esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Overall Performance (RQ1)  
We executed comprehensive experiments on both datasets and then compared the performance with several given baseline methods. Table 2 shows the results of comparing different algorithms with the same evaluation indicators. According to the data analysis in the table, our model outperforms other models on both datasets, and the evaluation performance is obviously stable.  
The best results for the data in Table 2 are shown in bold. Overall, the HR and NDCG values obtained by our experiments are superior to other methods. Compared with these methods, DACR shows better performance ranking. According to the performance comparison, we have the following analysis: DACR model combines attention representation learning and attention matching function learning modules. In experiments on the ml-1m dataset, DACR has a significant performance improvement over other algorithms and is better than the DeepUCF+a model. In the music dataset Lastfm, both the HR and NDCG of DACR reached the maximum value, which shows that our model not only performs well on datasets with low sparsity but still has good capability on datasets with high sparsity. In addition, when implicit feedback is used as input, the performance of DMF is severely degraded, and the performance of our DACR model is better than DMF, which shows that using neural networks as interaction functions can learn nonlinear information that cannot be learned by linear interaction, thereby enhancing the prediction accuracy of the model.  
Quantitatively, on the ml-1m and Lastfm datasets, compared with DeepUCF+a, the HR and NDCG metrics of DACR are improved by 0.88-1.19% and 0.65-1.15%, respectively.  
As mentioned before, BPR and DMF methods only contain shallow data interactions and lack complex and deep multiangle data interactions. DeepCF learns the model using the MLP structure in a way that ignores shallow interactions that are as important as the deep interactions learned by MLP. Therefore, the model has limited performance on the datasets. In other words, the DACR method solves the above two shortcomings and is more superior than the comparison methods.
## 1 Introduction
### Contributions
The implementation is compatible with a wide range of wireless communications technologies and any type of robotic device undertaking nodal mobility. * It presents and analyses results from a comprehensive set of experiments, demonstrating the platform's potential and highlighting the impact of real-world imperfections. The rest of the paper is organized as follows: Section 2 elaborates further on the scope of the proposed MANET platform and on associated use-case scenarios. Then, Section 3 presents the platform's modular architecture and provides all necessary details about the software design, as well as about implementation aspects and the hardware components involved. Subsequently, Section 4 discusses robotic devices that may be employed for the physical motion of the mobile nodes. Section 5 presents results from experiments using the platform. Finally, Section 6 provides concluding remarks.
# Hyperbolic Asynchronous Method of a Radio Navigation Technique
## 5 Measurement's Verification of the Asynchronous Method of Radio Navigation
### Technological Localization Demonstrator for Measurement Verification
The software of the control computer in the locator (User Terminal) is functionally similar to that of the reference station and is presented as follows:  
* loading text-based configuration files,
* connecting to the receiver using TCP/IP and configuring the receiver in the carrier and intermediate frequency range and its operating band,
* checking the connection to the receiver,
* checking if this is the first time the program starts and if so, the program starts the first single data acquisition,
* if this is the next work cycle of the program, it starts multithreaded work, i.e., it starts the next data acquisition and in parallel it starts performing data processing procedures from the previous acquisition,
* support of data card acquisition, their activation and configuration and then 40 s execution of the data acquisition procedure in the form of samples of the received signal at the output,
* reading a data file from a previous acquisition, determining the size of this file and, on this basis, creating a dynamic array to write this data,
* parallel calculation of the degree of correlation between the received data (received signal sequence) and the bit strings of the pilot signal sequence characteristic of individual reference stations,
* recording of the abovementioned received data, including sample numbers indicating the position of the first bit of the pilot string in location messages into a two-dimensional array, in which successive sequences of data received from individual reference stations are recorded in subsequent columns,
* visualization of the data received in the previous step to illustrate the current operating status of the locator (User Terminal),
* measurement of time differences between the received signals, the second component in relation (14),
* reading of location messages and decoding the data stored in them,
* entering the coordinates of the reference stations into the file station.txt,
* calculation of time differences based on coordinates stored in the file station.txt, the third component in dependencies (14),
* entering time differences into the patch_TDOA.txt file, the first component in dependencies (14),
* calculation of locator (User Terminal) position coordinates using dependency (15),
* reading and recording the locator's (User Terminal) position coordinates as reference values, measured with a JAVAD GPS/GLONASS receiver [26] and ALPHA model [27], to perform calculations verifying the quality of operation of the tested asynchronous locating system.
## 3 Results and Discussion
### The SROC Model in Bismuth Borate Glasses  
Initially, we apply the SROC model in xBi\({}_{2}\)O\({}_{3}\)-(1-x)B\({}_{2}\)O\({}_{3}\) glasses in the limited composition range \(0.20\leq\mathrm{x}\leq 0.40\). This restriction is based on our previous finding that beyond x = 0.40, Bi\({}_{2}\)O\({}_{3}\) assumes a dual role both as a modifier and as a glass former, as explicitly shown in ref. [43]. In this case, the charge balance equation, or the calculation of the O/B ratio, \(A(x)\), cannot be applied since the exact fraction of the Bi\({}_{2}\)O\({}_{3}\) acting as a modifier is unknown. So, we are left with only one equation, namely the mass balance or, equivalently, the normalization of the two SROCs at each subinterval of the composition range, with two unknown SROC functions. Prior to the application of the SROC model in bismuth borates, two modifications are needed with respect to xMO-(1-x)B\({}_{2}\)O\({}_{3}\) or xM\({}_{2}\)O-(1-x)B\({}_{2}\)O\({}_{3}\) glasses. These changes originate from the nominal charge of Bi cations, which is +3, that modifies the charge per boron and oxygen atoms per boron functions compared to mono- or divalent cations. More precisely, if we adopt the charge per boron function, i.e., \(c(x)\), for mono- or divalent cations, we have  
\[c(x)=\frac{2x}{2(1-x)}=\frac{x}{1-x}\] (8a) whereas, for trivalent cations like Bi [3+], we obtain \[c(x)=\frac{6x}{2(1-x)}=\frac{3x}{1-x} \tag{8b}\]  
The first modification is reflected in the actual value in the composition range of the three fundamental nodes \(\underline{\mathrm{M}}\), \(\underline{\mathrm{P}}\), and \(\underline{\mathrm{Q}}\) with respect to xMO-(1-x)B\({}_{2}\)O\({}_{3}\) or xM\({}_{2}\)O-(1-x)B\({}_{2}\)O\({}_{3}\) glasses. In particular, the metaborate \(\underline{\mathrm{M}}\) SROC node corresponding to \(c(x)=1\) occurs at \(x=0.5\) for M\({}^{+}\) or M\({}^{++}\) cations and at \(x=0.25\) for Bi\({}^{3+}\) ions. Along the same lines, for Bi\({}^{3+}\) ions, the SROC \(\underline{\mathrm{P}}\) and \(\underline{\mathrm{Q}}\) nodes occur at positions \(x=0.4\) and \(x=0.5\), respectively. This is straightforward from Equation (8b) since for \(\underline{\mathrm{P}}\) node, \(c(x)=2\), and for \(\underline{\mathrm{Q}}\) node, \(c(x)=3\). We recall that for M\({}^{+}\) or M\({}^{++}\) cations, SROC \(\underline{\mathrm{P}}\) and \(\underline{\mathrm{Q}}\) nodes are situated at \(x=0.67\) and \(x=0.75\), respectively. Such changes affect directly the different subintervals of the composition range. In particular, in the present case, we again have three intervals but with different limits: region I with \(x\;\epsilon\;[0,0.25]\), region II with \(x\;\epsilon\;[0.25,0.40]\), and region III with \(x\;\epsilon\;[0.40,0.50]\). The second modification is related to the expressions of the functions \(X_{\underline{N}}(x),\ X_{\underline{M}}(x),X_{\underline{P}}(x)\), and \(X_{\underline{Q}}(x)\). Following the original paper, we use the ratio of oxygen atoms to boron function, namely \(A(x)\).
## 3 Analytic Modeling  
We take a modular approach to creating the analytical model, segregating the study of various elements of system behavior into separate modules. Figure 4 depicts the components of our overall analytic model and their interdependencies with the proposed algorithms. In order to create the approximate mathematical interpretation for each module, it is assumed that the output values of all other modules are available.  
Figure 4 is explained as follows. In Section 3.1, given \(\mathcal{R}\), \(\mathcal{B}\), and \(\mathcal{I}\) as the sets of RF chains, beams, and devices, we employ integer programming to implement a greedy approach for approximate throughput maximization where the throughput (\(\theta_{p,b}^{\prime}\)) and SNR experienced by a link to the device \(t\) using beam \(b\) and chain \(r\) are modeled. Using the  
optimal throughput, \(\theta^{t}_{r,b}\) from Section 3.1, we capture the dynamics of (ergodic) inter-packet delay and propagation delay in Section 3.2 as observed from the transport layer for balancing delays across all links. In Section 3.2, given a total of \(W\) packets to be transmitted across the associated links, our goal is to determine a vector \(\mathbf{w}=[w_{1},w_{2},\ldots,w_{n}]\) so as to minimize the total transmission time \(T(\mathbf{w})\). However, we observe that finding optimal \(\mathbf{w}^{*}\) for MPTCP scheduling is a highly challenging and non-trivial task. Therefore, in Section 4, we model multipath scheduling as a sequential decision problem Multi-Armed Bandits (MAB), with the multipath scheduler and links acting as the actor and arms of the bandits. While the MAB approach resolves the optimization problem centrally, its dependence on local learning often leads to undesirable suboptimal behaviors, whereas the possible decentralized approach requires data sharing from neighbors. In Section 4.1, we enhance the MAB with FL and propose to provide congestion information to new joining MPTCP users during the handshake process. Further enhancements appear to be plausible with opportunistic scheduling; therefore, we provide some new insights and mathematical interpretations of adopting flexible packet-cloning (redundancy for forward erasure correction) for low-delay MPTCP scheduling, which requires further investigations and is left for future work.
## 3 The Proposed EFAFN Model
### Adaptive Feature Fusion  
Typical feature fusion methods are divided into early fusion and late fusion. Early fusion refers to the fusion of features and input into a model for training. For example, the most common method is to perform a simple concatenation operation on the parts. Early fusion methods learn to exploit the correlations and interactions between low-level features of each modality. The late fusion method trains a model for each modality separately and then uses a fusion mechanism to integrate the results of all single-modality models. Commonly used fusion mechanisms include averaging, voting, and training fusion models. Since late fusion methods train different models for different modalities, each modality can be better modeled, allowing for better flexibility. It is worth noting, however, that the late fusion approach ignores low-level interactions between modalities. In theory, early fusion should achieve better results for the sarcasm detection task than late fusion because the corresponding features have a specific index relationship and less feature abstraction in reality. Beyond that, sarcasm detection is all about finding interactions between modalities.  
After the above analysis, to better fuse the features of the three modalities together, we propose an adaptive feature fusion strategy with an early fusion method, as shown in Figure 4. Given the initial text feature \(u_{t}\), speech feature \(u_{a}\), and face image feature \(u_{v}\), the final prediction vector \(u\) is obtained according to the strategy \(\pi\). We approximate the policy by a deep policy network \(\pi(\cdot;\theta)\):  
\[u\sim\pi(u_{t},\varphi(u_{a};\alpha),\phi(u_{v};\beta);\theta) \tag{10}\]  
where \(\alpha\), \(\beta\), and \(\theta\) are the parameters of the network, which can be updated according to gradients. Since text features, speech features, and face image features come from three different modalities, we design two sub-network branches \(\varphi(\cdot;\alpha)\) and \(\phi(\cdot;\beta)\) to combine speech features and face image features are mapped into a feature vector that combines text features.  
Figure 3: Facial feature extraction network architecture diagram. In the above, face recognition uses the face detection model provided by DLIB. ResNet-152 is a residual learning network proposed by [PERSON] et al. (2014). This network is one of the most commonly used backbone networks in image classification tasks. The objects processed in our dataset are videos, so we use this architecture to average the features obtained from each frame in the video as the final facial feature vector.
## 3 Quantification, Assessment, and 3D Visualisation of UES in Naples
### Case Study  
The focus area extends over the Larger Urban Zones (LUZ-) of Naples, as conceived and mapped by the European Environment Agency (EEA) with the \"Urban Atlas\" project [79]. The focus area in Figure 2 is approximately 560 sq km, including Naples city with its 960,000 inhabitants, 14 satellite municipalities connected to the urban core directly, and 21 municipalities affecting the city in terms of economic, social, and environmental pressures.  
Figure 2: Larger Urban Zones (LUZ) of Naples (Italy): a case study.  
The Naples LUZ were chosen as a case study, as they represent a typical urban context in which neighbouring geographical regions have very different morphologies, with an uneven spatial organisation of the districts and scattered high-density zones. The peculiarities of an urban landscape featuring all of these components are suitable for being represented spatially by a 3D GIS environment. In this way, the UES spatial distribution can help to contextualise planning actions appropriately, as well as helping in the observation of the statuses of Regulation, Carrier, and Information services and the evaluation of their changes in space and time. Another reason for choosing this area relates to its data availability. OSM and Urban Atlas--which are the most relevant data sources for this application--release regular updates for the focus area.
## 4 Methodology
### Thermal Error Modeling
#### 4.2.3 IPSO-RBFNN Model on Thermal Error Modeling  
With the temperature-sensitive variables chosen using the KR-BPNN method, the IPSO-RBFNN model is further utilized to fit the data.  
The construction process of the IPSO-RBFNN model is as follows:  
Step 1: Initializing parameters and weights of RBF neural network;  
Step 2: Establishing the thermal error model for the spindle of CNC machine tool based on RBF neural network, and taking the sum of differences between actual output and expectation value as the fitness function of the particle swarm;  
Step 3: Updating parameters and weight using the improved PSO algorithm, as well as the velocity and position of particles;  
Step 4: Determining whether to end conditions (iteration). If yes, getting the optimal arguments of the RBF neural network; otherwise, repeating step 3.  
The flow of IPSO-RBFNN model on thermal error modeling is shown in Figure 6.
# Going Deeper into OSNR Estimation with CNN
## 3 Experimental Result and Analysis  
### Experimental Setup  
Figure 6 demonstrates the experiment platform our OSNR monitoring runs on. On the transceiver side, an external cavity laser (ECL) with less than 100 kHz linewidth generates a 194.1 THz carrier. PDM-QPSK and PDM-16 QAM in 14 Gbaud, 20 Gbaud, and 28 Gbaud Nyquist shaping optical signals with a 0.01 roll-off factor were generated in the DP-I/Q-modulator driven by a four-channel DAC with a 64 GSa/s sampling rate and 8-bit nominal resolution. Gain equalizers inserted in the link were aimed at making cascading of amplifiers possible so that the signal can be transmitted over long distances without distorting the envelope. Erbium-doped fiber amplifiers (EDFA) act as the amplified spontaneous emission (ASE) noise source in the experiment. Variable optical attenuators (VOA) along with EDFAs control the power of signal and noise and thereby adjust the OSNR at the receiver. A span of 80 km standard single-mode fibers (SSMF) with 16.75 ps/(km\(\cdot\)nm) average dispersion simulates the real link for different transmission distances in the recirculating loop for the sake of the acousto-optic switches (AOS). These optical switches are integrated in the fiber-recirculating controller Ovlink IOM-601. After the transmission, the signal is filtered by an optical bandpass filter (OBPF) before being sent into an integrated coherent receiver. The signal is sampled at a rate of 50 GSa/s in an oscilloscope with 16 GHz bandwidth. Finally, the four-channel raw data go directly to the OSNR monitoring network. The measurements of OSA are used as labels in the training process.  
Figure 5: Structures of Reduction blocks.
## 1 Introduction
### Objectives of the Review  
The principal aim of this review paper is to deliver a thorough and critical examination of the existing research and practices related to the seismic performance of structures that incorporate supplementary dampers or additional damping mechanisms. This review specifically seeks to achieve several interconnected objectives. It aims to integrate and assess the theoretical principles that support the implementation of supplementary damping systems in the design of seismic-resistant structures. The paper investigates the diverse range of damping devices currently in use, detailing their operational mechanisms, as well as their respective benefits and drawbacks. It evaluates the methodologies employed in the modeling and design of structures featuring supplementary dampers, encompassing both simplified and sophisticated numerical techniques. The review surveys experimental investigations and real-world applications to evaluate the performance of structures equipped with damping devices when subjected to seismic forces. Furthermore, it identifies essential design factors, challenges in implementation, and relevant code regulations concerning the application of supplementary damping systems. The paper examines recent innovations in damping technologies and their potential effects on future practices in seismic design. It also discusses the reliability, durability, and maintenance needs of structures that utilize supplementary dampers. Importantly, the review points out existing knowledge gaps and proposes avenues for future research in this domain. By fulfilling these objectives, this review aspires to be a significant resource for researchers, practicing engineers, and policymakers engaged in the seismic design and retrofitting of structures.
## 2 Methods
### Shaft with a Single Disk  
Figure 2 shows a disk of mass \(M\) that is assembled on a shaft. The single disk is located between the two bearing supports. It is supposed that the mass of the shaft is not significant compared with the mass of the disk. Considering that \(M\) is the mass of the disk, \(O\) is on the shaft axis, \(G\) is the center of mass of the disk, \(y\) is the deflection of the shaft, \(e\) is the eccentricity, \(F_{c}\) is the centrifugal force, and \(\omega\) is the angular speed of the shaft, then \(F_{c}\) is expressed by Equation (13):  
\[F_{c}=MA=M(y+e)\omega^{2} \tag{13}\]  
The shaft in Figure 2 can be viewed as a spring. Assuming that \(k\) is the shaft spring constant, the shaft resistance force produced by the deflection \(y\) is \(ky\), which is equal to the centrifugal force of the system. Then, another expression can be formulated for \(y\) in Equation (14). Due to the angular speed and type of bearings, it is concluded that the deflection and the imbalance are in phase.  
\[y=\frac{e\omega^{2}}{(\frac{k}{M})-\omega^{2}} \tag{14}\]  
Figure 2: Sketch of an unbalanced disk.  
From Equation (14), it can be noted that, when \(\omega=0\), the deflection \(y\) is also zero. When \(\omega^{2}=k/M\), \(y\) tends to the infinite. For this condition, \(\omega_{n}\) is known as critical speed:  
\[\omega_{n}=\sqrt{\frac{k}{M}} \tag{15}\]  
Figure 3 details the behavior of Equation (15). When \(\omega<\omega_{n}\), \(y\) is positive and it changes of sign when \(\omega>\omega_{n}\). For a high angular speed, \(y\) trends to a negative value equal to the eccentricity \(e\). Moreover, the larger deflection is given for values near to the natural frequency.