esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors
## 4 Gap Analysis and Directions for Novel Research
### Querying IoT Devices  
Existing techniques for querying and indexing IoT devices require further development to support the global sharing of IoT devices.  
1. Querying IoT device techniques lacks scalability due to the complexity of semantic data and the large number of IoT devices and data descriptions. Therefore, we envision a distributed IoT device query technique that is deployed on each blockchain node. Each query can be processed by multiple nodes that each query part of the RDF-store concurrently to achieve a fast and scalable query technique. Further, deploying IoT device query techniques on the blockchain can make it IoT-owned as no entity owns or controls the query technique.
2. Semantic query languages, which are required to support semantic description such as SPARQL, are complex and require experts to write queries. Also, they are not comprehensive enough to query all or most of the possible aspects of IoT devices, mainly for as they lack a comprehensive ontology. Therefore, we envision the development of an easy-to-use and comprehensive IoT device query language that is able to provide the constructs needed to query IoT devices and support all or most IoT application needs.
3. Existing techniques for indexing IoT devices are not IoT-owned and lack scalability. We envision developing a novel blockchain-based indexing technique that provides fast access to semantically clustered descriptions of IoT devices and their data stored in a blockchain. Early work on this area is published in [67]. The authors have proposed providing (1) a lookup table index for semantic information among blocks and (2) a block-level recursive model index for blocks to improve the query efficiency. However, this work requires development to address the semantic description of IoT devices.
# Traffic Offloading in Unlicensed Spectrum for 5G Cellular Network: A Two-Layer Game Approach
## 4 Coalition Game
### Coalitional Game Definition
Once after the coalition algorithm is executed, \(\sigma_{i}=0\) means the overloaded AP transfers parts of data successfully and just leaves \(n^{*}\) stations. At the same time, the APs with less traffic can accept stations from the heavy-loaded APs. Hence the station number of each AP reaches to \(n^{*}\) finally, namely, \(\sum_{i}n_{i,s}P_{i}\alpha_{i,s}=\sum_{i}n_{i,a}Q_{i}\alpha_{i,a}\). Moreover, if several APs work in the network and all APs station numbers are less than \(n^{*}\), some APs could be turned off and offload their data to other APs for transmitting, and finally every AP has \(n^{*}\) stations. Thus, \(\sigma_{i}=0\) is an ideal situation which means the station numbers in every AP are equal to \(n^{*}\) after onloading and offloading, as well as the performance of the whole WiFi network could reach the optimum value in this case. Case 2: \(\sigma_{i}>0\). However, on some occasions, the ideal situation cannot be reached after searching the whole channel, so a correction factor \(\sigma\) is utilized to balance the stations. In this case, the AP which has too much traffic could get help from other APs in the same coalition to reduce its burden but cannot balance every AP station number equal to \(n^{*}\). Due to the benefit for the players and the whole network's performance improvement after coalition forming, the APs still tend to cooperate with the overloaded AP. Thus, our aim is to minimize \(\sigma_{i}\) and make the active stations number close to \(n^{*}\) as much as possible.
## 2 Method for Measuring Rail Surface Roughness
### Chord-Based Measurement  
There is an advantage to measurement systems using a displacement sensor on a small mobile trolley in that they can measure directly displacement of the rail surface. However, the measured value can not express the actual surface roughness of rail correctly since the sensor only measures the relative distance between the moving platform and the rail surface as the sensor moves with the moving platform it is fixed upon. To compensate for this problem, the system assumes the rail surface is a sinusoidal wave function \((y(x)=Ysin(kx)=Ysin(2\pi x/\lambda))\) with size \(Y\) and wavelength \(\lambda\), defining the ratio of measurement system length (chord length or wheelbase, L) to sensor position, and calculating the transfer function between the measured values to the rail roughness to minimize amplification of the measured value [15]. However, such a modeling method can include an unmeasurable or amplified wavelength range, in which measured value is larger than actual value, depending on the sensor positions on the platform. For example, measuring the wavelength in the \(\lambda=5\)-\(250\) mm band is important when predicting the rolling noise of an electric train by measuring the rail surface roughness. In that case, the analysis method considering such characteristics of wavelength range is necessary to investigate rail acoustic roughness accurately. The surface contact method, which removes the platform's drive wheel and slides with the rail surface, may be used to compensate for the vulnerability of the measured value of the sensor fixed on the moving platform to the measurement of a specific wavelength on the rail surface [16]. However, it is difficult to automate the device, and there may be a large difference in the result value depending on the user. Moreover, the platform size may increase, and the platform surface in contact requires continuous maintenance.  
Therefore, using the chord offset method with multiple sensors on a small mobile measurement system, along with analyzing rail acoustic roughness, has been the most common choice to resolve measurement distortion in a specific wavelength range [17]. The recently proposed chord offset-based measurement method using multiple sensors, like chord offset synchronizing, increases data accuracy by compensating for the size and phase of the measured value based on multiple sensors in the mobile measurement system design and at the same time reducing external disturbance from the driving wheels [14]. However, while increasing the number of sensors mounted on the platform can increase measurement accuracy, this increases both device costs and sensor maintenance. This study analyzed the measured value as it changed with the number of sensors installed on an automated mobile platform when measuring rail surface for such things as corrugation, and examined the impact of the number of sensors on measurements of rail surface corrugation. The results of our analysis were tested and verified with a corrugation measurement test on the railway track in operation.
## 8 Conclusions and Discussion  
### The Limitations of the Review  
This narrative review, while providing a comprehensive overview of new generation urban traffic control systems, has several inherent limitations that should be acknowledged for transparent academic discourse.  
Methodological limitations include the narrative synthesis approach, which, although appropriate for this rapidly evolving interdisciplinary field, lacks the systematic rigor of formal systematic reviews with predetermined search protocols and standardized data extraction procedures. Our literature selection process, while extensive, may have inadvertently overlooked relevant studies due to the absence of exhaustive database searches across all possible sources.  
Bias considerations present another set of limitations. Language bias is evident as our review primarily encompasses English and Chinese publications, potentially excluding valuable insights from research conducted in other linguistic contexts. Geographic bias toward Chinese traffic control developments, while intentional given our focus, may limit the global applicability of certain findings. Temporal bias toward recent publications (2009-2025) reflects the rapid technological evolution in this field but may have underemphasized foundational earlier works that remain conceptually relevant.  
Scope limitations include our focus on urban road networks, which may not fully capture developments in highway or rural traffic control systems. The emphasis on connected and automated vehicle technologies, while reflecting current trends, may have resulted in less coverage of conventional traffic control innovations that remain relevant in many global contexts.  
Technical limitations arise from the interdisciplinary nature of this field, spanning transportation engineering, computer science, and artificial intelligence. The rapid pace of technological advancement means that some cutting-edge developments may not yet be reflected in the peer-reviewed literature, creating a potential gap between current practice and published research.  
Despite these limitations, we believe this review provides valuable insights into the evolution and future directions of urban traffic control systems, particularly in the context of intelligent connected vehicle integration and China's unique urban development challenges.
# An Improved Self-Born Weighted Least Square Method for Cylindicity Error Evaluation
## 2 Model of Least Square Solution of Cylindricity Error  
T Cylindricity error is the amount of deviation between the actual measured cylindrical surface and the ideal cylindrical surface, expressed as the difference between the radii of the two coaxial cylindrical surfaces. Assume that the measured coordinates in the right-angle coordinate system are \(P_{i}(x_{i},y_{i},z_{i})\), \(i=1,2,3,\ldots,n\). The intersection point of the least square cylinder axis L and the cylinder interface is set as \(G(a_{1},b_{1},c_{1})\). The direction vector is set as \(\overrightarrow{n}=(g,k,l)\). The equation of the axis \(L\) is:  
\[\frac{x-a_{1}}{g}=\frac{y-b_{1}}{k}=\frac{z-c_{1}}{l} \tag{1}\]The axial \(L\) equation was simplified to obtain [13]:  
\[\begin{array}{c}\frac{x-a}{p}=\frac{y-b}{q}=z\\ p=\frac{\xi}{q},q=\frac{\xi}{l}\\ a=a_{1}-c_{1}\times\frac{\xi}{l},\ b=b_{1}-c_{1}\times\frac{\xi}{l}\end{array} \tag{2}\]  
The distance \(d_{i}\). from each sampling point on the cylindrical surface to the axis is:  
\[d_{i}=\sqrt{\left[x_{i}-\left(p\times z_{i}+a\right)\right]^{2}+\left[y_{i}- \left(q\times z_{i}+b\right)\right]^{2}} \tag{3}\]  
The least square cylinder radius \(R\) is expressed as:  
\[R=\frac{1}{n}\sum_{i=1}^{n}d_{i} \tag{4}\]  
Then the objective function of the least square cylindrical method is [4]:  
\[J=\min\left(\sum_{i=1}^{n}(d_{i}-R)^{2}\right) \tag{5}\]  
The cylindricity error is transformed into a search for the corresponding ideal cylindrical surface axis parameters \((a,b,p,q)\), which minimize the objective function \(J\). From this, the least square cylinder axis \(L\) equation is obtained, and the maximum distance \(R_{max}\) and the minimum distance \(R_{min}\) from the actual measuring point to the axis are obtained. The diagram is shown in Figure 1. The corresponding cylindricity error:  
\[e=R_{max}-R_{min}s \tag{6}\]
## 4 Conclusions  
This study compares surface and bulk acoustic waves excited by a single laser source, a laser array composed of three sources without time delay, and a laser array of three sources with a time delay imposed on the central source. It is shown that for the same excitation energy density, arrayed photoacoustic excitation leads to surface acoustic waves that are around four times stronger in particular directions than that of a single source. Moreover, by applying an appropriate time delay to the center source, the time and distance required for constructive superposition to form a complete wavefront can be effectively shortened. The use of the laser array has a significant enhancement on the amplitude of bulk acoustic waves propagating into the object, analogous to the synthetic aperture method in ultrasonic detection. In a future study, we will systematically investigate the characteristics of wave generation by laser array sources with different energy spatial distributions, temporal profiles, and delays between pulses in order to obtain a better understanding of the mechanisms of wave generation using laser arrays. Using a laser array with an appropriate delay time can enhance the intensity of the excited acoustic signal without damaging the sample, and the excited acoustic wavefront still propagates in a regular shape with little clutter. This method provides a new way to manipulate the wavefront and improve the generation efficiency in nondestructive photoacoustic imaging applications.  
Conceptualization, J.X., X.L., J.H.M. and L.H.; software, R.H., B.X. and Y.Z.; writing--original draft preparation, R.H.; writing--review and editing, J.X., Z.X., W.L. and C.G.; supervision, J.X. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Major Scientific Instruments and Equipment Development Project, grant no. 61827814, Beijing Natural Science Foundation, grant no. Z19J0018, the Fundamental Research Funds for the Central Universities, grant no. 30920010011, the Ministry of Education collaborative project (B17023). This research was supported by the U.K. Engineering and Physical Sciences Research Council (grant EP/R042578/1).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
In marine seismic exploration, seismic signals comprise primaries that undergo first-order scattering, as well as multiples resulting from multi-order scattering events. Surface-related multiples involve multi-order scattering at the free surface interface between seawater and air and exhibit a smaller reflection angle and broader illumination compared to primaries. Internal multiples, originating from multi-order scattering among stratified layers, provide additional illumination compensation beneath the reflecting interface. However, in conventional primary migration, different-order wavefields may result in crosstalk artifacts. To address this issue, we developed a least-squares migration (LSM) method based on the multi-order wavefield global normalization condition. This methodology investigates the illumination effects and crosstalk artifacts associated with different-order surface-related and internal multiples, and then modifies the global normalization condition by incorporating an illumination compensation perspective. Virtual sources, represented by surface-related multiples and internal multiples, are integrated into the source compensation term, ultimately yielding a multi-order wavefield normalization condition. This normalization condition is subsequently combined with least-squares full-wavefield migration (LSFWM). Numerical experiments demonstrate that the normalization condition of multi-order wavefields can resolve the problem of weak deep imaging energy and promote the suppression of multiple crosstalk artifacts in the least-squares algorithm.  
full wavefield; least-squares migration; image condition; global normalization
## 2 Materials and Methods
### Multivariate Data Analysis
#### 2.5.3 Convolutional Neural Network  
The architecture of our convolutional neural network (CNN) is shown in Figure 2. We adapted the design of VGGNet [20] for one-dimensional spectra inputs. Patterns in spectral curves share some common characteristics with image patterns. For example, peaks and minimums in spectral curve is similar to edges in images. VGGNet is chosen for its high performance in image classification tasks and its modular design make it easy to be modified and extended.  
In Figure 2, an example input of 200 spectral bands was used to show the output size of each block. There are five main blocks in the architecture. The first four blocks are convolutional blocks (Conv Blocks), each of which consists of two consecutive convolutional layers followed by a max pooling layer. As blocks going deeper, the number of convolutional filters is doubled (starting from 16 and ending with 128). All convolutional layers use a kernel size of 3, stride of 1 and padding of 1. A convolutional layer has local connections to its input and can be trained to learn local patterns. By chaining convolutional layers together, deeper layers could have connection to larger part of the raw input. Thus, different layers \"see\" the raw input and learn features at different levels. The last block is a block of a fully connected layer (FC Block). The fully connected layer is used to learn combinations of features extracted by the convolutional layers. At last, a dropout layer and a dense layer were added as the output layer [21].  
The original VGGNet architecture used the rectified linear unit (ReLU) as activation function. The exponential linear unit (ELU) was reported to speed up learning and outperform ReLU [22]. In our experiment, ELU activation showed better performance than ReLU activation with batch normalization [23]. Thus, ELU is utilized in the architecture. ELU function is defined as  
\[f(x)=\left\{\begin{array}{cl}x&if\ x>0\\ \alpha(\exp(x)-1)&if\ x\leq 0\end{array}\right. \tag{2}\]  
Figure 2: The architecture of CNN.  
The output of the CNN is followed by a softmax function to produce values in the range \([0,1]\) as classification confidence scores. A classification loss is then calculated by comparing the confidence scores and true labels of samples. The softmax function and the loss function are defined as follows,  
\[p_{ij}=\frac{e^{z_{ij}}}{\Sigma_{k=1}^{K}e^{z_{ik}}}\ for\ j=1,\ldots,K \tag{3}\]  
\[Loss=-\sum_{i}\sum_{j}label_{ij}\log(p_{ij}) \tag{4}\]  
where \(z\) denotes the output of CNN, \(i\) denotes a sample, \(j\) denotes a class and \(K\) denotes the total number of classes.
# Closed-Form Analytical Solutions for the Deflection of Elastic Beams in a Peridynamic Framework
## 3 Analytical Solutions to Peristatic Problems
### Cantilever Beam  
Consider a cantilever beam subjected to the transverse load \(p(x)\). The solution can be identically decomposed using the following steps (see Figure 12).  
1. Place a fictitious simple support at the free end.
2. Transit the origin to be level with the free end and then subtract the corresponding rigid body motion.
3. Release the vertical displacement constraint and replace it with the corresponding shear force \(Q\).  
The shear force \(Q\) must balance with the resultant of the external loading such that  
\[Q=-\int\limits_{0}^{L}p(x)dx. \tag{38}\]  
By modifying the PD solution for the simply supported-roller-clamped beam (35) with consideration of shear force \(Q\) at the left end, the explicit PD solution for the cantilever beam can be given as follows:  
Figure 12: Solution representation for a cantilever beam.  
\[w(x)=\frac{2\delta^{2}}{LEI}\sum\limits_{n=1}^{\infty}\frac{ \int\limits_{0}^{L}p(x)\text{cos}\frac{(2n-1)\pi x}{2L}\text{d}x+Q}{ \left[\int\limits_{-\delta}^{\delta}\frac{1}{\xi^{2}}\bigg{(}1-\text{cos}\frac{ (2n-1)\pi\xi}{2L}\bigg{)}\text{d}\xi\right]^{2}}\bigg{[}\text{cos}\frac{(2n-1) \pi x}{2L}-1\bigg{]} \tag{39}\] \[=\frac{2\delta^{2}}{LEI}\sum\limits_{n=1}^{\infty}\frac{ \int\limits_{0}^{L}p(x)\bigg{(}\text{cos}\frac{(2n-1)\pi x}{2L}-1 \bigg{)}\text{d}x}{\left[\int\limits_{-\delta}^{\delta}\frac{1}{\xi^{2}} \bigg{(}1-\text{cos}\frac{(2n-1)\pi\xi}{2L}\bigg{)}\text{d}\xi\right]^{2}} \bigg{[}\text{cos}\frac{(2n-1)\pi x}{2L}-1\bigg{]}.\]
## 5 Evaluation of Countermeasures against ZDA
To demonstrate this, suppose that the measurement \(\omega_{g}(t)\) is contaminated by noise and consider two designs of GS: GS1 with \(w=[-28.252,58.5731,-70.906,41.5846]\) and \(z_{d,1}=0.1\), \(z_{d,2}=0.9\), \(z_{d,3}=0\); GS2 with \(w=[-8.924,18.983,-22.903,13.844]\) and \(z_{d,1}=0.1\), \(z_{d,2}=0.7\), \(z_{d,3}=0\). Figure 11 shows the effect of the measurement noise under the same setting of Figure 10. The sampled output of GS \(\hat{y}_{k}\) is denoted by \(\omega_{g,\text{GS}}\) in the figure. In Figure 10(a), some of the sampled outputs of GS \(\omega_{g,\text{GS1}}\) exceed the threshold although no attack signal is injected, leading to a false alarm. On the contrary, Figure 10(b) shows that GS2 with relatively smaller weights is less affected by measurement noise. Figure 11: GS simulation with measurement noise. Figure 10: ZDA detection using a generalized sampler (GS). Since the sampler weights depend on the location of desired zeros, we numerically investigate how they are related. With \(N\) and \(z_{d,3}\) fixed as \(N=4\) and \(z_{d,3}=0\), the desired zeros \(z_{d,1},z_{d,2}\) are selected from \((-1,1)\times(-1,1)\) and the corresponding sampler weights are determined. The result is shown in Figure 12a. In addition, by doing the simulation with the designed GS, we count the number of false alarms, see Figure 12b. It is observed that the number of false alarms is roughly proportional to the norm of sampler weights, and this explains why GS2 is less sensitive to measurement noise.
## 3 Results
### LOC Fractions
#### 3.2.2 Changes in EOC  
The effects of different land use patterns on the concentrations of EOC across different soil layers are illustrated in Figure 5. This study demonstrates that land use patterns significantly influence EOC levels in the soil. In terms of vertical distribution, EOC concentrations in all treatment soil layers decreased with increasing depth, with the most pronounced decrease observed in the SC treatment (60.48%) and the least in the FB treatment (43.81%). Notable differences were evident between treatments at the same soil depth. The concentrations of easily oxidizable organic carbon in the 0-20 cm soil layer, ranked by treatment, are as follows: FB > GB > SC \(\approx\) MC > MSR, with no significant differences among these treatments. Compared to GB, FB exhibited an increase of 3.47%, while SC, MC, and MSR showed reductions in EOC content of 10.7%, 11.76%, and 21.93%, respectively. Notably, the MSR treatment not only recorded the lowest EOC levels among all treatments but also ranked lowest among the three treatments conducted in the field. Furthermore, the reductions observed in the 20-40 cm and 40-60 cm soil layers were greater than those in the surface layers, displaying a similar trend.