esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Evaluation  
An objective assessment of the predictive performance of our classifiers was conducted using a 10-fold cross-validation approach. The dataset was divided into ten partitions, with nine partitions used for training and one for testing. This process was iterated ten times, and the average accuracy across these iterations was used as the final estimate of model performance.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Hyperparameter** & **Search Grid** & **Optimal Value** \\ \hline \multirow{4}{*}{XGBoost} & n\_estimators & [100, 500, 1000] & 500 \\  & learning\_rate & [0.1, 0.2, 0.3] & 0.2 \\  & max\_depth & [3, 4, 5] & 3 \\ \hline \multirow{4}{*}{SVM} & kernel & [’linear’, ’rbf’, ’poly’, ’sigmoid’] & rbf \\  & C & [0.01, 0.1, 1, 10, 100, 100] & 1 \\  & gamma & [1, 0.1, 0.01, 0.001, 0.0001] & 1 \\  & probability & [True, False] & True \\ \hline \multirow{4}{*}{1D-CNN} & learning\_rate & [\(1\times 10^{-4}\), \(1\times 10^{-3}\), \(1\times 10^{-2}\)] & \(1\times 10^{-3}\) \\  & batch\_size & [32, 64, 128] & 64 \\  & epochs & [50, 100] & 50 (with Early Stopping) \\  & filters (Conv1D) & [64, 128, 256] & 128 \\  & kernel\_size (Conv1D) & [3, 5] & 5 \\ \hline \multirow{4}{*}{BiLSTM} & learning\_rate & [\(1\times 10^{-4}\), \(1\times 10^{-3}\), \(1\times 10^{-2}\)] & \(1\times 10^{-3}\) \\  & batch\_size & [32, 64, 128] & 64 \\ \cline{1-1}  & epochs & [50, 100] & 50 (with Early Stopping) \\ \cline{1-1}  & lstm\_units & [64, 128, 256] & 128 \\ \cline{1-1}  & recurrent\_dropout & [0.0, 0.2, 0.4] & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter search grid and optimal values for selected classifiers.  
To comprehensively evaluate the models, five commonly used metrics were employed: sensitivity (Sn), specificity (Sp), Matthews correlation coefficient (MCC), accuracy (Acc), and area under the receiver operating characteristic (ROC) curve (AUC). The formulas for these metrics are as follows:  
\[\text{Acc} =\frac{TP+TN}{TP+FP+FN+TN} \tag{1}\] \[\text{Sn} =\frac{TP}{TP+FN}\] (2) \[\text{Sp} =\frac{TN}{TN+FP}\] (3) MCC =\frac{(TP\times TN)-(FP\times FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)( TN+FN)}} \tag{4}\]  
Acc measures the proportion of correctly predicted samples over the total samples. Sn measures the proportion of actual positives correctly identified, also known as Recall. Sp measures the proportion of actual negatives correctly identified. MCC considers true and false positives and negatives to provide a balanced evaluation, while AUC indicates the model's ability to distinguish between classes.
# A Fading Suppression Method for \(\Phi\)-OTDR Systems Based on Multi-Domain Multiplexing
## 5 Conclusions  
In this paper, we propose a fading suppression method for \(\Phi\)-OTDR systems based on MDM, which fuses the advantages of spatial weighting, PDM, and FDM, obtaining the fading-free demodulated phase. By means of parameter selection and optimized combination of the domain in MDM, an optimal solution for anti-fading can be achieved between performance and hardware complexity, which promotes the development of the fading suppression technology of \(\Phi\)-OTDR. This provides a guide for the high-fidelity sound field sensing of \(\Phi\)-OTDR.  
Figure 13: The fading probabilities of all combinations in the P and S states.  
**Author Contributions:** Conceptualization, S.T. ([PERSON]); methodology, S.T. ([PERSON]), S.T. ([PERSON]) and Y.Z.; investigation, Y.L. and N.Y.; resources, X.Z. and Y.Z.; writing--original draft preparation, S.T. ([PERSON]), S.T. ([PERSON]) and C.Z.; writing--review and editing, S.T. ([PERSON]) and S.T. ([PERSON]); visualization, Y.L., N.Y. and D.Z.; supervision, X.Z., Y.Z., C.Z., H.L. and D.Z.; funding acquisition, X.Z., Y.Z. and N.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China (62175100, U2001601); Equipping Pre-research Project (30601010104); Fundamental Research Funds for the Central Universities (2024300447, 0213-14380211, 0213-14380625); General Program of the Shenzhen Science, Technology, and Innovation Commission (JCYJ20220530113811026); Shenzhen Research Foundation, grant No. KJZD20230923114601003; CAS Pioneer Hundred Talents Program; Shenzhen Science and Technology Program (JSGGKQTD2022110115656030); Science, Technology and Innovation Commission of Shenzhen Municipality (YFJGJS1.0).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All original data, vibration data, and code will be made available upon request to the corresponding author's email with appropriate justification.  
**Acknowledgments:** We are very thankful to the reviewers and editor for their valuable comments and assistance, which have been instrumental in enhancing the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Key Concepts
### Holonic Control Architecture  
In the late sixties, the term holon has been introduced for the first time by philosopher [PERSON] [30]. [PERSON] developed the term as a basic unit in his explanation of the evolution of the biological and social structures. Based on his observations that organisms (e.g., biological cells) are autonomous self-reliance units, which have a certain degree of independent control of their actions, yet they still subject to higher level of control instructions. His conclusion was that any organism is a whole \"holos\"  
Figure 6: Drools Pattern Matching Mechanism.  
and a part \"on\" in the same time, which derived the term holon [31]. The concept of holon has been adopted in the early nineties by the intelligent manufacturing systems (IMS) consortium, to define a new paradigm for the factory of the future.  
The following terminologies has been defined by the IMS to provide a better understanding of the Holonic Control Architecture (HCA):  
* Holon: an autonomous cooperative building block of the manufacturing system that can be used to transform, transport, store and validate the information and the physical signals [32]. The holon structure is explained in detail in Figure 7a.
* Autonomy: the capability of the holon to create and control the execution of its own plans.
* Cooperation: a process whereby a set of holons develop mutual plans to execute them together.
* Holarchy: a system of holons which cooperate to achieve a goal or objective. The holarchy defines the basic rules for cooperation of the holons and thereby limits their autonomy.  
The HCA is a distributed control and communication topology which divides the manufacturing process tasks and responsibilities over different holon categories. Product-Resource-Order-Staff-Architecture (PROSA) which can be seen in Figure 7b is the most known HCA model [33]. The PROSA basic holons are:  
* Product Holon (PH): is responsible for processing and storing the different production plans which are required to insure the correct manufacturing of a certain product. Different production plans are needed to manufacture different products within the same workcell. Therefore, the PH has to match the required product type with its plans.
* Order Holon (OH): is responsible for composing, managing the production orders. Furthermore, in a small scale enterprise, it should assign the tasks to the existing operating resources and monitor the execution status of the assigned tasks.
* Operational Resource Holon (ORH): is a physical entity within the manufacturing system, it can represent a robot, machine, worker, etc.
## 2 Methodology
### Fusion Block  
In order to effectively integrate the encoding features from both the CNN branch and Transformer branch, the features are fused using the fusion block, which combines the channel attention mechanism and the spatial attention mechanism. Specifically, the fused feature representation is obtained through the following operations.  
\[T_{i}=Channel.Attn(t_{i}) \tag{7}\]  
\[G_{i}=Spatial.Attn(g_{i}) \tag{8}\]  
\[b_{i}=Conv\Big{(}t_{i}W_{1}^{i}\odot g_{i}W_{2}^{i}\Big{)} \tag{9}\]  
\[h_{i}=Residual([b_{i},T_{i},G_{i}]) \tag{10}\]  
where \(W_{1}^{i}\in{}^{D_{i}\times L_{i}}\), \(W_{2}^{i}\in{}^{C_{i}\times L_{i}}\), \(\odot\)\(|\) are the Hadamard products, and Conv is a 3 \(\times\) 3 convolutional layer.  
Channel attention is implemented using the SE Block proposed in [23], as shown in Figure 3, to facilitate the incorporation of global information from the Transformer branch. In the fusion block, channel attention is applied first to identify the specific positions of globally significant information in the feature maps from the Transformerbranch, aiming to enhance the feature representation capability. Due to the presence of noise in the raw data captured by image sensors and the aggregation of operations such as convolution and pooling during feature extraction, this noise may be further amplified. Consequently, in the CNN branch, low-level features may be affected by certain levels of noise. Ref. [24] proposed weighting in the spatial dimension of the feature maps. Through spatial attention, the network can better focus on crucial regions while disregarding irrelevant areas. Subsequently, the feature matrices from the two branches, with the same dimensions, are element-wise multiplied using the Hadamard product. Next, the concatenated interaction feature \(b_{i}\) and attention features \(G_{i}\), \(T_{i}\) are computed using the residual block. The resulting feature \(h_{i}\) effectively captures the global and local context of the current spatial resolution. Finally, the attention-gated (AG) method [16] and skip connection are combined (see Equation (11)) to generate the final segmentation result, as shown in Figure 1.  
\[H_{i-1}=Conv([Up(H_{i}),AG(h_{i-1},Up(H_{i}))]) \tag{11}\]
# Multipath Interference Impact Due to Fiber Mode Coupling in C+L+S Multiband Transmission Reach
## 3 Quality of Transmission Model
Lastly, the MPI noise power, \(P_{\text{MPI},i}\), in channel \(i\) originated in G.654E SMFs, where multiple replicas of the transmitted signal, due to the appearance of LP\({}_{01}\)-LP\({}_{11}\) coupling, can be modeled with Equation (3), which defines the MPI level. It is assumed that the MPI noise power in channel \(i\), \(P_{\text{MPI},i}\), after several identical spans, \(N_{span}\), is just the sum of the MPI noise powers of every span along the lightpath, as in [14,23], and can be given by  
\[P_{\text{MPI},i}(f_{i})=MPI\cdot P_{ch,i}\cdot N_{span} \tag{11}\]
## 4 Methods Used for Fault Diagnosis
### Differential Protection Methodology  
In advanced multi-terminal DC microgrids, the extension of short-circuit protection units is essential to ensure the safety of the whole system. In this method, it is obtained by the installation of local measurement and communication devices. Also, by computing the unknown parameters of the DC distribution line in short-circuit situations, as well as using communication links among protection components and SSCB, it is able to present fast fault protection (around 10 \(\upmu\)s) [25]. A reliable communication system needs to be implemented and interconnected with the local data among the terminals of the protective components synchronously. Additionally, the possibility of failure in data exchange and the interconnection delays is increased, resulting in low operation speed. Also, because of the mandatory emergency protection system, the system size and initial cost will also be increased [26].
# Transformative Technology for FLASH Radiation Therapy
## 3 High-Gradient Ion Linacs for FLASH-RT Developed by Argonne National Laboratory and RadiaBeam
### Future Developments for FLASH-RT  
In addition to the general development of high-gradient accelerator structures for low-velocity ions, we identify a few areas of R&D of special importance for FLASH-RT [41] with ion beams:  
* Investigating and pushing the beam current limit of compact ion linacs;
* Increasing the repetition rate of high-gradient structures;
* Developing RF sources capable of delivering the required high pulsed power.  
More importantly, and to enable this technology, establishing a linac-based advanced ion therapy research center in one of the National Labs would be a significant step forward and would allow the following:  
* Cancer therapy and radiobiology research with all ion beams up to neon;
* Radiography and tomography with ions lighter than carbon: proton, helium, etc.;
* Real-time MRI guidance during beam delivery, significantly enhancing the outcome of ion beam therapy;
* PET imaging using positron emitters (C-11, N-13, O-15, etc.) produced in the tumor for dose verification;
* FLASH ion therapy (FLASH IT) and other novel approaches.  
Figure 4: Design, fabrication, and high-power testing of the negative-harmonic traveling-wave structure (NHS) developed by RadiaBeam in collaboration with Argonne.  
Figure 5: Design, cold model, and measurements of the annular-coupled structure (ACS) developed by Argonne in collaboration with RadiaBeam.  
We mention, in particular, an ACCIL-type linac that could be installed at the existing IPNS site at the Argonne National Lab with the required infrastructure [42], which represents a significant cost saving compared to a greenfield installation [43]. Following the development and commissioning phases, an initial research program including cellular radiobiology and animal therapy could be conducted prior to human therapy and early clinical trials to prepare for FDA approval.
## 1 Introduction
Experimental tests were performed on the basis of the ability to predict, through numerical simulations, the oscillatory behavior of the gearbox, i.e., a typical dynamic quantity used to objectively evaluate the acceleration of the points on the outer surface of the gearbox housing [11]. [PERSON] et al., using simulation techniques and experimental studies, created a new seal for an outer radius that is designed to improve the torque transmission of a magnetorheological clutch, and the transmitted torque was measured and studied for improvement [12]. [PERSON] et al. designed a new drum magnetorheological clutch with a wedge-shaped boundary, and experimental results showed that the proposed wedge-shaped drum magnetorheological clutch generated more torque than the existing drum magnetorheological clutch, thus resulting in better performance [13]. [PERSON] et al. proposed a magnetorheological fluid-based differential (planetary) gear transmission that variably connects motor power to a load attached to the planetary carrier output through a sun gear input [14]. [PERSON] and [PERSON] et al. proposed the study of magnetic gears through multi-objective optimization. A multi-objective optimization approach was proposed to maximize the transmitted torque per unit mass of the magnetic gears while minimizing the moment of inertia of the moving parts. Self-performance optimization is a deterministic optimization algorithm combined with a weighted-sum approach of objectives, and the results were discussed and compared with those of commercial applications [15]. [PERSON] et al. proposed a magnetic gear (MG) that achieves the same functions as a differential while having unique advantages such as reduced maintenance and high efficiency, and they verified it through experiments and simulations [16]. Many studies have been conducted on power transmission devices using MR fluid in automobiles, but most of them involve the power transmission using only the sheer force of the MR fluid;thus, there has been no research conducted that addresses power transmission through mechanical friction [17; 18; 19]. In this paper, based on a detailed analysis of the axial magnetic induction intensity generated by the coil along the radial distribution, a shear-mode MR (magnetroheological) clutch with uniform magnetic field distribution along the radial direction is proposed for a tension control system. MR fluid is a smart material that provides a solution to mechanical problems in power distribution systems. Accordingly, the longitudinal and lateral dynamic properties of 4 WD vehicles were examined and analyzed, and an algorithm was developed to control the front/rear power distribution using an MR fluid multi-plate clutch according to the road surface state and driving conditions. The CarSim vehicle dynamics simulation program was used to check the performance of the algorithm by conducting vehicle dynamic performance and turning stability experiments using a HILS (Hardware in the Loop) system.
# Machine Learning in Chronic Pain Research: A Scoping Review
The hyperplane is found by optimising a cost function. Decision trees (DTs) are multistage decision systems where classes are sequentially rejected until an accepted class is reached. DTs can be used both for classification and regression. A random forest (RF) is an ensemble of DTs. The output of a RF is the mode of the classes (classification) or the average prediction of the trees (regression). K-nearest neighbours (kNN) is a supervised learning method where for each data point, the distance to the training points is considered. If most of the nearest training points to the data point belong to a certain class, the data point is assigned to that class. Clustering is the task of dividing an unlabelled dataset into groups. k-means clustering is one of the most popular clustering methods. In k-means clustering, the task is to partition n data points into k clusters. Each observation is assigned to the cluster with the nearest mean. AdaBoost is the abbreviation for adaptive boosting. Boosting is a technique used for creating a strong classifier from a number of weak classifiers. Deep learning is a family of ML methods based on neural networks (NNs). The algorithms are inspired by the human brain. NNs consist of neurons arranged in different layers, similar to neurons in the brain. Previous review papers have focused on how ML and statistical methods [19; 20; 21; 22] can be utilised in the treatment of patients with chronic pain. However, these reviews had a restricted scope or were outdated in that they did not consider recent developments in ML research. An up-to-date analysis of ML research in chronic pain care is currently lacking. This is the intended contribution of the present paper. The aim of this scoping review is to summarize and disseminate research findings on the use of ML approaches in chronic pain research, to identify research gaps, and to make recommendations for future research [23]. The literature search was guided by a set of predefined search queries to major research databases. The identified publications were screened by several independent reviewers following pre-defined inclusion and exclusion rules. This rigid process ensured broad coverage of published evidence and facilitated the objective validity and relevance assessment of every publication included in the review [23]. The following research questions (RQ) will be addressed in the present review:  
* RQ1: Which ML methods have been used? * RQ2: Which data have been used? * RQ3: Were patient-generated data used? * RQ4: Which chronic pain conditions were applied with ML algorithms? * RQ5: Which stages (e.g., assessment/diagnosis or treatment) of chronic pain care were addressed? By answering the research questions, we plan to contribute to the body of knowledge by providing insight into how ML approaches may be used to improve the treatment and management of patients with chronic pain.
## 4 The Attitude Control Algorithm of Propulsion Mechanism  
To implement the 2-DOF control of the propeller attitude, it is necessary to establish a mapping model between the attitude parameters (\(\alpha\) and \(\beta\)), and the two drive joints (\(P_{2}\) and \(P_{3}\)), and motion of the mechanism (\(P_{2}R_{2}\) and \(P_{3}R_{3}\)).  
Assuming that the current attitude angles are \(\alpha\) and \(\beta\), the position parameters \(\left(x_{o},y_{o},z_{o}\right)^{\mathrm{T}}\) of the propeller can be obtained by using Equations (9), (10), and (12).  
The space coordinates of the three spherical hinges \(\mathbf{P}_{S1}^{S}(i=1,2,3)\) in the \(S\) coordinate can be obtained by using Equations (1), (2), and (8) taking the driving branched chain as an example, as shown in Figure 2.  
Because \(\mathbf{P}_{S2}^{S}\) and \(\mathbf{P}_{P2}^{S}=\left(-R/2,\sqrt{3}R/2,0\right)^{\mathrm{T}}\), this leads to  
\[S_{2}P_{2}=\mathbf{P}_{P2}^{S}-\mathbf{P}_{S2}^{S}\]  
The projection of \(S_{2}P_{2}\), \(M_{2}P_{2}\) and \(M_{2}S_{2}\), are shown as follows.  
\[M_{2}P_{2}=S_{2}P_{2}\cdot\left[0,0,1\right]^{T}=\left(\mathbf{P}_{P2}^{S}-\mathbf{P} _{S2}^{S}\right)\cdot\left[0,0,1\right]^{\mathrm{T}}\]  
\[M_{2}S_{2}=\sqrt{\left(S_{2}P_{2}\right)^{2}-\left(M_{2}P_{2}\right)^{2}}\]  
Figure 2: Driving chain \(P_{2}R_{2}S_{2}\).  
Further the motion input of driving branch chain could be obtained.  
\[d_{2}=R_{2}P_{2}=M_{2}P_{2}-\sqrt{\left(l_{2}\right)^{2}-\left(M_{2}S_{2}\right) ^{2}}\]  
In the same way, the motion input of driving branch chain \(P_{3}R_{3}S_{3}\) could be obtained also.
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 6 Conclusions  
Considerable progress has been made in MIR SC generation from ChG fibers over the past decade due to the advantages of a wide transparency window and high optical nonlinearity. Wide MIR SC spectra (>7 \(\upmu\)m) have been obtained in ChG step-index fibers, MOFs, and tapered fibers, while ultrabroad SC spectra (>13 \(\upmu\)m) have been realized in ChG fibers of cm-level fiber length when pumped with an MIR OPA. In particular, the broadest SC spectrum (from 2 to 16 \(\upmu\)m) covering the entire MIR molecular fingerprint region was achieved with a novel Ge-Te-AgI ChG fiber [126]. In addition, the use of MIR SC sources spanning 2-7.5 \(\upmu\)m from a ChG fiber in multispectral tissue imaging at wavelengths beyond 5 \(\upmu\)m has been reported very recently [5].  
MOFs and tapered fibers have great flexibility in dispersion engineering, resulting in multiple options on pump sources. A small \(A_{\mathit{eff}}\) and a large \(\gamma\) make the nonlinear power intensity dependent effects significant, which can efficiently broaden the SC spectrum. However, extra losses induced by exposure to the external environment in MOFs and the use of tapered fibers with insufficient taper waist diameters can reduce transmission efficiency. Step-index fibers have high power handling capacity and mechanical strength, which is conducive to practical applications and equipment stability. Nevertheless, the output power of the SC generation from ChG fibers is generally low, and the maximum output power has been reported to be 565 mW [99]. This will impose barriers to practical applications. Therefore, it is necessary to improve the output power by optimizing the pump scheme or using novel ChG fibers with a high threshold against laser damage. As a unique spectral characteristic, the coherence of MIR SC sources is also a significant consideration for practical applications.  
Although broadband MIR SCs can be generated from ChG fibers when pumped by OPA or OPO, these systems are generally unsuitable for practical applications, since the complexity and costliness of OPA and OPO sacrifice system robustness and compactness. Currently, MIR SC sources from ChG fibers are being developed as all-fiber optics. The most effective pump scheme to achieve all-fiber SC sources is to cascade ChG fibers to ZBLAN fibers which are pumped with MOPAs. However, it is imperative to improve the key technology of reliable, low-loss fusion splicing between silica and ZBLAN fibers, and between ZBLAN and ChG fibers. MIR SC sources with good performance from ChG fibers are likely to become commercially available in the near future.  
S.D. conceived and designed the structure of this review; S.D. and Y.W. wrote the paper; all authors contributed to the discussion and revision of the paper.  
The authors acknowledge the National Natural Science Foundation of China (NSFC) (Grants No. 61435009, 61627815) and the K. C. Wong Magna Fund in Ningbo University.  
The authors declare no conflict of interest.