## 5 Experimental Verification
### Experimental Program  
According to the analysis of the anti-loosening principle of the TCS and the ICS, the preload of the TCS comes from the thread deformation of the bolt and the nut and is transmitted to the cutter shaft through the screw and wedge. The tension of the screw was measured to reflect the preload force of the TCS in this article, as shown in Figure 11a. The preload of the new ICS comes from the compression deformation of the connecting links. As shown in Figure 11b, this paper obtained the preload force of the ICS by measuring the pressure of the connecting links. Since both cutter systems were symmetrical structures, the following measurement methods were used to reduce system errors. For the TCS, two strain gauges were attached to each screw along a coaxial line, and the average value was taken as the strain value of the screw. The new TCS took the average strain of the two connecting links on the same side as the strain value on that side.  
Figure 10: Scale sample. (**a**) The TCS; (**b**) The novel TBM ICS.  
To speed up the relaxation process of the overall structure, a vibration block was installed on the cutter shaft, as shown in Figure 12. When the cutter system reciprocated with the vibration table, the vibrating block impacted the cutter shaft due to inertia and generated an alternating load, which promoted the relaxation of the overall structure. To ensure the same size of the alternating load, the mass of the vibrating block of the two schemes was 0.65 kg.  
The anti-loosening performance test system of the cutter system is shown in Figure 13. The scale sample of the cutter system was fixed on the vibration table, and the experiments were performed with vibration accelerations of 3 g and 10 g. Moreover, the vibration frequency of both cases was 30 Hz. During the experiment, the strain value was recorded every hour by the strain collector.  
Figure 11: Strain measurement. (**a**) Measuring location of the TCS; (**b**) Measuring location of the ICS; (**c**) Measuring location of the TCS scale sample; (**d**) Measuring location of the ICS scale sample.  
Figure 12: Schematic diagram of the vibration block position.
## 4 Strategies for Separation of AE Impulses from the Background Signals of Various Types
### Algorithm for Isolating the Useful Signal against the Background of Periodic Local Acoustic Pulses
Summing up, the strategy for separating a useful signal from the periodic local acoustic pulses with superimposed AE pulses requires sequential implementation of the following procedures:  
* Decomposition of the signal into empirical modes and construction of the first of these modes;
* Isolation of single AE impulses from the entire data volume;
* Multiparameter cluster analysis of a complete set of single pulses; the cluster associated with pulses in which the background acoustic pulse prevails (the case when the amplitude of the superimposed AE pulse is much less than that of the background acoustic pulse) is excluded from further consideration.
## 3 Proposed Method
### Tree-Based Routing Protocol  
In WSN multi-hop routing, when the sink is in a static state, the nodes closer to the sink have high traffic, they are not only responsible for sending their own data but also act as relay nodes to forward the data sent by the more distant nodes, they are more likely to die due to energy depletion, so it is essential to allow the sink to move. The mobile sink is able to constantly change the neighboring nodes around them to balance the energy consumption of the network, whereas the sink is allowed to move only in the inner area, which reduces the latency and energy consumption. At the very beginning, all the nodes store the initial position of the sink, which is located right in the center of the entire monitoring area, and the nodes in the inner area are connected to the sink through the geographic routing protocol. The sink only needs to notify the one-hop nodes and the two-hop nodes of its new position after each move instead of notifying the whole network through flooding, which is definitely energy-efficient. Figure 3 reveals the schematic diagram of the tree-based routing protocol, the yellow nodes represent the one-hop neighbor nodes of the sink, the green  
Figure 2: The advantage of improved geographic routing.  
nodes represent the two-hop nodes of the sink, the blue nodes are the normal nodes in the inner area, and \(R\) represents the communication radius of the sink. The sink moves in the inner area and sends a \"my new location\" control packet to its one-hop neighbor nodes for every \(R\) distance moved, and the one-hop neighbor nodes will continue to send a \"sink new location\" control packet to their nearest neighbor nodes after receiving this command, which is called two-hop nodes, and all other nodes in the network can connect to sink through these nodes. when the sink moves, the original one-hop node will select the nearest neighbor node of the original sink as the next hop if it is out of the radio range of the current sink. Each time a new location of the sink is stationed in the network for a while to collect the data of the whole network, the sink has to maintain the periodical movement to balance the network energy consumption in the inner area.
## 5 Results and Discussion
### [45\({}^{\circ}\)]\({}_{6}\) Model Calculation Results and Validation
#### 5.1.3 Fibre Damage  
When the torque reached \(-\)9940.31 Nm, the MDI of layer 6 fibres reached 1.013, which indicates that the fibres exhibited damage first; this corresponds to the stiffness fracture at point d in Figure 15a. This outcome is consistent with the signal of interface cracking monitored by the acoustic emission shown in Figure 10a. The large amount of plastic deformation that occurred after the damage to the matrix led to the fracture of the SiC fibres. Point e in Figure 15a shows the stiffness reduction corresponding to fibre damage when the load reached \(-\)10,136.80 Nm. The trend of MDI values of each layer of the fibre and matrix under this load is shown in Figure 17; the trend of the MDI of the matrix and fibre was the same. When the 1 st and 6 th layers of the matrix in the reinforced section almost failed, the matrix became useless and debonding with the fibre led to the inability to equalize the load. Therefore, the fibres experienced transverse instability, resulting in the fibre MDI of the 1 st and 2 nd layers reaching 1, along with the 5 th and 6 th layers, which began to lose stability and fail. This result means that the model started to crack from the 1 st and 6 th layers to the intermediate layers of the reinforced section. Point f in Figure 17a shows the stiffness fracture corresponding to the fibre damage when the load reached \(-\)10,282.00 Nm, at which time all layers of the fibres in all directions fractured.  
The damage zone of the fibres gradually became larger with increases in torque; when the torque reached \(-\)10,136.80 Nm, a large number of fibres were sheared short by the titanium matrix, and the fibre failure area was extended in the 45\({}^{\circ}\)direction, as shown in Figures 20 and 21, which corresponds to the fibre ply angle. When the torque increased to \(-\)10,282.00 Nm, all layers of the fibres failed to fracture. The damaged area almost covered the entire U-shaped section, as illustrated in Figure 22, and the layers gradually lost their load-bearing properties. Finally, catastrophic damage occurred due to the loss of the load-bearing properties.  
Figure 19: M = \(-\)10,136.80 Nm matrix MDI: **(a–f)**: 1 st layer–6 th layer.  
Figure 18: M = \(-\)7322.29 Nm matrix MDI: **(a–f)**: 1 st layer–6 th layer.
###### Abstract  
Now, with the availability of 3-D ultrasound data, a lot of research efforts are being devoted to developing 3-D ultrasound strain elastography (USE) systems. Because 3-D motion tracking, a core component in any 3-D USE system, is computationally intensive, a lot of efforts are under way to accelerate 3-D motion tracking. In the literature, the concept of Sum-Table has been used in a serial computing environment to reduce the burden of computing signal correlation, which is the single most computationally intensive component in 3-D motion tracking. In this study, parallel programming using graphics processing units (GPU) is used in conjunction with the concept of Sum-Table to improve the computational efficiency of 3-D motion tracking. To our knowledge, sum-tables have not been used in a GPU environment for 3-D motion tracking. Our main objective here is to investigate the feasibility of using sum-table-based normalized correlation coefficient (ST-NCC) method for the above-mentioned GPU-accelerated 3-D USE. More specifically, two different implementations of ST-NCC methods proposed by [PERSON] et al. and Luo-Konofagou are compared against each other. During the performance comparison, the conventional method for calculating the normalized correlation coefficient (NCC) was used as the baseline. All three methods were implemented using compute unified device architecture (CUDA; Version 9.0, Nvidia Inc., CA, USA) and tested on a professional GeForce GTX TITAN X card (Nvidia Inc., CA, USA). Using 3-D ultrasound data acquired during a tissue-mimicking phantom experiment, both displacement tracking accuracy and computational efficiency were evaluated for the above-mentioned three different methods. Based on data investigated, we found that under the GPU platform, [PERSON] method can still improve the computational efficiency (17-46%), as compared to the classic NCC method implemented into the same GPU platform. However, the [PERSON] method does not improve the computational efficiency in some configuration or improves the computational efficiency at a lower rate (7-23%) under the GPU parallel computing environment. Comparable displacement tracking accuracy was obtained by both methods.  
Keywords:motion tracking; ultrasound elastography; graphics processing unit; correlation; sum-table; block-matching +
Footnote †: journal: Applied
## 3 Experiments and Analyses
### Evaluation Indexes  
In order to comprehensively assess the performance of the proposed model, four evaluation indexes of recall, precision, mean average precision (mAP) and F1 score are adopted in here. All these evaluations are calculated based on the confusion matrix [27].  
In target detection, the intersection over union (IoU) is the overlap rate between the candidate bounding box and ground truth box. DR indicates the candidate bounding box and GT indicates the ground truth box, and the IoU is given as:  
\[I_{0U}=\frac{S_{GT}\cap S_{DR}}{S_{GT}+S_{DR}-S_{GT}\cap S_{DR}}, \tag{2}\]  
where \(S_{GT}\) represents the area of ground truth box, \(S_{DR}\) represents the area of candidate bounding box, \(S_{GT}\cap S_{DR}\) represents the intersection area of the two.  
Recall is the number ratio of correctly detected targets to all real labeled targets. It is expressed as:  
\[R=\frac{T_{P}}{T_{P}+F_{N}}, \tag{3}\]  
where \(T_{P}\) refers to the number of true positive samples, that is, the positive samples are correctly identified as positive samples, \(F_{N}\) refers to the number of false negative samples, i.e., the positive samples are incorrectly identified as negative samples.  
Precision is the number ratio of correctly detected targets to all predicted targets. It is given as:  
\[P=\frac{T_{P}}{T_{P}+F_{P}}, \tag{4}\]  
where \(F_{P}\) refers to the number of false positive samples, that is, the negative samples are incorrectly identified as positive samples.  
Figure 3: Sample dataset. (**a**) Sample 1; (**b**) Sample 2; (**c**) Sample 3; (**d**) Sample 4; (**e**) Sample 5; (**f**) Sample 6.  
Average precision (AP) is used to comprehensively measure the model quality by considering recall and precision. AP is described as:  
\[A_{t}^{c}=\frac{1}{N}\sum_{R\in\{0,0.01,...,1\}}\max\biggl{\{}P\left(\overset{ \sim}{R}\right)\biggr{\}}, \tag{5}\]  
where \(t\) is the threshold of IoU, \(c\) is the given category number, \(N\) is the number of confidence thresholds selected, \(\overset{\sim}{R}\) is the next recall value. Because there are only two categories, namely single type and adherent type, thus \(c=2\).  
The mAP is the average of AP of all categories. It is calculated as:  
\[m_{AP}=\frac{\sum_{i=1}^{c}A_{i}}{c}. \tag{6}\]  
For cell target detection, the larger the mAP value, the better the detection performance of the model, thus the higher the recognition rate.  
F1 score also combines precision and recall and is their harmonic mean. It avoids the single maximum value of precision or recall to comprehensively reflect the overall index. F1 is expressed as:  
\[F_{1}=\frac{2\times P\times R}{P+R} \tag{7}\]