esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Experimental Parameters  
Our research benefits from an experimental setup honed through meticulous investigation and a series of prior empirical studies. To fortify the model's robustness and In deep learning, the choice of a loss function is paramount to model performance. Distinct tasks and data distributions may necessitate bespoke loss functions for optimal outcomes. Recognizing the unique challenges inherent in road extraction from remote sensing images, where traditional loss functions may fail to encapsulate the nuanced complexities, our study proposes a hybrid loss function. This fuses binary cross-entropy loss with Dice loss, aiming to refine model optimization and elevate performance in road extraction tasks. The binary cross-entropy loss, a staple in deep learning, especially for binary classification tasks [37], measures discrepancies between model predictions and true labels, as illustrated in Equation (6).  
\[L_{\text{{BCE}}}=-\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}} }\left[y_{i}\log\left(\overset{\cdot}{y_{i}}\right)+(1-y_{i})\log\left(1- \overset{\cdot}{y_{i}}\right)\right] \tag{6}\]  
where \(y_{i}\) represents the real pixel label value, \(\overset{\cdot}{y_{i}}\) represents the label pixel value predicted by the model, and \(N\) represents the number of pixels.  
The Dice loss, also referred to as the Sorensen-Dice coefficient or F1 Score, serves as a metric gauging the similarity between two samples. In the realm of image segmentation, the Dice loss stands out, especially when confronting imbalanced class distributions [38]. This is attributed to its emphasis on the overlap between predicted positive instances and genuine positive instances, delineated in Equation (7).  
\[L_{\text{{dice}}}=1-\frac{2|X\cap Y|}{|X|+|Y|} \tag{7}\]  
where \(X\) is the predicted image generated by the model, \(Y\) is the real label of the input image, \(|X|\) represents the number of pixels in the predicted image, \(|Y|\) represents the number of pixels in the real label, and \(|X\cap Y|\) represents the intersection between predicted maps and ground truth labels.  
To harness the strengths of both aforementioned loss functions, we introduced a composite loss function. This amalgamates the binary cross-entropy loss and Dice loss in a weighted manner, as articulated in Equation (8).  
\[L_{\text{{loss}}}=\alpha L_{\text{{BCE}}}+\beta L_{\text{{dice}}} \tag{8}\]  
where \(\alpha\) and \(\beta\) are weight coefficients. In this experiment, we considered the two loss functions to be equally important, so we set \(\alpha=\beta=0.5\).  
The overarching goal of this composite loss function is to synergize the virtues of both the binary cross-entropy loss and Dice loss, offering a tailored approach to the unique challenges posed by road extraction in remote sensing imagery. Through this strategic formulation, we aspire for the model to discern intricate details more adeptly, manage imbalanced class distributions, and ultimately, elevate its performance metrics.
## 4 Related Research on Service Discovery Protocols
### Directory-Less Architecture
#### 4.2.1 Service Discovery Integrated with Routing  
Integration of service discovery with an underlying routing protocol was first proposed by [PERSON] _et al._[4] and described in an Internet Draft. In their approach, service discovery requests (SREQ) are piggybacked on routing request packets (RREQ) and service discovery replays (SREP) on routing reply (RREP) messages. This solution relies on a reactive routing protocol, such as AODV. Now, several different proposals exist for reactively-routed MANETs, as well as for proactively-routed MANETs.
###### Abstract  
The short spreading code used by the BeiDou Navigation Satellite System (BDS) B1-I or GPS Coarse/Acquisition (C/A) can cause aggregately undesirable cross-correlation between signals within each single constellation. This GPS-to-GPS or BDS-to-BDS correlation is referred to as self-interference. A GPS C/A code self-interference model is extended to propose a self-interference model for BDS B1, taking into account the unique feature of the B1-I signal transmitted by BDS medium Earth orbit (MEO) and inclined geosynchronous orbit (IGSO) satellites--an extra Neumann-Hoffmann (NH) code. Currently there is no analytical model for BDS self-interference and a simple three parameter analytical model is proposed. The model is developed by calculating the spectral separation coefficient (SSC), converting SSC to equivalent white noise power level, and then using this to calculate effective carrier-to-noise density ratio. Cyclostationarity embedded in the signal offers the proposed model additional accuracy in predicting B1-I self-interference. Hardware simulator data are used to validate the model. Software simulator data are used to show the impact of self-interference on a typical BDS receiver including the finding that self-interference effect is most significant when the differential Doppler between desired and undesired signal is zero. Simulation results show the aggregate noise caused by just two undesirable spreading codes on a single desirable signal could lift the receiver noise floor by 3.83 dB under extreme \(C/N_{0}\) (carrier to noise density ratio) conditions (around 20 dB-Hz). This aggregate noise has the potential to increase code tracking standard deviation by 11.65 m under low \(C/N_{0}\) (15-19 dB-Hz) conditions and should therefore, be avoided for high-sensitivity applications. Although the findings refer to Beidou system, the principle weakness of the short codes illuminated here are valid for other satellite navigation systems.  
BDS; short spreading code; high-sensitivity; spectral separation coefficient; self-interference; Neumann-Hoffmann +
Footnote †: journal: Sensors
## 5 Conclusions  
In order to reuse a large amount of construction waste, clay bricks ought to be used in road engineering, as it reduces the environmental and economic pressure. Unconfined compressive strength of cement stabilized macadam subbase is the main strength index in the structure designing process; combining with SEM scanning, XRD scanning and \"modified EDTA titration experiment\", we studied the contribution of the potential activity of RBA to the strength of cement stabilized macadam subbase and the activation mechanism of its potential activity.  
1. Component tests of SEM and XRD scanning showed that SiO\({}_{2}\) andCaCO\({}_{3}\) exist in clay brick as the dominant ingredient.
2. When construction waste clay brick is mixed into cement stabilized macadam in the form of aggregate below 4.75 mm, there is an optimum content of 50% under the strength standard with the RBA in performance described in this article, and the unconfined compressive strength of 7 d, 28 d, and 90d are 5.46 MPa, 7.62 MPa, and 9.61 MPa respectively, which are greater than the strength of the cement stabilized macadam standard specimen and the values stipulated in specification.
3. The titration results of the \"modified EDTA titration test\" show that 50% of specimens have the lowest content of Ca\({}^{2+}\), and the potential activity is maximized in this proportion.
4. All the experimental results show that the RBA used in this study has potential activation in cement stabilized structure, and the optimal replace proportion (50%) of RBA in cement stabilized macadam subbase is proven. In this proportion, RBA replaced CBA and enhanced the unconfined compressive strength; at this time, the potential activity is fully stimulated. Appropriate methods exist to digest construction waste in large quantities and solve the problem of funding for the disposal process. According to this blending method and proportion, it can be widely applied in the road structure subbase, which can solve the problems of environmental governance and economic problems.  
Conceptualization, C.L. and Y.W.; methodology, Y.W. and Y.G.; validation, C.L.; formal analysis, Y.L.; investigation, Y.W. and Y.G.; writing--original draft preparation, C.L.; writing--review and editing, Y.W. and G.T.; project administration, C.L.; funding acquisition, C.L. and W.S.  
This research was funded by the Science Technology Development Program of Jilin Province grant number 2018 ZDGC-9-1 and the National Natural Science Foundation of China grant number 51478203.  
The authors would like to thank the anonymous reviewers for their constructive suggestions and comments to improve the quality of the paper. In addition, thanks to the Second Highway Bureau of China Communications Construction incorporated company for the supply of raw materials.  
The authors declare no conflict of interest.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 2 Related Works
However, significant challenges remain in de signing community detection methods that balance scalability, adaptability, and robustness to noise and dynamic network structures. To address these challenges, this study introduces GraphDBSCAN, a novel hybrid clustering algorithm that integrates multiple enhancements to improve community detection. First, it incorporates an automated DBSCAN parameter tuning mechanism using K-nearest neighbor (KNN)-based Eps selection, eliminating the need for manual parameter adjustment. Second, it employs proximity-based feature extraction to improve density estimation, ensuring more accurate cluster formation. Finally, it enhances modularity refinement through an improved edge removal strategy, refining community structures more effectively than traditional approaches. By combining adaptive clustering, density-aware partitioning, and automated noise reduction, GraphDBSCAN provides a scalable, parameter-free solution that significantly enhances clustering accuracy, modularity, and noise resilience. Experimental results demonstrate that it consistently outperforms state-of-the-art methods across both synthetic and real-world datasets.
## 2 Material and Methods
### Preprocessing  
Since the slide images were large and with blank areas that took the majority of space in the image, it was necessary to reduce the image size as well as re-organize the image in a way to zoom in and locate areas of concern. We adopted a strategy similar to the Patch-Based Image Reconstruction (PBIR) method proposed in [8]. Briefly, PBIR works by splitting the original whole-slide image into multiple small patches of different sizes and selecting the patches with more tissues were chosen to reconstruct a new image. Our preprocessing approach consists of two stages. We firstly performed data augmentation on images via 3D rotation, transpose, vertical flip, and horizontal flip; then, for each whole-slide image, we sliced it into patches of 224 \(\times\) 224 pixels. Out of these tiles, we selected the top 36 most informative ones. The number 36 was empirically chosen to seek a balance between processing speed and model precision. The idea was to keep the tiles with prostate tissues and discard the ones mostly with white background (with a pixel value of 255) that does not have much predictive value to the task. Specifically, we sorted the tiles by the mean pixel value in ascending order, and kept the top 36 tiles that were considered as the most informative ones in the image. The selected tiles were then assembled to form a new image, which served as an input sample used for training. Figure 1 displays the steps of preprocessing that allows the DL model to focus on the areas worth studying.
# An IoT-Based Solution for Intelligent Farming +
## 2 Related Work  
The IoT domain embraces a huge number of applications with different goals, features, requirements and constraints. Consequently, to solve the specific needs of such applications, a wide range of communication models, protocols and standards have been emerging, being either applied on WSN, M2M or Cyber-Physical Systems (CPS) [7]. Considering the needs of the agricultural sector, the first part of this section presents a review of the most relevant communication technologies, protocol stacks and standards. Additionally, we evaluate if their use could meet the SheepIT project needs, as well as other related intelligent farming applications. Then, we perform a survey on existing animal monitoring solutions for livestock support, focusing on their distinctive features.
## 4 FWA Design Considerations, Challenges, and Solutions
### Envisaged 5G Deployment Spectrum
#### 4.1.2 Penetration Loss Owing to Structures (Low-Emissivity Glass)  
It has been observed that there are considerable losses through the walls of buildings during transmission. This can even be comparatively more severe for newer constructions. Likewise, the loss rises swiftly with frequency for certain materials such as concretes. To address the structural loss challenges for indoor reception, some solutions exploit standard two-pane windows as good entry ports due to the associated moderate loss [267; 252].  
Meanwhile, there have been notable works on means of enhancing the building energy efficiency for improved public health, environmental protection and to boost the dwindling economy. An emerging and outstanding driver being employed for building energy efficiency enhancements is the low-emissivity (low-e) pane (window/glass). This can be majorly attributed to its suitable thermal and solar performances. Low-e window is a category of glass that is specifically designed for thermal radiation reduction. In this technology (passive or solar control), special low-e thin metallic coatings (insulators) are employed to reduce the amount of infrared radiation, or heat energy, being transferred via the glass [278; 279; 280; 281; 282]. However, the employment of infrared reflective or low-e glass in the building can significantly cause system coverage reduction due to the higher loss compared with standard two-pane windows. Consequently, low-e glass is considered to be an unreliable entry port for mm-waves [267]. A typical scenario with low-e glass is illustrated in Figure 14a. In [94], measurement results were presented for the associated losses for houses with plain-glass windows and low-emissivity windows. The results showed that low-emissivity window-related losses are relatively higher.
# A Dual-Band Modified Franklin mm-Wave Antenna for 5G Wireless Applications
## 3 Parametric Analysis
### Effect of Length of Second Type Ground Cut \(L_{\text{C}}\)  
The effect of the width of the second type of ground cut on the antenna is analyzed. The length is varied to observe its impact. The variation in the length does not significantly affect the frequency, but it results in a considerable variation in the return loss values at both resonant frequencies. The variation of the length of the second type of ground cut \(L_{\text{C}}\) is given in Figure 12.
## 5 Slowly Varying Fading Parameter in 2 WRC
### Updates of Previously Trained Weights  
As stated previously, training of the ANN-based classifier is further determined by parameters \(E\) and \(\eta\). A basic approach for training an ANN classifier is to randomly initialize its weights and then perform backpropagation. Assume this is done for the training dataset \(\mathcal{D}(\triangle h_{0})\), parameterized by the channel phase. The trained weights are denoted by \(\alpha_{0}\). Consider further that after the training process is performed, the ANN can be used to classify the received symbols for a certain time, while \(\triangle h\approx\triangle h_{0}\). Due to the variability of the wireless channel, consequently \(\triangle h=\triangle h_{1}\
eq\triangle h_{0}\). Inevitably, the classifier needs to be updated according to \(\mathcal{D}(\triangle h_{1})\), resulting in new weights \(\alpha_{1}\). In general, we ask whether we can re-use previously trained results \(\alpha_{i-1}\) for obtaining new weights \(\alpha_{i}\). To provide an answer, we propose three procedures, which differ in the strategy of re-initialization and compare their classification performance.  
For minimizing the number of operations for training, we propose the following general scheme with diverse epoch numbers and learning rates. This procedure repeats with period \(I\). First, the network is trained on \(\mathcal{D}(\triangle h_{0})\) over \(E_{1}\) epochs with rate \(\eta_{1}\). For \(I-1\) subsequent sets \(\mathcal{D}(\triangle h_{i})\), the network is trained over \(E_{2}<E_{1}\) epochs with rate \(\eta_{2}>\eta_{1}\). The motivation is such that a higher number of training epochs \(E_{1}\) is used to train weights via a smaller learning rate \(\eta_{1}\), leading to more precise initial training.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 3 Preliminaries
### CAZAC Sequences  
CAZAC sequences have many excellent characteristics, including constant amplitude, ideal periodic autocorrelation and good cross-correlation properties, and low PMEPR. In addition, they are still CAZAC sequences after the forward and inverse Fourier transform. Therefore, CAZAC sequences are usually used as training sequences for symbol synchronization.  
The most commonly used CAZAC sequences include ZC sequences [36], Frank sequences [37], Golomb polyphase sequences [38], and generalized chirp-like (GCL) sequences [39]. Among these, ZC sequences are the most widely used.  
The ZC sequences can be described as  
\[a_{k}(N,u,q)=\left\{\begin{array}{ll}\exp\Bigl{(}\frac{|2\pi u}{N}\Bigl{(} \frac{k^{2}}{2}+qk\Bigr{)}\Bigr{)},&for\ N\ even\\ \exp\Bigl{(}\frac{|2\pi u}{N}\Bigl{(}\frac{k(k+1)}{2}+qk\Bigr{)}\Bigr{)},&for \ N\ odd\end{array}\right., \tag{3}\]  
where \(N\) is the length of the ZC sequences, \(k=0,1,\cdots,N-1\), \(q\) is an arbitrary integer, and \(u\) is a positive integer coprime to \(N\).  
Let \(a_{k}\) be a ZC sequence of length \(N=sm^{2}\) as defined in Equation (3), where \(s\) and \(m\) are any positive integers, and then the GCL sequences are defined as  
\[s_{k}=a_{k}b_{(k)\text{ mod }m},\ \ \ \ \ k=0,1,\cdots,N-1, \tag{4}\]  
where \(b_{i},\ i=0,1,\cdots,m-1\) is a sequence containing \(m\) complex numbers, whose absolute value is equal to one, and \((k)\) mod \(m\) means that index \(k\) is reduced modulo \(m\). Here, it is easy to see that ZC sequences are exactly the GCL sequences when \(m=1\) and \(b_{i}=1,\ i=0,1,\cdots,m-1\).  
Figure 1: General model of an OFDM system.  
CAZAC sequences have better autocorrelation and cross-correlation properties than traditional PN sequences. The autocorrelation function (ACF) of the CAZAC sequences can be given as  
\[\sum_{k=0}^{N-1}c(k)\cdot c^{*}(k+\tau)=\begin{cases}0,&\tau\
eq 0\\ N,&\tau=0^{\prime}\end{cases} \tag{5}\]  
where \(c(k)\) is a CAZAC sequence with a period of \(N\), and * means the conjugate operation.
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 4 Verification of Mimblewimble
### Security Properties of Pedersen Commitments
#### 4.3.3 Switch Commitments  
As already mentioned, if an attacker succeeds in breaking the computational binding property of a commitment then money can be created from thin air. Switch commitments [39] were introduced to enable the transition from _computational bindingness_ to _statistical bindingness_, specially to the commitments stored in the blockchain. The notion of statistical security implies that a computationally unbounded adversary cannot violate the property except with negligible probability.  
If in a certain moment we believe that the bindingness of the commitment scheme gets broken, we could make a soft fork on the chain and switch existing commitments to this new validation scheme which is backwards compatible.  
Below, we show the changes that are needed for our model to also encompass Switch commitments. In Pedersen commitment definition (Definition 2) we add a third point generator \(J\) of the elliptic curve \(\mathcal{C}\) whose discrete logarithm relative to \(G\) and \(H\) is unknown. We define the new commitment algorithm as follows:  
\[Com(v,r)=r^{\prime}.G+v.H,with\ v\ the\ transactional\ value\] \[r^{\prime}=r+hash(v.H+r.G,r.J),\]  
where \(r\) is the blinding factor randomly chosen in the finite field \(\mathbb{F}_{n}\) and \(J\) is the third point generator.  
Note that \(r\) is still randomly distributed and the hash value of _EIGamal_ commitment is computed which is the combination of ElGamal encryption [40] and a commitment scheme.
## 1 Introduction  
In recent years, Wireless Visual Sensor Networks (WVSNs) have emerged as an interesting field. Popular applications are environmental monitoring, seismic detection, military surveillance, medical monitoring, video surveillance, or the Internet of Things (IoT), _etc._[1]. IoT integrates a part of the Future Internet and extends network capabilities to Machine-to-Machine (M2M) communications by a dynamic network infrastructure with self-organization techniques. As compared to a WSN, the camera equipped sensor nodes in WVSNs can capture, process and transmit the real time visual data (images, video). Since the visual data is much larger and complicated than scalar data, delay Quality of Service (QoS) is a challenging issue resource for constrained WVSNs. WVSNs attract more attention on how the camera sensor nodes can cooperatively pass their data more efficiently with minimum delay through the network on a large scale or under realistic physical environmental conditions [2,3]. The architecture of WVSNs is shown in Figure 1.  
In WVSNs for video surveillance, the delay is a very important QoS metric, and the delay is highly dependent on the routing strategies. However, delay routing in WVSN is more difficult than in WSN due to the fact visual data is much larger and complicated than scalar data. Furthermore, battery-limited powered and resource (channel-, CPU-) constrained camera nodes make this delay routing problem more challenging in WVSNs. [4]. Basically, for real-time video surveillance applications, meeting the end-to-end delay QoS is the most important criteria. However, due to the limited resources of WVSNs, delay QoS routing decisions should also consider overall resource utilization so that the future video surveillance applications could be also satisfied. Hence, besides meeting the end-to-end delay QoS for each user, minimizing the average delay in WVSNs is also another crucial performance metric. In this paper, for the first time, we consider the delay QoS routing  
Figure 1: Wireless Visual Sensor Networks.  
strategy in WVSNs to meet the end-to-end delay QoS for each application and at the same time minimize the average delay.
# Study on Nitrogen-Doped Graphene Ink and Its Effects on the Heat Dissipation for the LED Lamps
## 3 Experiment Description
### Nitrogen-Doped Graphene (NDG) Ink and NDG-Ink Coated (NGC) LED Lamp Experiment
#### 3.2.4 Temperature Measurement  
Thermocouples with a recorder were used to measure the temperature distribution for the LED lamp. In a sealed box of 2 \(m^{3}\), a DR230 temperature recorder, a hybrid multi-channel record, was used for the experiment, as shown in Figure 4. The scanning time was 500 ms for up to 300 channels. Meanwhile, the record could be connected to the computer for data transmission and analysis. In the box, there were 30 points for the installation of thermal couples. In the experiment, the K-type of the thermal couple was used. The direct current (DC) regulated power supply was applied for the power input in the experiment. There were eight monitoring points for the LED lamp, as shown in Figure 5. The LED lamps under investigation included the original lamp, fin-removed lamp, and NGC lamp, as shown in Figure 6.  
Figure 4: Hybrid recorder DR230.  
Figure 5: Layout of the monitoring points.  
Figure 6: Different types of the LED lamps. (**a**) Original lamp, (**b**) Fin-removed lamp, (**c**) NGC lamp