esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Machine Learning Classifiers  
Three supervised ML algorithms (SVM, RF, and k-NN) are trained and compared using the feature vectors (see below) and target labels [38, 39]. The code was written using Python with its additional packages of NumPy, Pandas, sklearn, SciPy, and matplotlib [40,41]. The code can be shared on GitHub upon request. However, the authors would not be able to share the PI-Berlin dataset as it is not public.  
Support Vector Machine: SVM's core idea is finding a decision boundary (hyper-plane) that helps separate space vector/dataset into classes. The decision boundary is searched through the maximum margin classifier, which is decided by the support vectors. SVM generates an optimal hyperplane in an iterative manner, which is used to minimize errors. The distance between the nearest points is known as the margin. The hyper-plane is selected based on the maximum possible margin between support vectors [17,42]. A radial basis function (RBF) is used as a kernel function in this study, and other hyper-parameters like (penalty parameter 'C', gamma) are found by implementing a grid search to find the optimal value [43].  
Random Forest: RF is an ensemble of ML techniques that builds multiple decision tree classifiers on random sub-samples of the training dataset. Each decision tree predicts the response by following the tree's decisions from the root to the leaf. The output of each decision tree is then averaged to determine the prediction [44]. RF's main advantage is leveraging the power of a large number of randomly selected trees to represent the solution. Thus, instead of using one decision tree, RF uses all the decision trees to determine the classification; this procedure reduces errors and uncertainties [42]. In this study, the number of trees selected was 5 and 10. The minimum number of samples that are required to split an internal node is set to 25. The maximum depth of the tree is kept at five [45].  
k-Nearest Neighbors: k-NN categorizes objects based on their nearest neighbors' classes in the dataset, assuming the neighbor objects are similar. This non-parametric method does not make any assumptions regarding the underlying data distribution. Instead, it chooses to memorize the training instances used in the supervised training. This method's main limitation is its intensive time and memory requirements [17,39]. In this study, parameters are selected by implementing a grid search regarding neighbors [46].
# A PINN-Based Nonlinear PMSM Electromagnetic Model Using Differential Inductance Theory
## 4 Examples of PINN Surrogate Modeling
### PMSM Torque Calculation Based on PINN Modeling
#### 4.4.1 Torque Calculation Under SE Conditions  
The results of torque harmonics and EDS calculations for SE conditions are shown in Table 3.  
When the calculated operating point coincides with the FEA sampling point (\(I_{\text{AMP}}=450\) A, \(\beta=70\) deg), the torque and flux linkage results in Figure 9 confirm that the physical quantities used by OLAM are strictly identical to the FEA outputs, demonstrating the accuracy of the nonlinear modeling approach presented herein. This study further shows that both OLAM and PINN can accurately predict torque, with PINN achieving a 62% reduction in torque EDS compared to OLAM. Although the GLA model employs accurate flux linkage data, its torque formulation omits differential inductance; as a result, while it correctly captures average torque, it suffers significant errors in torque ripple estimation, yielding an EDS of 16.71.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Conditions/Indicators**} & \multicolumn{5}{c}{**Harmonics**} & \multirow{2}{*}{**EDS**} \\ \cline{3-6}  & & **0** & **6** & **12** & **18** & **24** \\ \hline \multirow{4}{*}{450A/70\({}^{\circ}\)} & FEA & 54.34 & 1.76 & 1.30 & 0.11 & 0.42 \\  & GLM & 54.33 & 1.04 & 2.60 & 0.25 & 0.25 \\  & OLAM & 54.27 & 1.77 & 1.28 & 0.09 & 0.46 \\  & PINN & 54.32 & 1.80 & 1.29 & 0.08 & 0.42 \\ \hline \multirow{4}{*}{490A/62\({}^{\circ}\)} & FEA & 76.74 & 2.09 & 1.73 & 0.10 & 0.36 \\  & GLM & 75.06 & 1.32 & 1.04 & 0.26 & 0.37 \\ \cline{1-1}  & OLAM & 77.16 & 2.21 & 1.72 & 0.08 & 0.31 \\ \cline{1-1}  & PINN & 76.71 & 2.08 & 1.72 & 0.10 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of torque calculation results by FEA, GLM, OLAM and PINN.  
When the operating point deviates from the FEA sampling point (\(I_{\rm AMP}=490\) A, \(\beta=62\) deg), it is evident that, due to severe magnetic saturation in the weak-field region, the GLM exhibits the largest errors, and the torque and armature flux linkage results in Figure 10 indicate that OLAM--which constructs a locally linearized model based on flux linkage, magnetic co-energy, and differential inductance at the nearest FEA sampling point--loses accuracy in the weak-field region due to the motor's complex nonlinear behavior. Although OLAM can approximate torque with a mean error of 5.4 % and an EDS of approximately 5.8, its flux linkage error is notably larger. In contrast, at non-FEA sampling points, the PINN model still delivers high-precision torque and flux linkage predictions, with torque mean error and EDS reduced to only 8% and 16% of OLAM's values, respectively, demonstrating excellent generalization capability.  
Figure 9: Torque and armature flux linkage under the SE condition computed by the FEA, PINN, OLAM, and GLM methods: (**a**) torque waveform; (**b**) torque FFT; (**c**) d-axis flux linkage; (**d**) q-axis flux linkage (\(I_{\rm AMP}=450\) A, \(\beta=70\) deg).
# Design of an Open Remote Electrocardiogram (ECG) Service
## 4 A Prototype for a Remote ECG Service
### The MCU: ADC and Serial Encoder  
The MCU for sensor data acquisition is an Arduino Uno board, the cost of which is around 10$. The connection with the ECG acquisition board is obtained by stacking them, thus linking the ADC input of the Arduino and the output from the acquisition board: the result is mechanically stable and sufficiently compact (see Figure 3). The analog output of the sensor board is converted into a 10-bits integer by the ADC embedded in the Arduino MCU.  
We wrote the Arduino sketch that samples the signal and sends it to the networking MCU using the Arduino Integrated Development Environment (IDE). To have the accurate timing which we need for filtering and analysis purposes, a timer-driven interrupt triggers sampling at 250 Hz [26]. The code snippet  
Figure 4: The remote ECG display on the doctor’s side.  
for the interrupt handler is in Listing 1: note that it is prepared to collect six analog data for a standard 12-lead ECG though we use only one in the prototype. The main loop waits for the buffer to be readable and encodes a data frame of 33 8-bit characters:  
<hh>:<mm>:<ss>.<ddd> <v1><v2><v3><v4><v5><v6>Q  
The first field is a 1 ms resolution timestamp, and the other six fields are integers in the interval \([0-999]\), each of which corresponding to one sample from an ECG channel encoded as a three-characters ASCII string. The Q character serves as frame separator. The redundancy of this format is functional to data integrity and simplifies the development. The encoding compresses (by clipping) the sampled 10-bits value into the [0-999] range. The timestamp is not as accurate as the sampling period: it is used only for rendering and to detect missing data.  
We introduce a two positions buffer to avoid interference between data fetch and encoding; a semaphore regulates access to the buffer.  
It is worth saying that Olimex provides a different driver [25], but its accuracy is not sufficient for the task. Thus, we implemented a different one.  
void timer2_Overflow_ISR() { int i; unsigned longt; digitalWrite(PROBEPIN1,HIGH); // probe if (full[b]) { Serial.println(\"fail\"); return; } for (int Channel=0;Channel<6;Channel++) { int x=analogRead(Channel); // clip in [0..999] x=(x<=12)?0:(x-12); Data[Channel][b] = (x>999)?999:x; } full[b]=true; b=b^1; // toggle 0<->1 buffer digitalWrite(PROBEPIN1,LOW); } ```
Listing 1: The function triggered every 4 ms on the Arduino MicroController Unit (mcu). Each element in Data[i] is a buffer for six ECG values. There are two such elements to implement a two positions buffer. full is a 0/1 switch to rotate the buffers, and to mark when the buffer is available. The probe pin level allows inspection of sketch operation. The complete sketch is available from the repository [24].
# An Analysis of MPTCP Congestion Control
## 4 Multi-Homing Solutions  
It is becoming more common for end hosts to be connected via multiple interfaces to the Internet. Similarly, ISPs offer multiple paths to allow routers to send traffic with the same destination across different paths. In data centres, a large number of servers often communicate with each other over redundant links [2].  
Despite this, the protocols widely deployed today were not originally designed for redundant, multi-path networks. The lack of multi-path aware protocols prevents the potential benefits that multi-homing can provide. Over time, different solutions at each layer have been proposed to fill the current gap with respect to the layers' use cases.
## 4 Privacy-Preserving Incentive Mechanism  
Before presenting the details of the proposed incentive mechanism, this section starts with an introduction of the main entities: a trusted authority (TA), CVs, and RSUs, and how they are involved in the proposed incentive mechanism.  
* Trusted Authority: The TA plays two essential roles in the proposed mechanism: the identity manager (\(g\)) is responsible for generating identities (public key and private key pairs) and relating vehicles' pseudonyms to their real identities, and the mediator (\(\mathcal{M}\)) unfreezes the frozen incentives to either refund the traffic operator when users behave dishonestly or transact the incentives to users when RSUs behave maliciously (are hacked). Therefore, TA is assumed to be fully secure and trusted. Note that though both \(g\) and \(\mathcal{M}\) function in a centralized manner, little computation or communication burden is introduced because vehicles only request identities once with \(g\) when they participate in the mechanism for the first time. \(\mathcal{M}\) only interacts with vehicles and RSUs under malicious behaviors, which are rare because the malicious party can be traced and penalized.
* CVs: Each CV is assumed to be able to communicate with RSUs and nearby vehicles using V2X technology. There are initiators/leaders \(\mathcal{L}^{R},\ \mathcal{L}^{V}\), which initiate the collaborative routing scheme and the route validation scheme, respectively, and corresponding followers \(\mathcal{G}^{R},\mathcal{G}^{V}\) The study does not consider attacks in the communication process, implying communication channels are assumed to be secure. Also, each vehicle is assumed to have a tamper-resistant device with which to store sensitive information, including a secret key and value of time securely (vehicle hardware side attacks are not considered).
* RSUs: RSUs also play multiple roles in the proposed mechanism. Some RSUs serve as checkpoint signers (\(S\)) in the route validation protocol, signing on multi-signature transactions when vehicles pass the validation at the checkpoints. And all RSUs are the nodes of the consortium blockchain, with some authorized RSUs (\(V\)) verifying the incentive transactions and position proofs and undertaking the consensus work to generate new blocks. The RSUs are semi-trusted and can be potentially malicious. However, we assume that only a small percentage of RSUs are malicious, which is widely accepted in other consortium blockchain applications [35; 36].  
Figure 4: Example of secret sharing-based MPC.
## 3 Data and Methodology
### Geological and Geomorphological Survey  
The field geological survey aims to gain insights into slope deformation and damage by examining the distribution, scale, and structural features of landslides. The first field geological survey was carried out in September 2022, and multiple slides have occurred in the study area. In March 2023, a follow-up geological survey was conducted as the trailing edge of the eastern pit slope on the southern side exhibited renewed and conspicuous deformation indications.  
This field survey involved employing a 5 m standard tape measure alongside a geological compass to record orientation and fracture data at specific stratigraphic points. Furthermore, data concerning landslide features such as tension cracks, slope seepage, and geological formations within the study area were meticulously documented. This comprehensive geological investigation work has facilitated a preliminary identification of the stratigraphic distribution in the study area, laying the groundwork for the subsequent construction of a three-dimensional geological model.
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions
### Results on Synthetic Uniform RS Hazy Images  
Figure 4 shows the dehazed results obtained on synthetic uniform RS hazy images. Figure 4a presents the hazy image, Figure 4b-n show the dehazed results using different methods, and Figure 4o displays the ground truth image. Among the evaluated methods, ZID produces the worst results with severe color shift (Figure 4j), while FADE and USIDNet exhibit obvious over-enhancement problems (Figure 4d,i). DehazeFlow, DDIP, and YOLY fail to remove haze adequately, as shown in Figure 4e,k,l, respectively. Although _DCP_ and RefineDNet can thoroughly remove haze, the results in Figure 4b,h display a slightly uneven dark color. SRKTDN, DWGAN, and ZIR show good haze removal ability, but the recovered images in Figure 4f,g,m exhibit slight color distortion with saturation loss. Using the ground truth image in Figure 4o as a reference, CEEF and our proposed method produce better results. Therefore, our method is superior to most other methods when processing RS images with uniform haze.  
To evaluate the dehazing performance quantitatively, we calculated the PSNR, SSIM, and LPIPS values using the dehazed image and the ground truth. Higher PSNR and SSIM values indicate better results, while lower LPIPS values signify better perceptual similarity. Table 2 presents the quantitative dehazing evaluation results for RS images with uniform haze. The dehazing methods are categorized, and the best result for each category and each metric is marked in bold. As shown in Table 2, for the traditional dehazing category, _DCP_ obtains the best score for SSIM and LPIPS metrics, while the CEEF obtains the best value for the PSNR metric. DehazeFlow obtains the best results for all three metrics in the learning-based category.  
Our proposed method exhibits outstanding performance in the zero-shot dehazing category, ranking first in both SSIM and the LPIPS metrics, and second in the PSNR. Notably, our approach outperforms all dehazing methods in both traditional and learning-based categories for all three evaluation metrics. Compared to the existing _DCP_ approach, our proposed method achieves an improvement of 2.04 dB and 0.11 for PSNR and SSIM, respectively. Moreover, our method surpasses RefineDNet, which has a similar networkarchitecture, by gaining an increase of 2.93 dB and 0.14 for PSNR and SSIM, respectively. These results confirm that our approach represents a significant improvement over the SOTA methods in uniform dehazing.
# Market-Oriented Procurement Planning Leading to a Higher Service Level and Cost Optimization
## 3 Design of the Conceptual and Simulation Models
### Procurement Planning
In the first case, the EOQ is calculated every week depending on the current demand pattern of the customers, i.e., an EOQ that changes depending on the expected demand for the next year. In the second case, based on the demand forecast for the next year, the EOQ is fixed for the next year, and the value does not change until the next planning year. With the EOQ calculated, the number of expected orders for the next year is derived; however, this number is typically not an integer, and therefore an adjustment is necessary. The weeks between orders are truncated to an integer, and then the EOQ is adjusted by maintaining the total expected demand for the next year. * Variable order quantity (depending on the target level, S): The inventory is filled up to a target level [47]. This is one of the options of the dynamic or feedback-dependent model. The model uses the reorder point with variable order quantity to respond dynamically. This method requires a high administration and IT effort to check the stocks after each transaction [43]. It is usually applicable for high-value goods with variable demand. The time frame between procurement orders varies. To define the target level (S), it should be a level defined by an invariant formula, but whose parameters vary over time. As a result, the target level varies over time, and so does the order quantity to reach it. This ordering method is able to change the target level to respond to customer demands, their volatility, and existing delivery backlogs. After this, the definition can determine the quantity ordered, \(Q_{RP}\), in the model, when the stock drops under the reorder \begin{table}
\begin{tabular}{c c c} \hline \hline
**Order QuantityOrder Cycle** & **Fix** & **Variable** \\
**(Order Rhythm Method)** & **(Reorder Point Method)** \\ \hline Fix & t, q—Policy & s, q—Policy \\ \hline Variable & t, S—Policy & s, S—Policy \\ \hline \hline \end{tabular} t = Fixed period between two order (order rhythm); q = order quantity; s = stock that triggers an order (reorder point).  
\end{table} 
Table 5: Overview of the possible ordering policies (own elaboration based on [48]). point (s). With this quantity ordered, a warehouse is planned to have sufficient products to meet customer demands for a period equal to two times the replenishment time. One of the simulation models is capable of shaping its strategy depending on customer demand. The model can do so by changing procurement policies. One of the criteria used for deciding these changes is the variation coefficient, which gives an indication of the variability of the demand. Due to this, the reorder point deals better with demand uncertainty. When the variation coefficient is higher, the demand uncertainty is also higher.
# Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation
###### Abstract  
Force sensing in robotic-assisted minimally invasive surgery (RMIS) is crucial for performing dedicated surgical procedures, such as bilateral teleoperation and palpation. Due to the bio-compatibility and sterilization requirements, a specially designed surgical tool/shaft is normally attached to the sensor while contacting the organ targets. Through this design, the measured force from the sensor usually contains uncertainties, such as noise, inertial force etc., and thus cannot reflect the actual interaction force with the tissue environment. Motivated to provide the authentic contact force between a robotic tool and soft tissue, we proposed a data-driven force compensation scheme without intricate modeling to reduce the effects of force measurement uncertainties. In this paper, a neural-network-based approach is utilized to automatically model the inertial force subject to noise during the robotic palpation procedure, then the exact contact force can be obtained through the force compensation method which cancels the noise and inertial force. Following this approach, the genuine interaction force during the palpation task can be achieved furthermore to improve the appraisal of the tumor surrounded by the soft tissue. Experiments are conducted with robotic-assisted palpation tasks on a silicone-based soft tissue phantom and the results verify the effectiveness of the suggested method.  
robotic-assisted palpation; tumor location; sensor uncertainties +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
The SP\({}_{3}\)-model-based image reconstruction was performed on a set of 219 human PIP joints, with 99 joints belonging to subjects with RA and 120 joints belonging to healthy subjects. The \(k\)-fold cross validation was employed to evaluate the diagnostic performance of SP\({}_{3}\)-based DOT images of absorption and scattering coefficients in the fingers. A comparison of sensitivity and specificity values was made between SP\({}_{3}\)-based images, DE-based images, and ERT-based images. The sensitivity and specificity values were 88 and 93% with the SP\({}_{3}\) based images, 91 and 98% with the ERT based images, and 67 and 81% with the DE based images, respectively. It was also shown that the SP\({}_{3}\) model performs better in achieving the fewest optimal features than the DE and ERT models. In terms of computational efficiency, the SP\({}_{3}\) model is approximately 100 times faster and takes 30-fold less memory than the ERT model. In conclusion, the results presented here demonstrate that the SP\({}_{3}\) model provides sufficiently accurate DOT images with a sensitivity of 88% and specificity of 93% to achieve clinically significant diagnostic results that compare favorably to the ERT model, while leading to a significant reduction in computation time and system resources. Therefore, it is expected that the SP\({}_{3}\)-based DOT reconstruction can translate into direct clinical benefits, allowing researchers to image finger joints in near real-time and to evaluate DOT images for RA diagnosis at its early stage and treatment monitoring at a clinical setting.  
Future work involves the application of the algorithms and classifiers presented in this work to therapeutic areas of monitoring drug responses in a longitudinal clinical study, refinement of fragmented algorithms into a clinically useful all-in-one package, and further enhancement of the SP\({}_{3}\) reconstruction algorithm through the use of parallel computing neural network techniques.  
Conceptualization, methodology, software, data analysis, and visualization, L.M. and S.H.K.; writing--original draft preparation, S.H.K. and L.M.; writing--review and editing, S.H.K. and A.H.; resources, supervision, project administration, and funding acquisition, A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, Grant number 5R01 AR050026, and the National Heart, Blood, and Lung Institute, Grant number 1R01-HL115336. Furthermore, support was provided partially by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, Grant Number 5 T32 AR059038 02.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors would like to thank [PERSON] for his insightful comments and suggestions on feature extraction, data mining and classification.  
The authors declare no conflict of interest.
## 4 The Proposed Authentication Mechanism  
This section presents the proposed scheme, which involves two scenarios to secure the communication between the SBs and the CC. In the first scenario, an SB will be SE, and the CC will be RE. While in the second scenario, the CC will be SE and the SBs will be REs. The notations used in this study (first scenario) are given in Table 1. While, the notations of the second scenario are similar to the first one.  
The suggested scheme offers SGMA, authorization and information privacy between the SB and the CC. The proposed scheme is involved in the following phases.  
* _Initialization Phase_: the SBs in this phase are recorded in the CC with unique identity numbers.
* _Authentication Phase_: from the SB to the CC to authenticate each other.
* _Key Session Phase_: is the mutual key creation, which is utilized as a session key and forwards data from the SB and the CC.