esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Computation from a Saved State
```
0:\(h\): threshold for requests for the creation of new time steps
0:\(\langle s_{j},\ldots,s_{k}\rangle\): a previously computed trajectory
0:\(O_{r}\): the set of all previously received observations
0:\(O_{n}\): a set of newly acquired observations
0:\(s_{f},\ldots,s_{t}\): updated trajectory
1:\(n\gets min\{t\,|\,obs(f,v,t)\in O_{n}\}\)
2:\(\triangleright\) Check if some of the newly acquired observations predate step \(j\)
3:if\(n<j\)then
4:\(\triangleright\) If so, we recompute the trajectory from scratch
5:\(p=\{time(0).\}\cup\{time(t).\,|\,t\in I\,\,or\,\,obs(f,v,t)\in O_{r}\cup O_{n}\}\)
6:\(A\leftarrow\text{Incremental}^{h}(p,O_{r}\cup O_{n},h)\)
7:\(\tau=\text{extract-trajectory}(A)\)
8:else
9:\(I=\{j,\ldots,k\}\)
10:\(p=\{time(t).\,|\,t\in\,\text{or}\,\,obs(f,v,t)\in O_{n}\}\)
11:\(\triangleright\) Find the saved state right before the first observation
12:\(m=max\{i\,|\,i\in I\,\,\text{and}\,\,i\leq n\}\)
13:\(A\leftarrow\text{Incremental}^{c}(p,O_{n},h,s_{m})\)
14:\(\langle s_{M},\ldots,s_{t}\rangle=\text{extract-trajectory}(A)\)
15:\(m^{\prime}=max\{i\,|\,i\in I\,\,\text{and}\,\,i<m\}\)
16:\(\tau=\langle s_{j},\ldots,s_{m^{\prime}},s_{m},\ldots,s_{l}\rangle\)
17:endif
18:return\(\tau\)
```  
**Algorithm 5** Algorithm _UpdateCache_  
The algorithm relies on the assumption that the user maintains a record of the latest saved trajectory as well as of all previously received observations. When new observations become available, the user invokes _UpdateCache_. Then, _UpdateCache_ determines the smallest time step that is affected by the new observations and incrementally updates the remaining part of the trajectory, returning the result of the computation. Intuitively, the trajectory will then be saved by the user for later reuse, when new observations are received. It is not difficult to see that the algorithm can seamlessly handle cases in which earlier parts of the trajectory are missing, e.g., if they have been dropped to save memory. If new observations correspond to states that pre-date the first state of the trajectory, _UpdateCache_ falls back to recomputing the trajectory from scratch using the record of all observations. Due to these features, _UpdateCache_ is suitable for use on cloud platforms where nodes may be terminated and restarted, as well as in cases in which memory constraints prevent one from saving a complete trajectory. Under those conditions, _UpdateCache_ is still capable of leveraging clingo's incremental computation capabilities and of avoiding recomputations whenever possible.
## 3 Results and Analysis
### SEM Analysis  
In this study, SEM image analysis was performed to observe the fiber morphology and dispersion in ECM specimens mixed with CF and SF under repetitive electrical heating. Figure 12 displays SEM images of the ECM specimens mixed with CF. As shown in Figure 12a, CF within the cement matrix forms an electrical pathway that acts as a bridging among the pores. It is believed that an electrically conductive network formed in this way serves as a passage through which electrons flow, thereby improving heat generation performance. From Figure 12b, however, we can see the fiber balling of CF which has the property of hydrophobicity in the formed ECM. When mixing the mortar, such fiber balls occur frequently due to the high aspect ratio of CF, and CF that is not evenly distributed due to fiber balling happens to act as some voids. This phenomenon often occurs as the CF content increases, thus negatively affecting the improvement in flexural strength. On the other hand, Figure 12c shows SEM images of the ECM specimens mixed with SF. In Figure 12c, however, even when mixed up to 1.25%, the heating performance was not improved because an electrically conductive network was not formed within the ECM-SF125 specimen. As a result, it is judged that the incorporation of SF has little effect on the heating performance of the ECM.
## 2 Materials and Methods
### Remote Sensing Products  
Vegetation indices play an important role in monitoring agricultural crops and vegetated surfaces using multispectral satellite imagery or other technologies. Several sensors can be used to obtain vegetation indices, but the most widely used are the Normalized Difference Vegetation Index (NDVI), which is used to monitor surface growth and vegetative vigor [59], and the Enhanced Vegetation Index (EVI), which uses the red and infrared bands like NDVI, but uses the blue band to remove atmospheric influences [59].  
To access the seasonality of phenology on site, the x NDVI was used, which presents the direct relationship with leaf chlorophyll content, i.e., the higher the index, the more new leaves (leaf flush) in the canopy. The time series of surface reflectance from the MODIS sensor (on board the Terra and Aqua satellites) with atmospheric correction from the MAIAC algorithm (multi-angle implementation of atmospheric correction) have \(1\) km of spatial resolution and are temporally aggregated in 16-day compositions. These images are used to calculate the Normalized Vegetation Index (NDVI) described in Equation (6):  
\[NDVI=\rho NIR-\rho Red/\rho NIR+\rho Red \tag{6}\]  
where NIR is near-infrared (841-876 nm) and Red is red band (620-670 nm). These wavelength values refer to the MODIS product. For the estimation of the leaf area index (LAI), the product MCD15A3H (v006)--Terra + Aqua LAI was used, which consists of the composition of 4 days in the selection of best data values in the selected period by two satellites, according to [PERSON] et al. [60]. However, the EVI decouples soil and atmosphere influences from the vegetation response by correcting the \(\rho\)NIR/pred ratio using visible blue reflectance (\(\rho\)blue) and correction coefficients [61]:  
\[EVI=2.5\left(\frac{\rho NIR-pred}{\rho NIR+6 pred-7.5\rho blue+1}\right) \tag{7}\]
## 2 Related Work
### Discussion  
The proposed work aims to reduce the complications of diabetic patients through early detection and real-time monitoring, precisely when a patient moves, which may risk their and other drivers' and pedestrians' lives. Our proposed work includes the selection of the right attributes for early and accurate prediction of diabetes. We proposed a novel approach that integrates human genetics information with advanced CGM sensors combined with machine learning approaches and VANET technology. Table 1 presents the recent studies that compare machine learning algorithms and identify the best-performing algorithm for diabetes prediction.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Publication** & **Compared Algorithms** & **Parameters** & **Best Accuracy** \\ \hline [PERSON] et al. [3] & RF, J48, Neural Networks & Accuracy, Sensitivity, Specificity, MCC & Random Forest Accuracy = 80.84\% \\ \hline [PERSON], et al. [13] & Naïve Bayes, J48, SMO, ZeroR, OneR, Simple Logistic, Random Forest & Accuracy, Precision, Sensitivity, Specificity, Recall, F-measure & SMO Accuracy = 99.66\% \\ \hline [PERSON], et al. [19] & Random Forest, Naive Bayes, SVM, Logistic Regression, Multilayer Perceptron & Precision, Recall, Accuracy & Multilayer Perceptron Accuracy = 77.08\% \\ \hline [PERSON] et al. [20] & Logistic Regression, Gradient Boosting Machine, Random Forest, RPART & AROC, Sensitivity & Logistic Regression Sensitivity = 73.4\% \\ \hline [PERSON], et al. [21] & Decision Tree, Naïve Bayes, Support Vector Machine, Random Forest, KNN & Accuracy, Sensitivity, Specificity & Naïve Bayes Accuracy = 82.30\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different machine learning algorithms for diabetes prediction.  
All the above-proposed approaches perform well and give fruitful information regarding diabetes prediction, analysis, awareness, and monitoring. However, some are using clinical or manual data, and some are testing their results on a specific group of people. At the same time, our proposed approach is novel and generic, which can predict the future diabetic level based on current genetic data and provide an opportunity for future researchers. Additionally, our proposed method outperforms by achieving results up to 99.5% using all evaluation parameters and 10-fold cross-validation.
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 3 Approach
### Core Components
#### 3.3.5 Counting  
As the name indicates this process is responsible for counting all objects passing by. Due to the fact that the proposed system should not only deliver proper results for a specific road scene, the counting mechanism should also be designed as abstract as possible. The counting process is the last part of the system loop and gets all necessary data from the preceding tracking framework. A list of all tracked objects enriched with additional information such as object ID, current position, object type and a list of frames is provided by the tracing process. The mentioned list of frames can be seen as a history of where and when the same object was detected and associated with the same identifier.  
Counting is only performed with data received from the tracking process. If an object was tracked successfully several times it is considered to be positioned on a valid trace. Due to the nature of recording from static cameras objects enter and leave the scene regularly. At some point in time they cannot get tracked anymore because they get out of sight. This also means that the history of frames will not change anymore and that is the point where the counter can increase the number of encountered vehicles.  
To not take any tracking errors into account the counting system filters the tracing history for valid objects. Such errors can occur due to partial or complete occlusion, e.g. on high traffic scenes, or when the detector itself delivers wrong detections. As a result, objects which are tracked only a small number of times are considered to be incorrect, hence will not be used by the counting framework at all.  
The counting system delivers the total count of objects passing by. Furthermore, the counting process has access to additional information such as the object class. For this reason the system can also count not only the total number of objects, but also the number of objects per object class. See Figure 9(b) for a visual demonstration of the counting process.  
Figure 9: Tracking vehicles with predictions at low FPS. Each of the four objects gets detected in the first frame (**a**). The tracking algorithm tries to match objects based on a minimum distance to the predictions (dashed boxes) in the next frame (**b**). An association can be performed without the need for analysing the visual appearance of objects which can change greatly on low FPS. Source: Youtube.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 2 Structure of CFRP and Its Electrical Conductivity
### Traditional Methods  
Metal alloys have high thermal and electrical conductivities, so the lightning strike damage of metal aircraft structures is limited. Metallic strips arranged on the top of CFRPs can avoid the lightning strike damage. Copper or aluminum alloys with excellent conductivity can protect CFRPs against the lightning strike on horizontal and vertical composite stabilizers.  
Traditionally, lightning protection of composite aircrafts is based on methods such as incorporating a conductive metallic mesh [77; 91]. As a continuously conductive outer layer, the mesh can dissipate indirect or direct electromagnetic interference effects. The similar problems exist in CFRPs of other components, such as wind turbines and masts. More electrically conductive CFRP structures should be adopted in order to reduce or avoid lightning strike protection materials in aircrafts. Although the efficiency of this method has been verified, the metallic mesh also increases the weight and offsets the advantages provided by the high strength and stiffness of CFRP [92].
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Sink Centric  
In these protocols, the sink manages the sensing levels and the routing decisions according to the collected information and sent by sensor nodes in the sensing area. Among the representative protocols of this class, we find:  
* **Real-Time and Reliable Transport (RT)2 Protocol** (RT) 2 is designed as a reliable and timely transport protocol using collaborative transportation of events [27]. It considers nodes as sensor nodes and actor nodes: When sensor nodes detect a triggered event, sensed data would be sent to the base station (BS) through active nodes. In (RT) 2, congestion is detected and controlled by actor nodes. In [28], the authors show that the (RT)2 protocol can adapt easily to the heterogeneous nature of WSNs by dint of its configuration and provides real-time communication and various reliability requirements, which helps in reducing energy consumption, but it consumes large amounts of the bandwidth.
* **Loss Tolerant Reliable Event Sensing (LTRES) Protocol** Mainly designed for the dynamic event detection in WSNs, LTRES determines the event-sensing fidelity level (ESFE) according to which the end-nodes adjust their source rates [29]. It performs network traffic control, which is based on distributed source rate adaptation and ensures reliable event detection, but the use of the source rate adaptation mechanism makes it very greedy in energy.
* **Reliable Robust and Real-Time (RRRT) Protocol** As a reliable and timely event detection transport protocol, RRRT uses a combined congestion control mechanism to achieve reliability and conserve energy [30]. Researchers in [18] have shown that RRRT protocol improves energy conserving and ensures reliable event detection due to the reporting frequency adjustments, but it presents high levels of extra overhead because of the congestion detection and control techniques.
* **Simultaneous Multiple Event-to-Sink Reliable Transport (SMESRT) Protocol** It ensures multiple event detection in WSNs with the improved rate of energy consumption by dint of the combined payload control component as this protocol combines all payloads at an elected CH and sent only one packet to the sink, which provides less traffic. Despite having high levels of extra overhead, SMESRT has the advantage of assigning different reporting frequency for different events [31].
* **Congestion and Delay Aware Routing (CODAR) Protocol**  
Figure 2: Event-driven routing model.  
CODAR is designed to reduce the latency and to provide reliable routing of the critical data through different techniques of mitigation, congestion avoidance and End-to-end delivery data management. It divides the sensors into the critical nodes (nodes closer to the event) and regular nodes (nodes away from an event). Experiences in [30] show that this protocol avoids congested nodes to reduce congestion in the network has the advantage of delivering a high amount of the critical data within specified delays, but it remains unsuitable for large networks [32].
# Terahertz Wideband Filter Based on Sub-Wavelength Binary Simple Periodic Structure  
[PERSON]  
1 Institute of Micro-Nano Optoelectronic Technology of Shenzhen University, Shenzhen 518060, China; [EMAIL_ADDRESS] (P.Y.); [EMAIL_ADDRESS] (H.H.X.); leile  
4.07 THz. The filter's highest transmittance was nearly 37%. It is currently the filter with the widest 3-dB bandwidth and the highest center frequency.  
The methods and structures reported above are relatively complicated, such as T-type and Y-type metal multilayer film structures, and fabrication and processing are difficult. The selected materials (e.g., gold, silver, and lithium niobate) are costly. Additionally, the filter has low transmission efficiency--nearly 37% in a terahertz band above 3 THz. Therefore, it is important to realize a broadband transmission filter with high transmission efficiency, low cost, and a simple structure.  
Based on the research on sub-wavelength optical design and application accumulated by our research group [10; 11; 12; 13; 14; 15; 16; 17; 18; 19], this paper proposes a method based on a simple periodic binary sub-wavelength structure [12; 13] to design a terahertz broadband filter. A silicon-based terahertz wideband filter with a center frequency of 4.07 THz was designed. The theoretical 3-dB bandwidth is 2.33 THz, the highest transmission efficiency is nearly 50%, and the bandwidth is 0.13 THz wider than the best existing DCF. The maximum transmission efficiency is increased by more than ten percent. The device was further fabricated and tested, and the experimental results were consistent with the design results, which proved the feasibility of the design method. In this paper, high-performance broadband filtering was realized with a simple structure, which breaks through the limitations of current design and is convenient for mass production. It has important application value in terahertz communications and imaging.
## 6 Experiments
### Datasets  
The datasets used to validate our proposed architecture originate from the SAG-5 mill at Minera La Escondida (BHP Billiton), covering operational records from August 2018 to July 2019. This dataset comprises 696,356 instances across 40+ operational features, of which ten were identified as critical for overload prediction: returned pebble count, fine granulometry, medium granulometry, coarse granulometry, mill power, mill pressure, solid percentage, throughput, and mill speed. Metallurgical experts labeled 10,197 instances (1.46% of the dataset) as overloads, reflecting the imbalance of the classification problem. Sensor-based data occasionally included out-of-range values; these were filtered based on expert judgment, resulting in a refined dataset of 667,762 records (5% data loss). The dataset required temporal validation to predict overloads 10 min ahead using 15-min historical windows. Data recorded at 30-second intervals were filtered to ensure coherence, yielding 626,565 Gram matrices for model training (10% data loss).
## 3 Results
### Effects of Upper Limb Robot-Assisted Therapy  
The included systematic review [16] highlighted that robotic training is the most studied rehabilitation intervention in upper limb rehabilitation in PwMS. In general, the included studies (RCTs and non--RCTs) have shown that robotic training can improve the motor coordination, manual dexterity, and functional capacity of the upper limbs. At the body function level, maximum shoulder anteflexion (+27 degrees) and handgrip strength (+12%) were increased in a prospective cohort study [37]. In contrast, a case series study reported an improvement in proximal arm impairment (+15%) [31]. The tremor was decreased after an error-enhancing adaptive robotic protocol [35]. Several studies reported improving robot-driven measures [29; 32; 36; 37] or EMG activity [30], but this was only the case in the robotic group. At the activity level, manual dexterity, which was measured by 9 HPT, improved by 12-47 s in two case series studies [34; 36] and only in the robotic group (+15%) in an RCT [29]. Arm function, which was measured by WMFT, increased by 25-33% in a prospective cohort study [37]; however, the ARAT Grasp sub-score significantly improved in the robotic protocol that included real objects manipulation (+77%) compared to the other condition (+29%) [33]. When measured by TEMPA tasks, arm capacity improved (\(-\)23 s) in a case series study [34]; however, better arm use in everyday life was only reported in the robotic group in two RCTs [30; 32; 30]. Only four studies [29; 30; 34; 37] had a follow-up evaluation, with retention of arm function being shown to be maintained at one [30] or two months [34] after robotic therapy.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 3 Proposed Scheme
### Image Restoration  
When the receiver has the private key \(sk\), a self-embedded key \(k_{1}\) and the hidden key \(k_{2}\), additional data can be extracted after decryption and the original image can be restored. The directly decrypted image is obtained by decrypting with the private key \(sk\), and the directly decrypted image is divided into three parts of \(A_{D},B_{D},C_{D}\) according to the method of room reserving. Since the additional data are hidden in \(P_{t}\) of \(A_{D}\), the hidden key \(k_{2}\) can be used for scrambling recovery after the additional data are extracted. The receiver extracts self-embedded data from \(B_{D}\) and \(C_{D}\) to restore the original image.  
\(L\_map\) is extracted from the LSB of \(B_{D}\), and the self-embedded key \(k_{1}\) is used to scrambling recovery. Finally, according to the extraction step of the PEE, all the self-embedded data can be extracted from \(C_{D}\) and the data extracted from \(C_{D}\) is used to complete the recovery of the \(A_{D}\). The original image of reversible recovery has thus been obtained.
# Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys
###### Abstract  
The avoidance of scrap and the adherence to tolerances is an important goal in manufacturing. This requires a good engineering understanding of the underlying process. To achieve this, real physical experiments can be conducted. However, they are expensive in time and resources, and can slow down production. A promising way to overcome these drawbacks is process exploration through simulation, where the finite element method (FEM) is a well-established and robust simulation method. While FEM simulation can provide high-resolution results, it requires extensive computing resources to do so. In addition, the simulation design often depends on unknown process properties. To circumvent these drawbacks, we present a Gaussian Process surrogate model approach that accounts for real physical manufacturing process uncertainties and acts as a substitute for expensive FEM simulation, resulting in a fast and robust method that adequately depicts reality. We demonstrate that active learning can be easily applied with our surrogate model to improve computational resources. On top of that, we present a novel optimization method that treats aleatoric and epistemic uncertainties separately, allowing for greater flexibility in solving inverse problems. We evaluate our model using a typical manufacturing use case, the preforming of an Inconel 625 superalloy billet on a forging press.  
GP regression; FEM; surrogate modeling; multi-objective optimization; hot metal forming; Inconel 625