esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Performance Evaluation for Overhead  
Figure 16 presents the total overhead observed for the routing protocols in the experiments. The LECAR protocol generates the highest overhead compared with the others. It is because, LECAR must share the location information, buffer occupancy information, and other routing information to make the proper routing decision. The generated overhead is relatively low and almost the same as Spray and Wait and LAROD-LoDiS when there are 20 or fewer UAVs. Afterward, with the increase in the number of UAVs, the overhead increases significantly. The reason is understandable: LECAR is primarily designed for scenarios where a small number of UAVs cover a large area (i.e., when the density of the UAVs is relatively low). Therefore, the overhead of LECAR increases with the increase in the density of the UAVs. In addition, LER exhibits a similar tendency with slightly lower overhead than LECAR because it does not share the buffer occupancy information. Moreover, LAROD-LoDiS produces the next highest overhead because it shares the location information but in a briefer form. Spray and Wait generates moderate overhead. Finally, GPSR and GPSR-Q generate the lowest overhead following the previous results.  
Figure 16: Performance comparison of the considered routing protocols regarding overhead when the buffer size is **(a)** 25 MB and **(b)** 50 MB.  
Figure 15: Performance comparison of the routing protocols regarding the delay per packet when the buffer size is **(a)** 25 MB and **(b)** 50 MB.
# Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation
## 3 Results
### Validation Using NWP Model Data
#### 3.3.1 Comparison against the Previous 1D-Var Version  
Two decades ago, UCAR developed an atmospheric 1D-Var to overcome the difficulties faced by pre-existent, traditional retrieval methods, and recently the 1D-Var underwent a significant upgrade. Figure 9 compares two 1D-Var versions, the one described in this paper (NEW), which produces \"wetPf2\" data, and the previous version (OLD) used to produce \"wetPrf\" data. Shown here is the root-mean-square difference (RMSD) from the operational ECMWF analysis computed over the COSMIC profiles (\"atmPrf\") available for 2008-2014. A priori profiles, shared by OLD and NEW, are generated from the operational NCEP analysis, which is independent of the ECMWF analysis to a certain extent. In the temperature RMSD (upper panels), OLD varies significantly with the latitude and height and is significantly larger than NEW. The RMSD of OLD exceeds four degrees in the lower troposphere of some southern latitudes, as opposed to NEW which shows smaller, quasi-uniform RMSDs across all latitudes. In the specific humidity RMSD (lower panels), OLD is significant in low latitudes with maxima around 15\({}^{\circ}\)S and 20\({}^{\circ}\)N, increasing toward the surface. Again, OLD is notably larger than NEW in all latitudes and heights.  
The leading cause of the disparity between OLD and NEW is the difference in the degree to which 1D-Var fits RO observations. NEW determines the fitting based on the observation error relative to the background error, whereas OLD attempts to fit RO refractivity exactly. With the exact fitting, we intended OLD to reduce the dependence of the 1D-Var solution on the background and squeeze out the most information from observations. However, this exact fitting to imperfect observations comes at a cost. One good example is that an observed refractivity in the lower troposphere has significant negative bias under a cold, dry atmospheric condition, while the background is unbiased. In this case, OLD can reproduce the negatively biased refractivity by either reducing the background humidity or increasing the temperature. Of the two options, the first is not possible because OLD cannot dehydrate the already-dry background. Consequently, OLD seeks a solution warmer than the background, which leads to an excessive temperature correction when the observation bias is large. An extreme background correction can also occur when the observation is much larger than its background counterpart in a saturated atmosphere. Although these circumstances are occasional, the subsequent background correction is so significant that OLD becomes much larger than NEW in temperature and moisture RMSDs. We find that the exact fitting to imperfect observations pursued by OLD leads to suboptimal combinations of retrieved temperature, pressure, and moisture, causing OLD to bear much larger RMSDs. On the other hand, NEW effects modest and spatially slow-varying RMSDs (Figure 9b,d).
## 2 Theory
### Improved Cam-Shift Algorithm  
A traditional Cam-Shift algorithm achieves the tracking of a specific target based on a color histogram of the target area and can adaptively change the size of the search window to track the target accurately. The traditional Cam-Shift algorithm performs well under a simple background. However, it loses the target easily when the background gets complex or severe occlusion occurs. That is because under a more complicated background, there are many external factors that have an effect on the color characteristic of the target, such as the interference of the similar color, occlusion, variant posture, etc. If the traditional Cam-Shift algorithm is used under these circumstances, it will cause the wrong location of the target to be identified and eventually the target tracking fails.  
The Kalman filter algorithm is an optimal linear recursive filtering algorithm based on the minimum mean square error (MMSE) and it can predict the next state according to the current state of the target. In an OVLC system with different interferences, the target LED signal source tracking can be affected by various factors. Thus, the proposed algorithm combines traditional Cam-Shift and Kalman filter algorithms, using a Kalman filter to predict the location of the target in the next frame according to the current state information first, and then invoking a Cam-Shift algorithm that uses the predicted position as its initial searching position. The improved algorithm can not only shorten the searching path of the Cam-Shift algorithm but also reduces the effect from external factors, enhancing the robustness of the algorithm.  
A detailed process of the proposed tracking algorithm is shown in Figure 6. First, select the target LED source signal area as the tracking target and initialize the search window. Then, use the Kalman filter algorithm and Cam-Shift algorithm separately. When there is no similar color interference or severe occlusion, the central position of the search window is used as the measurement to update the Kalman filter; otherwise, the predicted value calculated by the Kalman filter is used as the measurement. This paper uses \(\mu\) and \(\gamma\) to determine whether the tracking is interfered by similar color or severe occlusion, respectively. The calculation formulas of \(\mu\) and \(\gamma\) are as follows:  
\[\left\{\begin{array}{l}\mu=Rect_{current}/Rect_{previous}\\ \gamma=Rect_{current}/Rect_{origin}\end{array}\right. \tag{22}\]  
where \(Rect_{current}\) is the size of the search window of the current frame calculated using the Cam-Shift algorithm, \(Rect_{previous}\) is the size of the search window of the previous frame, and \(Rect_{origin}\) is the size of the initialized search window. If \(\mu>threshold_{1}\), it is considered that the similar color interference is happening. If \(\gamma<threshold_{2}\), it is considered that severe occlusion is occurring.
# Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack
## 2 Background
### Blockchain Technology: The Digital Concept
#### 2.1.3 Hashing and Digital Signatures  
A digital signature is a mechanism that is used to validate the accuracy and integrity of a data string [6]. Every block comprises one unique digital signature based on the strings that the block contains. Figure 2 presents a partial view of 3 blocks that are chained to each other. Each block produces a digital signature which is generated considering the contents, such as Merkle hash and the digital signature of previous blocks. A digital signature does not always qualify to validate the next block, as the bitcoin blockchain sets a level of difficulty to determine the block creation time. Hence, having fewer transactions in a block does not necessarily help to create a block faster than the other nodes. If the difficulty level requires having 17 zeros in front of the digital signature, then the miners require meeting that condition to validate a block. An attacker making modifications to the data string changes the digital signature and will fail to meet the requirement for the next blocks that are chained to it [7]. To overcome the scenario, a new signature must be generated for every subsequent block  
Figure 1: The layout of a bitcoin block. The block number contains a 4-byte arbitrary number confirming it as a bitcoin block, and the block size ensures the size of the block. A block header layout shows its contents. The block header is hashed using SHA-256 and the hash value to be added to the “previous hash” section of the next block header. The transaction counter counts the total number of transactions, and the transaction list contains the list of all transactions added to the block.  
on the chain. The process becomes very costly and nearly impossible, as the attacker is required to generate new signatures not only for the blocks which have been corrupted but also any blocks that are being added on a regular interval.
## 4 Discussion
### Selection of Research Methods  
Traditional methods for measuring and analyzing terrain features rely on manual field surveys and GIS statistical analysis [39]. While these methods have been widely employed, they pose challenges in measuring complex terrain features and demand significant labor and time investment. In contrast to [PERSON]'s study [40], this research utilizes terrain feature parameters for terrain analysis. This approach enables measurements of landforms in multiple dimensions and scales, incorporating both qualitative and quantitative assessments to characterize the terrain visually.  
The choice of terrain feature parameters holds significance as it directly impacts the integrity of terrain feature identification. Considering each terrain feature parameter's unique geological significance, choosing representative indicators that comprehensively depict terrain features from numerous parameters becomes crucial. This helps minimize information overlap and data redundancy, enhancing computational efficiency and accuracy. In contrast to [PERSON]'s study [41], this research conducted a correlation analysis of terrain feature parameters and excluded indicators with a correlation of 0.937 or higher. This approach identifies the optimal combination of parameters describing the topographic features in the study area, reducing redundancy in topographic feature information.
## 4 Simulation Experiments and Data Analysis  
### Simulation Parameters  
In order to verify the efficiency and stability of the proposed optimization scheme, this paper simplifies the network model and limits the research scope to a small-scale environment; therefore, the following parameter settings are implemented. The setup scenario is a vehicle traveling at a speed \(\mathbf{v}\) at a uniform speed on a 2000-meter road, and the tasks generated by the vehicle can be randomly adjusted in the range between [20, 100]. An RSU is installed every 400 m along the road. Each RSU is equipped with an Intel Atom x7-E3950 (4-core) CPU, and each RSU is accompanied by an MEC server equipped with an AMD EPYC 9554P (64-core) processor. Five RSUs are set up as nodes in the blockchain, where the nodes participating in the consensus are set up as four. The simulation experiment is repeated for 20 independent trials to eliminate the effect of randomness. Other parameter settings in the simulation environment are shown in Table 1.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 2 Open Technological Issues
### End-Effector Design  
The design of the end effector also plays a crucial role in bin-picking success rates, as the objects that need to be grasped from the bin are non-orientated, potentially interlocked, jumbled, and heavily occluding each other.  
Gripper designs in bin-picking applications range from two-finger to multi-finger grippers, from suction or magnetic grippers [17] to soft grippers [18]. In the field of bin-picking, suction typically has an advantage over parallel-jaw or multi-finger grasping due to its ability to reach narrow spaces and pick up objects with a single point of contact [19]. However, the choice of the most suitable end effector for bin-picking tasks depends on a number of factors.  
First, the optimal end effector design depends on the geometric shape of the parts that need to be picked from the bin. For example, in a crowded bin filled with adjacent cuboid objects, there may be no gaps to insert fingers for picking. In this situation, the objects can only be grasped from above, and a suction gripper is more effective than a two-fingered gripper. Additionally, when the bin is filled with items with various shapes, textures, and materials, a single end effector may not be able to effectively grasp all of them. Tool chargers, commonly used in bin-picking systems, can allow robots to switch grippers to attempt different types of grasp on the object [20]. In [9], a multi-gripper strategy has been implemented in order to enable more robust ways of grasping objects using suction and multiple fingertips. Additionally, in [16], the possibility of using a dual-arm cobot with a different end effector in each arm has been explored. Moreover, the most appropriate end effector also depends on the state of the bin, which changes while robots pick items from it: a multi-gripper switching strategy based on object sparseness was proposed in [21].  
Finally, end-effectors used in bin-picking applications may also include force sensors to detect collisions with items and perform force control to avoid damaging items, as well as to ensure that the objects have been correctly grasped.
## 3 Research Methods
### NISDL Algorithm
#### 3.3.3 NISDL Method II  
Figure 2 is the deep learning framework of NISDL method II. In this method, the ROI images and SSI values were processed for features extraction. Subsequently, the two kinds of features were combined in the second half of the whole framework. An average pooling layer and the DenseNet201 (excluding the last two layers) were also used for features extraction of ROI images. For SSI values, a convolution layer and an average pooling layer were adopted for feature extraction and dimensionality reduction. After features combination, three fully connected layers were constructed in NISDL method II. Therefore, the skin temperature can be obtained. The algorithm in detail, including NISDL method I and II, is shown in Table 1.  
Figure 1: NISDL method I. (SSI values were combined with skin images firstly.).  
Figure 2: NISDL method II. (SSI values and skin images were processed by deep learning networks. Two kinds of features were extracted and the features were combined.).
# Quantification and Analysis of Carrier-to-Interference Ratio in High-Throughput Satellite Systems
## 3 Quantification Method of CIR
### Multi-Carrier Scenarios
\begin{array}{ll}(C/I)_{fd}&=\frac{P_{1}G_{1}(\theta_{1})}{\frac{1}{\gamma}I_{ i}}=\frac{P_{1}G_{1}(\theta_{1})}{\sum\limits_{i}^{N}\sum\limits_{j=1}^{N_{i}} \frac{[(f_{ij}^{step}-f_{ij}^{start}+f_{2}-f_{1})-(\max(f_{ij}^{step},f_{2})-\min( f_{ij}^{start},f_{1}))]P_{1}G_{1}(\theta)}}\\ &=\frac{P_{1}G_{1}(\theta_{1})}{\sum\limits_{i}^{N_{i}}\sum\limits_{j=1}^{N_{i} }\frac{[(f_{ij}^{step}-f_{ij}^{start}+f_{2}-f_{1})-(\max(f_{ij}^{step},f_{2})- \min(f_{ij}^{start},f_{1}))]P_{1}G_{1}(\theta)}{\theta_{1}}}\\ &=\frac{P_{1}G_{1}(\theta_{1})}{\sum\limits_{i}^{N_{i}}\sum\limits_{j=1}^{N_{i }}\frac{[(f_{ij}^{step}-f_{ij}^{start}+f_{2}-f_{1})-(\max(f_{ij}^{step},f_{2})- \min(f_{ij}^{start},f_{1}))]P_{1}G_{1}(\theta)}{\theta_{1}}}}\\ \end{array}  \tag{22}\]  
In the same way, if the target beam also includes multi-carriers, and the total transmitting power and bandwidth are denoted as \(P_{0}\) and \(B_{0}\), respectively, Equation (22) should turn into:  
\[ \begin{array}{ll}(C/I)_{fd}&=\frac{(f_{2}-f_{1})P_{0}G_{1}(\theta_{1})/B_{0 }}{\sum\limits_{i}^{N}\sum\limits_{j=1}^{N_{i}}\frac{[(f_{ij}^{step}-f_{ij}^{ start}+f_{2}-f_{1})-(\max(f_{ij}^{step},f_{2})-\min(f_{ij}^{start},f_{1}))]P_{1}G_{1 }(\theta)}{B_{1}}}\\ \end{array}  \tag{23}\]
## 3 Dynamic Neural Network Identification
### Identifier Based on Dynamic Neural Networks
: _The approximation error \(\tilde{f}_{\varrho}(\zeta,t)\) is bounded as_  
\[|\tilde{f}_{\varrho}(\zeta,t)|^{2}\leq f_{0}+f_{1}\|\boldsymbol{\xi}\|^{2}, \tag{30}\]  
_where \(f_{0}\) and \(f_{1}\) are positive constants._  
The elements of the vector \(\boldsymbol{\
abla}(\zeta)\) are defined as sigmoid-type activation functions:  
\[\boldsymbol{\
abla}_{i}(\zeta_{i}):=\mathbf{b}_{i}(1+e^{-\mathbf{d}_{i}\zeta _{i}})^{-1}-\mathbf{c}_{i},\;\;i=1,...,n \tag{31}\]where the positive real parameters \(\mathbf{b}_{i}\), \(\mathbf{d}_{i}\), and \(\mathbf{c}_{i}\) represent the bound, the slope, and a constant that shifts the function, respectively. The vector activation function \(\boldsymbol{\Psi}\) fulfill the condition  
\[\boldsymbol{\tilde{\Psi}}^{\top}\Delta\boldsymbol{\tilde{\Psi}}\leq \boldsymbol{\tilde{\zeta}}^{\top}\,\boldsymbol{D}\boldsymbol{\tau}\boldsymbol{ \tilde{\zeta}}, \tag{32}\]  
where \(\boldsymbol{D}_{\boldsymbol{\Psi}}\) is a positive definite matrix. From Equations (24) and (25), the dynamic of the identification error is given by  
\[\boldsymbol{\dot{\tilde{\zeta}}}=\left[ \begin{array}{c}\xi_{2}\\ a_{1}\xi_{1}+a_{2}\xi_{2}+\bar{b}(t)u(\zeta)+\boldsymbol{\tilde{W}}(t)^{\top} \boldsymbol{\Psi}\Big{(}\boldsymbol{\tilde{\zeta}}\Big{)}+\boldsymbol{W_{0}} ^{\top}\boldsymbol{\tilde{\Psi}}+\bar{f}_{\varrho}(\boldsymbol{\zeta},t)- \boldsymbol{K_{id}}^{\top}\boldsymbol{\tilde{\zeta}}\end{array} \right]_{.} \tag{33}\]  
where \(\bar{b}(t)=b-\bar{b}(t)\) is the parameter error. The weight and activation function errors are defined as \(\boldsymbol{\tilde{W}}(t)=\boldsymbol{W_{0}}-\boldsymbol{W}(t)\) and \(\boldsymbol{\tilde{\Psi}}=\boldsymbol{\tilde{\Psi}}(\zeta)-\boldsymbol{\Psi} \Big{(}\boldsymbol{\tilde{\zeta}}\Big{)}\), respectively. Defining the matrices  
\[\boldsymbol{A}= \begin{bmatrix}0&1\\ a_{1}&a_{2}\end{bmatrix} _{,}\quad\boldsymbol{B}= \begin{bmatrix}0\\ 1_{,}\end{bmatrix} _{,} \tag{34}\]  
then the identification error dynamic can be written as  
\[\boldsymbol{\dot{\tilde{\zeta}}}=\boldsymbol{A}\boldsymbol{\tilde{\zeta}}+ \boldsymbol{B}\bar{b}(t)u(\boldsymbol{\zeta})+\boldsymbol{B}\boldsymbol{\tilde {W}}(t)^{\top}\boldsymbol{\Psi}\Big{(}\boldsymbol{\tilde{\zeta}}\Big{)}+ \boldsymbol{B}\boldsymbol{W_{0}}^{\top}\boldsymbol{\tilde{\Psi}}+ \boldsymbol{B}\bar{f}_{\varrho}(\boldsymbol{\zeta},t)-\boldsymbol{B} \boldsymbol{K_{id}}^{\top}\boldsymbol{\tilde{\zeta}}. \tag{35}\]  
where \(A\) results in a Hurwitz matrix. The time-varying weight vector \(\boldsymbol{W}(t)\) is adjusted by certain weight learning laws designed by Lyapunov stability:  
\[\boldsymbol{\dot{W}}(t)=\Gamma(\boldsymbol{W}(t),\boldsymbol{\tilde{\zeta}}) \tag{36}\]  
Notice that the nonlinear function in the right hand side of Equation (36) depends on the identification error. This means that, if the state of the system Equation (24) deviates from the identifier state, the weight vector changes in order to improve the approximation of the unknown plant structure as it evolves over time. The DNN adjustment is explained in more detail in Section 5.
# Characteristics of BeiDou-3 Experimental Satellite Clocks
## 1 Introduction
In view of the limited number of tracking stations for BeiDou-3, a controlled trial using the current BeiDou-2 satellites is set up to figure out the impact of the performance of the clock offsets estimated by global stations and regional stations. Moreover, the frequency characteristics of the BeiDou-3 satellites are discussed with Non-Uniform Fast Fourier Transform (NUFFT), and the results of the prediction are presented in order to verify the clock characterization.
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 2 Related Work
### Tree Growing Algorithm
#### Enhanced Variation  
An enhanced variation of the tree growing algorithm iteratively adds grey and white nodes to the DS. Like the greedy tree growing algorithm, this algorithm colors node either in white, grey, or green in order to express the node's state. White nodes are non-dominators. Grey nodes are also non-dominators, but are adjacent to at least one dominator. Green node are dominators. Algorithm 2 shows the enhanced tree growing algorithm in pseudo-code. When all nodes are colored in green or grey, the algorithm has successfully constructed a CDS. However, centralized CDS construction algorithms are usually not directly applicable to WSNs due to the absence of a central administration and possible large network size [21]. For this reason, we design and implement a fully distributed tree growing algorithm, which is based on the enhanced variation of the tree growing algorithm. We discuss the design of our algorithm in the next section.  
```
1:initially color all nodes in white
2:whilewhite nodes existing do
3:choose a grey or white node \(n\) with the largest number of white neighbors
4:color node \(n\) in green and the neighbors of node \(n\) in grey
5:endwhile
6:while dominating set is not connected do
7:choose a grey node \(m\) with the largest number of green neighbors
8:color node \(m\) in green
9:endwhile
```  
**Algorithm 2** Enhanced tree growing algorithm.