esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
A prosthetic hand must exhibit basic anthropomorphic behavior, as it needs to interact with objects designed, created, and used by the human hand [2]. The design of prosthetic hands involves inherent contradictions, demanding an optimal balance to meet diverse requirements. Table 9 below provides a comparison of some of the prosthetic hands available in the literature. Two main conclusions can be drawn from the above table despite its non-exhaustive nature--(i) prosthetic hands having 2-DoF MCP joints with active Ab/Ad motion involve complexity in mechanism or actuators or both, (ii) the majority of prosthetic hands available in the literature have parallel finger arrangement with reduced dexterity. This work addresses these challenges by presenting a novel design paradigm for underactuated prosthetic hands. This methodology leads to novel bio-inspired finger configurations for the index, middle, ring, and little fingers. The configuration consists of two key elements: finger orientation and finger placement. The novel finger orientation reduces the number of controlled joints by eliminating the need for an active MCP Ab/Ad joint, simplifying the overall hand design. This innovative approach allows the hand to grasp objects of various sizes and shapes without sacrificing dexterity. Additionally, finger inclination can  
Figure 13: (**a,b**) Complete setup of the 3D printed prototype with finger in its extended position. (**c–f**) Grasping various objects using the hand prototype. be customized for specific tasks or postures, increasing versatility. Further, a new finger placement is introduced to improve dexterity in the three DoF fingers in the current work. The positioning of the finger bases is undertaken to match anthropomorphic behavior. Grasp simulation results show that the current configuration outperforms conventional parallel finger design. A preliminary prosthetic hand prototype was designed, 3D-printed, and validated using a tendon-driven mechanism and a novel whippletree-based differential mechanism-based motion transmission system. The novel differential mechanism's lever lengths were calculated and optimized using experimental tendon excursions and anthropometric data. The authors emphasize that alternative inclination angles, derived from various performance indices, can enhance grasping capabilities beyond those of traditional prosthetic hands with parallel fingers. Rather than determining the optimal inclination angles, this paper demonstrates that the proposed methodology significantly improves grasping performance. However, it is also possible for some of the derived orientations to be non-ergonomic, leading to intersecting finger trajectories. Even in the current work, based on the PIs selected, we have obtained inclinations of 13\({}^{\circ}\) and 10\({}^{\circ}\) for the middle and ring finger, respectively. This orientation may lead to the two fingers intersecting while grasping small
## 2 Metamaterial Structure Design and Simulation Geometry  
The proposed metamaterial unit cell is represented on an inexpensive FR-4 dielectric material (PCBWay, Xiacheng, China) with a thickness of 1.6 mm, a physical size of 11 \(\times\) 11 mm\({}^{2}\), and electrical dimension of 0.074\(\lambda\)\(\times\) 0.074\(\lambda\). The FR-4 substrate has a dielectric constant of 4.4 with a dissipation factor of 0.02. The resonator of the unit cell is made of annealed copper with a thickness of 0.035 mm and a conductivity of 5.96 \(\times\) 10\({}^{7}\) S/m. The metamaterial resonator consists of a split ring resonator and modified Coptic cross shaped resonator, as indicated in Figure 1. The top view of the unit cell represents a resonating patch of the metamaterial, as shown in Figure 1a; on the other hand, the isometric view shows the annealed copper resonating patch with FR-4 substrate, as presented in Figure 1b. The length and width of the SRR are 10.4 mm and 1 mm, respectively. The split gap of the SRR is 0.2 mm, which is fixed by the trial and error method. The split gap of the modified Coptic cross is 0.4 mm. The design specification of the metamaterial is presented in Table 1. The time domain solver with hexahedral meshing system is used to derive the scattering parameters. The simulation scenario is displayed in Figure 2, where a transverse electromagnetic wave (TEM) is usually propagated through the proposed unit cell. As shown in Figure 2, PEC-PMC boundary geometry is used for simulation. The metamaterial unit cell and arrays of unit cells are placed between the two waveguide ports (one is the transmitting end, the other is the receiving end).  
Figure 1: Different views of the unit cell: (**a**) Top view, (**b**) Isometric view.
## 4 Materials and Methods
### Confusion Matrix and Performance Measurement  
Confusion matrix is a matrix used to evaluate the performance of classifier models in general. For binary classification problems, confusion matrix size 2 \(\times\) 2 is used. However, in real life problems, classification can involve more than two classes or categories. These problems are considered as multi-class classification problems. For a multi-class classification problem, confusion matrix size 3 \(\times\) 3 is used. Principally, 3 \(\times\) 3 confusion matrix is similar to 2 \(\times\) 2 confusion matrix. The general form of 3 \(\times\) 3 confusion matrix is as follows:  
\[\begin{array}{cccc}A&B&C&B\\ &&C\\ \end{array}\left[\begin{array}{cccc}aa&ba&ca\\ ab&bb&cb\\ ac&bc&cc\\ \end{array}\right]\]  
where the columns denote actual cases, the rows denote predicted cases.  
For category A,  
True Positive (TP): _aa_  
True Negative (TN): \(bb+cb+bc+cc\)  
False Positive (FP): \(ba+ca\)  
False Negative (FN): \(ab+ac\)  
Calculations for other categories are similar.  
In this research, our problem was a multi-class classification problem, because the severity level consisted of three distinct categories, and we aimed to classify each dengue patient into one of those level. So, we used 3 \(\times\) 3 confusion matrix and these performance measurements below:  
1. _Accuracy_: This metric measures the overall performance of the model. Generally, accuracy is the proportion of true results among the total number of cases examined. _Accuracy_ can be expressed as follows: \[Accuracy=\frac{number\ of\ correctly\ classified\ examples}{total\ number\ of\ cases}\] (2)  
_Accuracy_ can also be calculated from the elements of the 3 \(\times\) 3 confusion matrix using the formula below:  
\[Accuracy=\frac{\sum_{i=1}^{r}x_{ii}}{\sum_{i=1}^{r}\sum_{j=1}^{r}x_{ij}} \tag{3}\]  
where \(x\) denotes an element of the confusion matrix, \(i\) denotes the number of rows of the confusion matrix, and \(j\) denotes the number of columns of the confusion matrix. Note that the numerator of the Formula (3) is the sum of the main diagonal elements of the confusion matrix, and the denominator is the sum of all of the elements of the confusion matrix.
2. _Sensitivity_: this metric measures the proportion of the True Positive (_TP_) cases among the total number of positive cases. The formula to calculate _sensitivity_ is as follows: \[Sensitivity=\frac{TP}{TP+FN}\]
3. _Specificity_: This metric measures the proportion of the True Negative (_TN_) cases among the total number of negative cases. The formula to calculate _sensitivity_ is as follows: \[Specificity=\frac{TN}{TN+FP}\]
# Ultra-High-Performance Concrete (UHPC) Piles and Splicing Options
## 6 Conclusions  
This study aimed to explore options for connecting segments of Ultra-High-Performance Concrete (UHPC) piles in planned and unplanned scenarios. After reviewing the driven piles, this paper thoroughly investigated splicing systems for UHPC piles, assessing prior research and field applications. The applicable splices were then introduced and evaluated based on criteria like capacity, durability, affordability, and ease of application. The following main conclusions can be drawn:  
* Pile-splicing is necessary to join two segments of piles during driving; the advantages relate to reduction in installation and transportation time and preventing damage to precast concrete during construction.
* Welded, bolted, post-tensioning, epoxy-bonded dowel, FRP sheet/jacket, NSM FRP, and mechanical coupler were identified to have the potential for use in splicing UHPC piles.
* All identified pile-splicing options can be used in preplanned situations. However, epoxy-bonded dowel, FRP sheet/jacket, and NSM FRP can also be utilized in the case of unplanned situations on the field.
* FRP sheet/jacket and NSM FRP pile splice options have a good corrosion resistance that is especially in demand in corrosive environments.
* Mechanical coupler is very easy to install and is quick. Welded, bolted, epoxy-bonded dowel, FRP sheet/jacket, and NSM FRP are other options that require moderate time for installation.
* Welded, epoxy-bonded dowel, FRP sheet/jacket, NSM FRP, and mechanical coupler have a low cost of production.
* FRP sheet/jacket method takes less time to prepare compared to other options. Welded, bolted, epoxy-bonded dowel, FRP sheet/jacket, and NSM FRP are other options that take a moderate amount of time to prepare.
# Rectification for Stitched Images with Deformable Meshes and Residual Networks
## 3 Materials and Methods
### Loss Function  
The loss function of the proposed RIS-DMRN consists of two components: the local loss function and the global loss function. The calculation is formulated as follows in Equation (1):  
\[l_{total}=\omega_{local}l_{local}+\omega_{global}l_{global} \tag{1}\]  
where \(\omega_{local}\) and \(\omega_{global}\) represent the weights assigned to the local loss and global loss, respectively. The local loss \(l_{local}\) and global loss \(l_{global}\) contribute to the overall loss, and the weights control the balance between preserving local details and maintaining global context during the rectification process.
# Research on Performance of a Dense Graded Ultra-Thin Wearing Course Mixture
## 2 Experimental  
### Mix Design for Different Asphalt Mixtures  
Styrene-butadiene-styrene (SBS) modified asphalt with penetration grade of PG-70, basalt aggregate and limestone powders were used for all of the asphalt mixtures prepared in this study. According to the recommendation by Chinese specification and previous studies, the polyester fibre was used for SMA mixtures. To improve the rutting resistance and the anti-wearing performance of ultra-thin wearing course mixture (UWM), a multi-chain polyolefin modifier was added to the SBS modified asphalt to prepare modified ultrathin wearing course mixtures (M-UWM).  
Five different asphalt mixtures were studied including an SMA mixture with nominal maximum aggregate size of 13.2 mm (SMA13), dense-graded asphalt mixture with nominal maximum aggregate size of 13.2 mm (AC13), SMA mixture with nominal maximum aggregate size of 9.5 mm (SMA10), ultrathin wearing course mixture with nominal maximum aggregate size of 9.5 mm (UWM10), and a modified ultrathin wearing course mixture with a nominal maximum aggregate size of 9.5 mm (M-UWM10). The SMA13 and AC13 are commonly used in the pavement surface layer while SMA10, UWM10 and M-UWM10 are mainly used in the wearing course above the pavement surface layer [18]. While the SMA10 is a traditional wearing course mixture, the UWM10 and M-UWM10 are newly developed wearing course mixtures. Based on Marshall mix design, the gradations for different asphalt mixtures are shown in Figure 1 and the Marshall design parameters for different asphalt mixtures are listed in Table 1.  
Figure 1: \(Cont.\)  
Figure 1: The designed gradation for different asphalt mixtures: (**a**) SMA13; (**b**) AC13; (**c**) SMA10; (**d**) UWM10 and M-UWM10.  
Based on the designed asphalt mixtures, two test roads were paved. One was paved with AC13 used in the surface layer and M-UWM10 used in the wearing course above the AC13 layer. The other was paved with SMA13 used in the surface layer and SMA10 used in the wearing course above the SMA13 layer.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 5 The Influence of Constellation on Space Environment
### PMD Rate Influence  
Although the Starlink satellite adds the post-mission active derailment link in the design phase, if the satellite collides or is damaged during the mission cycle, the active derailment device can not be effectively triggered, resulting in a longer period of satellite re-entry into the atmosphere and increasing the impact on the space environment, so it is necessary to analyze the post-mission disposal (PMD) success rate.  
Figure 16: The influence of constellations on the evolution of objects more than 10 cm.  
Figure 15: Breakup fragments’ orbit distribution.  
Figure 17 compares the object growth trend under the four PMD success rates of 50%, 75%, 90%, 100%. The increase in the number of objects corresponding to the higher PMD rate at the initial stage of deployment is more moderate. After the completion of deployment, the growth trend of objects corresponding to the four PMD rates is similar, and the growth rate of objects with 90% and 100% PMD success rates slows down in the later stage. The lower the PMD rate, the higher the number of satellites left in orbit, the greater the probability of collision with the passage of time, and the interaction between the accumulation of satellites and space objects will lead to the continuous deterioration of the space environment.  
Figure 18 shows the ratio of the number of objects corresponding to different PMD rates to the number of objects without constellations. The higher PMD rate can reduce the growth rate of space objects, but due to the influence of constellations, the growth rate of objects continues to rise. By 2040, even with the PMD success rate up to 100%, the growth rate of objects will still be about 1.07. Therefore, the introduction of large satellite constellations is bound to have an impact on the space environment and the growth of space objects, and the risk of this impact can be reduced to a certain extent by improving the success rate of PMD.  
Figure 17: The influence of different PMD success rates on the evolution of objects.  
Figure 18: The ratio of different PMD success rates and objects without joining constellations.
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 2 Related Works
### Combination Models Based on Time Series  
Due to the strong correlation between the deformation series of multiple observation points, it is not enough to concentrate only on the time dimension of a single observation point when predicting dam deformation. As spatio-temporal data mining has advanced, an increasing number of academics have proposed spatio-temporal data prediction models [19; 20], but the application of these models to predict dam deformation is still limited. Dam displacement data can be successfully fitted and forecasted in time and space due to the use of time autoregressive models by [PERSON] et al. [21]. Nevertheless, the model is only suitable in the condition of a uniform distribution of monitoring points. [PERSON] et al. [22] proposed a spatio-temporal autoregressive model based on the Kriging method with spatial weighting, which combined the dam deformation data, effectively describing the variation of the actual space and enhancing prediction accuracy. All of the aforementioned models can enhance the model's predictive power to some extent, but the effect on the complete trend needs to be improved. The above models have the problem of not being able to capture the dynamic correlation between different measuring points at any time in the deformation sequence, as well as the dynamic correlation dependence of different measuring points at different times.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 1 Introduction  
In recent years, Internet of Things is experiencing a greater push to provide green solutions for a sustainable environment. Green Internet of Things is more relevant in remote outdoor areas without easy access to electricity. Further, a large scale IoT deployment for practical applications [1, 2] involving hundreds of nodes can drastically reduce the energy consumption by adopting Green IoT.  
For remote monitoring and data collections, the IoT nodes mostly connect through a resource-rich gateway in the field to the data center. However, setting up a Local Area Network through a gateway is quite challenging in a hilly site [3, 4] for reasons, such as steep terrain, higher vegetation density, channel conditions, inadequate Fresnel Zone clearance [5], and coverage issues. Hence, the Local Area Network protocols, such as RPL [6] (Routing Protocol for Low-Power and Lossy Networks), that is based on IEEE 802.15.4 are not suitable to use in our environment. Alternatively, the IoT nodes in large sites can directly connect to cellular networks. Due to its pervasive and superior connectivity compared to other wireless solutions, several recent works [7, 8] have advocated the use of cellular networks to connect IoT devices in a large-scale deployment. This new paradigm of connecting IoT devices is referred to as C-IoT.  
This paper deals with such a C-IoT deployment in hilly terrains for a multi-site landslide early warning system. The deployed nodes are directly connected to the Internet, which makes them extremely susceptible to various Denial of Service (DoS) attacks frompublic networks during real-time collaborative communications. Our first landslide deployment site, sprawling around 7 acres, uses an IoT gateway and a LoRa Local Area Networks, along with a fog computing framework [9], for local communications among the nodes. The IoT gateway protects the nodes from external DoS attacks. On the other hand, the second site sprawls a large area of 72 acres (Figure 1) and uses C-IoT for communications. The Tier 1 nodes in Figure 1 monitor crown part of the hill, whereas Tier 2 and Tier 3 nodes monitor the middle and toe part of the hill, respectively.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 3 Materials and Methods
### Step 5. Identify Mitigation Strategies  
The risk classification conducted at Step 4 defines the risk acceptance levels for the missions and has to be intended as a guideline to identify requirements and initiate discussions for design revisions. Alternatively, it is necessary to provide document analysis for those failures that cannot be corrected at the current stage of the satellite development. Note that the FMECA has to be continuously revised, following the choices made during the design phase, using it as a dynamic decision support tool.
## 2 Materials and Methods
### Cultivation and Collection of Samples to Extract Spectral Data  
According to the stages of the methodology in Figure 1, the first step is to prepare the samples with standard nitrogen fertilizer and with nitrogen over-dose. Hence, several seeds of cucumber of Super ArshiyaF1 variety were planted into 40 pots, and they were grown under laboratory conditions. All pots received the same inputs until the leaves grew; then the pots were divided into 4 groups of 10 pots. The first group with standard nitrogen was considered as the control treatment. A sample view of the pots in this category is shown in Figure 2. The second, third and fourth groups received nitrogen fertilizer overdoses by 30%, 60% and 90%, respectively.  
After 24 h of applying the treatments, sample leaves were obtained from each group and their spectral signatures were extracted by a hyperspectral camera. The process for obtaining the samples of the dataset was as follows. First, 10 leaves were picked from each category, making a total of 40 cucumber leaves. For each leaf, the spectral images were captured, as described in Section 2.3, giving 327 images per leaf (one for each spectral band considered). This makes a total of 13,080 images. Then, the leaves were transferred to laboratory for the measurement of the nitrogen content by chemical analysis, as presented in Section 2.4. After manually analyzing the images, 10 patches were selected for each leaf in the 327 images, calculating the mean of each patch. Thus, 100 samples are available for each category, which consist of a tuple of 327 values, and the corresponding nitrogen content measured.  
Although the process for obtaining this data is expensive (in terms of volume of images and laboratory analysis), the number of 100 samples per class is clearly insufficient  
Figure 1: Main stages of the non-destructive estimation of nitrogen in cucumber plants.  
Figure 2: Cucumber pots prepared for data collection.  
for deep neural network analysis. For this reason, data augmentation was performed to obtain more synthetic samples. This was carried out by random weighted averaging of the real samples (i.e., computing a weighted average of two random tuples, and the same weighted average of the corresponding measured nitrogen content). Using this procedure, 900 synthetic samples per class were produced, totaling 1000 samples per class in the dataset (i.e., 4000 samples in the four classes).
# Machine Learning-Based Smartphone Grip Posture Image Recognition and Classification
## 3 Methods
### Preprocessing  
The captured grip posture image data were processed to remove the background using a skin color and hand contour detection algorithm from Python's open-source computer vision library (OpenCV). The background of the captured input images was removed using a background subtraction algorithm, which was implemented using OpenCV functions including (1) cv2.cvtColor, (2) cv2.GaussianBlur, (3) cv2.findContour, (4) cv2.boundingRect, and (5) cv2.bitwise, applying an adaptive threshold to each image frame [47; 48; 49]. Although the RGB color standard method is commonly used for skin color differentiation, ambient light intensity may affect the actual color. When an object is exposed to light, the color channel values of the actual image may increase, making it difficult to find boundaries for skin color [50]. Therefore, in this study, the YCrCb (Y: luminance, Cr: red chrominance, Cb: blue chrominance) color standard was used to recognize skin color while minimizing the impact of brightness on skin color recognition [51]. As shown in Figure 5, the hand contour is extracted from the binary-segmented image. The hand contour is extracted using the convex hull method, which is based on skin segmentation and convexity defects. Using the extracted hand contour, the boundary is set, and the image frame is cropped [50]. As shown in Figure 6, left hand grip postures were horizontally flipped to match the categories of the right hand grip postures. For example, the left hand grip posture R4-L1 (with four fingers on the right side and one finger on the left side of the phone) was considered and classified as the same data as the right hand grip posture L4-R1 (with four fingers on the left side and one finger on the right side of the phone).  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Grip Posture Name** & **Grip Posture Image** & **Number of Data** \\ \hline
6 & L3-R1-T1 & & 311 \\ \hline
7 & L4-R1 & & 520 \\ \hline \multicolumn{4}{c}{**Total**} & 3278 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: Preprocessing results of grip posture images.