esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Optimization and Training Process  
The training process begins by broadcasting the global model \(M^{r}\) to all clients at the start of each training round. This initial global model serves as the baseline from which each client will further customize and improve the model based on its specific dataset, whether it is high-resource (e.g., English) or low-resource (e.g., Twi). Each client independently computes local updates using a loss function \(\mathcal{L}_{c}\) that is specifically tailored to reflect the linguistic characteristics and needs of the data it holds. For the low-resource clients, such as those handling the Twi dataset, a higher aggregation weight \(\alpha_{\text{LR}}\) is assigned to the updates. This weighting approach is crucial as it ensures that signals from the Twi language are adequately represented and do not get diluted by the more dominant linguistic features of the high-resource languages. The local update from each client is computed as shown in Equation (11). After all clients have computed their updates, these are sent back to the central server, where they are aggregated to form the new global model. The aggregation is carefully weighted to balance and maximize the contribution of both high-resource and low-resource datasets outlined in Equation (12).  
CLOF not only enables the model to effectively converge but also guarantees that the translation quality for low-resource languages like Twi is substantially enhanced through this structured optimization and training process, as demonstrated by metrics such as SpBLEU, WER, and ROUGE. Therefore, this training methodology not only resolves the imbalance in data resources but also establishes the foundation for the expansion of the solution to other low-resource languages, thereby offering a generalized, robust solution to multilingual machine translation challenges.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
###### Abstract  
La Salle and the Observatori de l'Ebre (OE) have been involved in a remote sensing project in Antarctica for the last 11 years. The OE has been monitoring the geomagnetic activity for more than twenty years and also the ionospheric activity of the last ten years in the Spanish Antarctic Station Juan Carlos I (ASJI) (62.7\({}^{\circ}\)S, 299.6\({}^{\circ}\)E). La Salle is finishing the design and testing of a low-power communication system between the ASJI and Cambrils (41.0\({}^{\circ}\)N, 1.0\({}^{\circ}\)E) with a double goal: (i) the transmission of data from the sensors located at the ASJI and (ii) the performance of an oblique ionospheric sounding of a 12,760 km HF link. Previously, La Salle has already performed sounding and modulation tests to describe the channel performance in terms of availability, Signal-to-Noise Ratio (SNR), Doppler spread and delay spread. This paper closes the design of the physical layer, by means of the channel error study and the synchronization performance, and concludes with a new physical layer proposal for the Oblique Ionosphere Sounder. Narrowband and wideband frames have been defined to be used when the oblique sounder performs as an ionospheric sensor. Finally, two transmission modes have been defined for the modem performance: the High Robustness Mode (HRM) for low SNR hours and the High Throughput Mode (HTM) for the high SNR hours.  
physical layer; modulation; synchronization; symbol error rate; geomagnetism; observatories; ionosphere; HF modem; long-haul link; remote sensing 2016 8330
## 4 Solution Based on Greedy Strategy State Transition Algorithm for Optimization
### Construction of a State Transition Algorithm Based on Greedy Strategy
#### 4.2.2 Design Steps for the Shift Transformation Operator State Transition Algorithm  
* **Step 1**--Neighborhood and sampling.  
The set of candidate solutions generated by the state transition operator acting on the current \(x_{k}\) forms a neighborhood \(N_{x_{k}}^{operator}\), where the operator is a specific operator. The pseudocode for the random sampling strategy in the successive state transition Algorithm 1 is shown below:  
\begin{tabular}{l} \hline \hline (1) & **for \(i\gets 1,SE\) do** \\ (2) & \(State(\cdot;i)\leftarrow\textbf{Best}_{k}+\alpha\dfrac{1}{n\|\textbf{Best}_{k }\|^{2}}R_{t}\textbf{Best}_{k}\) \\ (3) & **end for** \\ \hline \hline \end{tabular} where \(\textbf{Best}_{k}\) represents the current optimal solution; \(SE\) represents the search intensity and sequence size; \(State\) represents the size of the sequence generated by the mobile transformation under \(\textbf{Best}_{k}\), which forms the set of states \(SE\). \\ \hline \end{tabular}  
* **Step 2**--Selection and update.  
In the state transition algorithm, it is necessary to select the \"optimal solution\" from the set of states of \(SE\) sequences, then compare it with the quality of the optimal solution \(\textbf{Best}_{k}\) and update it.  
\[\textbf{Best}=\left\{\begin{array}{ll}\textbf{newBest},&\text{if }f(\textbf{ newBest})<f(\textbf{Best})\\ \textbf{Best},&\text{else}\end{array}\right.\]  
where \(\textbf{newBest}\) is the \"optimal solution\" selected from the set of states.  
* **Step 3**--Alternating swap.  
To find the global optimal solution in the shortest possible time, the state transition algorithm includes global search operators and local search operators, which are alternately used. The pseudocode for the continuous state transition Algorithm 2 is as follows:  
\begin{tabular}{l} \hline \hline \((1)\textbf{Best}\leftarrow\textbf{Best}_{0}\) \\ \((2)\textbf{repeat}\) \\ \((3)\) & **if \(\alpha<\textbf{a}_{\min}\) then** \\ \((4)\) & \(\alpha\leftarrow\textbf{a}_{\max}\) \\ \((5)\) & **end if** \\ \((6)\) & \(\textbf{Best}\leftarrow\text{shift}(funfcn,\textbf{Best},SE,\beta,\gamma)\) \\ \((7)\) & \(\alpha\leftarrow\frac{\alpha}{f\epsilon}\) \\ \((8)\textbf{until}\) _some termination condition is met_ \\ \hline \hline \end{tabular}  
The flowchart used for solving the car PBS optimization scheduling problem using the state transition algorithm based on greedy strategy is shown in Figure 5.  
Figure 4: Illustration of the shift transformation operator.
# Wall Structure Geometry Verification Using TLS Data and BIM Model
## 2 Building Structures' Geometry Verification
### Plane Segmentation from Point Clouds
input point cloud (a), next to the 102,400 nearest neighbors (all the points inside the yellow circle) to the selected seed point (b), followed by the 1,638,400 nearest neighbors (colored by yellow and cyan) (c), and the inlier points (d) with red color for the chosen plane with the selected threshold value. In every iteration, the inlier points are updated (from the nearest neighbors) based on distance criterion, and parameters of the plane are re-estimated using all the inlier points. The calculation (plane re-estimation) is repeated until the number of inliers stops increasing, so there are no more inliers for the selected plane. The steps described above automatically segment the point cloud to subsets containing points lying on the surface of the verified walls. Depending on the selected threshold value, also points in the close surroundings are segmented to the given subset. As Figure 5 shows, often there is a lot of other objects close to the scanned surface, especially when the building is in operation, but also on the construction site of a building under construction. The individual steps of our approach are demonstrated in an example of a studio in a theatre. During the first step of the segmentation process, most of the furniture was removed, as these points do not meet the criterion defined by the distance threshold. The parts of the wall structure behind the removed objects (from the point of view of the laser scanner) are the empty areas in Figure 6. The points are colored, based on images taken by the instrument (RGB). The figure shows that the resulting subset of the point cloud also contains points not lying on the surface of the wall, e.g., posters, electrical sockets, and switches. The subset also contains doors. To solve the issue and to isolate points belonging to the surface of the walls, additional filtration procedures were created. These are based on local normal vectors and radiometric information (intensity, RGB) derived from the point cloud. The filtration can be divided into two main steps:  
* Normal vector-based filtration,
* Curve segmentation. Figure 4: Plane segmentation procedure: (**a**) input point cloud, (**b**) seed point and the 102,400 nearest neighbors, (**c**) 1,638,400 nearest neighbors, (**d**) inlier points. Figure 5: Original point cloud.
###### Abstract  
In the decommissioning of nuclear facilities, activated steel often contains radionuclides such as \({}^{55}\)Fe and \({}^{63}\)Ni, which are categorized as hard-to-measure due to their emission of only low-energy beta particles or X-rays. In samples exhibiting very low radioactivity, close to background levels, a large quantity of steel must undergo extensive physical and chemical processing to achieve the Minimum Detectable Activity Concentration (MDC) necessary for clearance, recycling, or reuse. Italian regulations set particularly stringent clearance levels for these radionuclides (1 Bq/g for both \({}^{55}\)Fe and \({}^{63}\)Ni), significantly lower than those specified in the EU Directive 2013/59 (1000 Bq/g for \({}^{55}\)Fe and 100 Bq/g for \({}^{63}\)Ni). Additionally, Italian authorities may enforce even stricter limits depending on specific circumstances. The analytical challenge is compounded by the presence of large amounts of non-radioactive Fe and Ni, which can cause color quenching, further extending analysis times. This study presents a reliable and optimized method for the quantitative determination of \({}^{55}\)Fe and \({}^{63}\)Ni in steel samples with activity levels approaching regulatory thresholds. The methodology was specifically developed and applied to steel from the Frascati Tokamak Upgrade (FTU) facility, under decommissioning by ENEA. The optimization process demonstrated that achieving the required MDCs necessitates acquisition times of approximately 5 days for \({}^{55}\)Fe and 6 h for \({}^{63}\)Ni, ensuring compliance with stringent regulatory requirements and supporting efficient laboratory workflows.  
decommissioning; clearance; LSC; \({}^{55}\)Fe and \({}^{63}\)Ni isolation; quenching Article  
Nuclear Department, ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Research Center Casseca, 00123 Rome, Italy; [EMAIL_ADDRESS] (M.L.C.); [EMAIL_ADDRESS] (T.G.); [EMAIL_ADDRESS] (G.A.M.)  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]
## 3 Results
### Antifungal Activity  
Both essential oils, EOL and EOF, showed strong antifungal activity against _C. albicans_, with MIC values of 250 \(\mu\)g/mL and 125 \(\mu\)g/mL, respectively. Although interesting, the  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Test Microorganisms**} & **EOL** & **EOF** \\  & **MIC * (\(\mu\)g/mL)** & **MIC * (\(\mu\)g/mL)** \\ \hline _Staphylococcus aureus ATCC25923_ & 500 & 500 \\ _Enterococcus faecalis ATCC29212_ & 250 & 250 \\ _Bacillus subtilis ATCC6633_ & 500 & 250 \\ _Escherichia coli ATCC25922_ & 1000 & 1000 \\ _Klebisella pneumothiae ATCC3083_ & 1000 & 1000 \\ _Salmonella Abony NCTC6017_ & 1000 & 500 \\ _Pseudomonas aeruginosa ATCC27853_ & 1000 & 1000 \\ \hline \hline \end{tabular}  
* MIC—minimal inhibitory concentration (ug/mL) range based on [29], (strong inhibition MIC \(<\) 500 \(\mu\)g/mL; moderate inhibition 600 \(\mu\)g/mL \(<\) MIC \(<\) 1500 \(\mu\)g/mL; weak inhibition \(>\) 1600 \(\mu\)g/mL).  
\end{table}
Table 2: Antibacterial activity of laurel (_Laurus noblils_ L) leaves (EOL) and fruits (EOF) essential oil.  
Figure 2: Antimicrobial activity of laurel leaves and fruits essential oil, EOL and EOF, respectively (MIC values were given in \(\mu\)g/mL).  
presented results of the antifungal activity were achieved by testing a single standard strain of _C. albicans_. Future studies should include other fungal species as well as clinical isolates.
## Appendix A Resolvent Theory for Classical Operators
The kernel may be represented as  
\[g\Big{(}\mathbf{x},\mathbf{x}^{{}^{\prime}}\Big{)}=\sum_{n}\frac{1}{v_{n}}u_{ n}(\mathbf{x})\cdot u_{n}\Big{(}\mathbf{x}^{{}^{\prime}}\Big{)}. \tag{11}\]  
The kernel  
\[\Gamma\Big{(}\mathbf{x},\mathbf{x}^{{}^{\prime}};v\Big{)}=\sum_{n}\frac{1}{v_ {n}-v}u_{n}(\mathbf{x})\cdot u_{n}\Big{(}\mathbf{x}^{{}^{\prime}}\Big{)} \tag{12}\]  
is referred to as the resolvent kernel of integral Equation (11) [58]. The kernel (11) and resolvent kernel are coupled via the Hilbert-Schmidt Relation [58]:  
\[-g\Big{(}\mathbf{x},\mathbf{x}^{{}^{\prime}}\Big{)}+\Gamma\Big{(}\mathbf{x}, \mathbf{x}^{{}^{\prime}};v\Big{)}=\
u\sum_{n}\frac{1}{(v-v_{n})v_{n}}u_{n}( \mathbf{x})\cdot u_{n}\Big{(}\mathbf{x}^{{}^{\prime}}\Big{)}. \tag{13}\]  
The general solution of Equation (11) may be efficiently presented in terms of the resolvent operator. It may be written as  
\[E_{\
u}(\mathbf{x})=E_{\
u 0}(\mathbf{x})+\
u\int_{S}\Gamma\Big{(}\mathbf{x}, \mathbf{x}^{{}^{\prime}};v\Big{)}E_{\
u 0}\Big{(}\mathbf{x}^{{}^{\prime}} \Big{)}d\mathbf{x}^{{}^{\prime}}. \tag{14}\]  
One more identity will be useful in our future consideration:  
\[\bigg{(}\frac{1}{v_{n}}-\frac{1}{v_{m}^{*}}\bigg{)}\int_{S}u_{n}(\mathbf{x}) \cdot u_{m}^{*}(\mathbf{x})d\mathbf{x}=2i\int_{S}\int_{S}\mathrm{Im}\Big{(}g \Big{(}\mathbf{x},\mathbf{x}^{{}^{\prime}}\Big{)}\Big{)}u_{n}(\mathbf{x}) \cdot u_{m}^{*}(\mathbf{x}^{{}^{\prime}})d\mathbf{x}d\mathbf{x}^{{}^{\prime}}. \tag{15}\]  
It may have a proven similarly to orthogonality Relation (10); however, instead of Relation (10) with index \(m\), its complex conjugated equation should be used.
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 2 Preliminaries
### Petri Net Fundamentals
In the timed Petri net, the transition \(T_{j}\) is enabled at time \(k\) when each input place \(P_{i}\) of \(T_{j}\) has more than or equal to \(F(P_{i},T_{j})\) tokens, and its time stamp is no more than \(k\). By firing \(T_{j}\) at time \(k\), the marking is changed according to the same rule as the Petri net described above, except that we attach the time stamp \(k+FD[T_{j}]\) to each output token. A colored Petri net is an extension of Petri nets, where we can introduce values (colors) to tokens, guard functions to transitions for their firing conditions, and arc functions to output arcs of transitions to calculate the colors of the output tokens [24]. Therefore, tokens become much more informative and allow us flexible and efficient modeling. This extended Petri net is denoted by \(CPN=(\Sigma,P,T,F,V,C,G,E,\mathbf{M}_{0})\), where \(P\), \(T\), and \(F\) represent a set of places, transitions, and arcs, respectively. \(\Sigma\) shows a set of color types, and \(C:P\to\Sigma\) is a color function for the places. Only tokens with colors specified by \(C(P_{i})\) are located in place \(P_{i}\). In addition, \(V\) is a set of arc variables, where \(\text{Type}(v)\in\Sigma\) for \(v\in V\). Moreover, \(\text{Type}(v)\) is the type of variable \(v\). Here, \(E\) denotes an arc function where \(E((T_{j},P_{i}))\) returns tokens on \(C(P_{i})\) for each output place \(P_{i}\) based on the binding values to each of the arc variables. The term \(G\) represents a guard function that returns a Boolean value to determine whether the corresponding transition is enabled. A marking \(\mathbf{M}_{k}\) at step \(k\) is a mapping of multiple sets of \(C(P_{i})\) to each place. Transition \(T_{j}\) is enabled at marking \(\mathbf{M}_{k}\) with binding \(b\) when for each input place \(P_{i}\) of \(T_{j}\),  
\[M_{k}(P_{i})\geq E((P_{i},T_{j}))(b) \tag{4}\]  
where \(E((P_{i},T_{j}))(b)\) denotes the result of the arc function for arc \((P_{i},T_{j})\) when we bind \(b(v)\) for each variable \(v\) in \(E((P_{i},T_{j}))\). At marking \(\mathbf{M}_{k}\), \(\mathbf{M}_{k+1}\) is generated by firing \(T_{j}\) with binding \(b\):  
\[M_{k+1}(P_{i})=M_{k}(P_{i})-E((P_{i},T_{j}))(b)+E((T_{j},P_{i}))(b). \tag{5}\]  
Colored and timed Petri nets are extremely powerful in the sense that complicated systems can be easily modeled. This fact contributes tremendously to modeling combinatorial optimization problems in our research. Another essential benefit of using Petri net modeling is the well-systematized formulation of behavioral and structural properties. These have been studied in Petri net research [16; 17; 23]. The behavioral properties includes _reachability_, _boundedness_, _coverability_, _liveness_, and _persistence_. The structural properties, such as _precedence relation_, _structural boundedness_ and _conservative_, are initial-marking independent and can be derived from the structure of the bipartite graph. These fundamental properties can be used for Ising model formulation from Petri net models.
## 3 Results and Discussion
### Induction of Drug Resistance in C. albicans Cells  
AMPs have a different mechanism of action from conventional antibiotics, which provide the advantage of effectively inhibiting the growth of drug-resistant bacteria and take a long time to induce drug resistance. Therefore, drug-susceptible _C. albicans_ were incubated with fluconazole or peptides, and changes in MIC over 15 passages were monitored (Figure 7). Surprisingly, the MIC of fluconazole-treated fungi increased exponentially from three passages and increased 1024-fold after 14 passages, confirming that drug resistance was easily induced. However, the growth of fungi treated with the peptides was inhibited at the same concentration range after 15 passages (Figure 7). This suggests that HnMc variant peptides do not induce drug resistance in the short term.
# Study on Mechanical Behavior of a New Three-Dimensional Sensor Under Rock Rheology
## 2 Three-Dimensional borehole Full Stress Monitoring Theoretical Analysis
### Uniaxial Compression Test of Pressure Film  
On the basis of the above theory, it is further ensured that the pressure film material and the designed structure can meet the function of reflecting the stress of surrounding rock through deformation. Considering the influence of pressure film thickness on mechanical properties, application environment and durability, three kinds of pressure membranes with film thicknesses of 0.5 mm, 0.8 mm and 1.0 mm were selected for uniaxial compression test to meet the primary requirements of sensitivity and strength. Therefore, a universal testing machine was used to pressurize the pressure membranes of three specifications at a rate of 0.01 KN / S in the laboratory. The experimental process is shown in Figures 6 and 7. The results of the uniaxial compressive test data of the pressure film are shown in Figure 8.  
Figure 6: Uniaxial compression test of pressure film.  
Figure 7: Pressure film specimen in yield stage.  
Figure 8a reflects the curve of the force and displacement of the pressure film under the condition of 0.5 mm film thickness, where \(P\) is the yield limit point of 0.5 mm pressure film parts; Figure 8b reflects the change curve of the force and displacement of the pressure film under the condition of 0.8 mm film thickness, where \(P^{\prime}\) is the yield limit point of 0.8 mm pressure film parts; Figure 8c shows the curve of the force and displacement of the pressure film under the condition of 1.0 mm film thickness, and the point \(P^{\prime\prime}\) is the yield limit point of the 1.0 mm pressure film part. All three figures have an obvious feature, that is, before the yield limit point of the pressure film material, the deformation amount and the force size basically show a linear growth relationship. Therefore, the pressure film material and the design structure are reasonable. The pressure film parts can clearly reflect the external stress through their own deformation and transfer to the oil.  
The expected service life of the pressure film is estimated based on experimental data and simulations conducted in controlled environments. The film's durability in real-world conditions depends on various factors, including loading conditions, environmental temperature, humidity and the material properties of the film. To more accurately assess its long-term performance, further extended testing is needed.
# Wedge Design and Dynamic Characteristics Analysis of Unequal-Length Wedge Slant Support Clutch
## 2 Unequal-Length Wedge Diagonal Brace Clutch Design
### Unequal-Length Wedge Design  
#### 2.2.1. Wedge Difference Quantity  
The wedge difference is an important part of unequal-length wedge design and directly influences the clutch wear life, impact torque, and critical off speed, defined as the difference in the middle section length of the different wedges. The unequal-length wedge in the clutch is divided into a short wedge block and long wedge block, where the short wedge block is the original wedge block in the clutch and the long wedge block is the wedge block obtained by extending the middle segment of the wedge block. The shape and size of the upper and lower cams are identical; only the length of the middle segment and distance between the center of the cam on the wedge and center of the lower cam on the wedge are different, as indicated in Figure 3.  
The materials for the inner and outer rings, cage, and wedge of the clutch are 16 Cr3 NiWMoVNbE-YJ.0225, 1 Cr11 Ni2W2 MoV, and GCr15, respectively. For engineering applications, the precision of the wedge can reach IT4 accuracy standard or higher with a change in the clutch. The length of the middle section varies by about 0.005 mm, the length of the wedge is 6.100 mm, wedge width is 4.540 mm, and center distance is 0.324 mm. Therefore, 0.005 mm is used as the gradient for the wedge differential design. To investigate the influence of the wedge difference on the clutch differential engagement characteristics, five wedges were designed, as listed in Table 1.  
Figure 3: Design of unequal-length wedge difference.  
Figure 2: (**a**) 3D view of the clutch; (**b**) differential engagement process of slow coupling clutch.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 2 Conventional Interpretation Techniques for Bender Element Tests
### Time Domain Methods
#### 2.1.1 First Arrival of the Shear Wave  
The estimation of the travel time was first performed by the direct visual identification of the arrival of the shear wave on the output signal. This technique was first introduced in geophysical field testing in [27] and was then adopted in geotechnical laboratories [21; 22; 25; 26]. While points C (sometimes called _first sharp_, which is the designation adopted in this paper), D (_first bump_), or E (_zero crossing_) in Figure 1 are all good candidates for the shear wave arrival [12; 28], [PERSON] et al. [8] showed that determining which of them is the actual arrival time is not straightforward. Moreover, traces of compression waves tend to pollute the readings and blur the exact location of the first arrival, introducing subjectivity into the technique [15].