esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks
## 6 Conclusions  
In this paper, we have investigated the joint optimization of client selection and communication resource allocation in FL-aided wireless IoT networks. By taking both the amount of trained data and the consumption of communication resources into consideration, a MINLP was formulated to maximize the utility of the network. Then, an alternative direction-based algorithm was proposed to solve the problem efficiently. The simulation results have confirmed that the proposed algorithm is effective in reducing communication resource consumption and improving learning performance. Furthermore, the results also revealed that distinguishing node weights based on the amount of collected data is beneficial for FL. In the future work, we will extend the algorithm to the FL-enabled scenario with massive multiple-input multiple-output communications, where the higher dimensional signals make joint optimization of client selection and resource allocation more challenging.  
Conceptualization, J.Z. and Y.N.; methodology, J.Z. and Y.C.; validation, J.Z. and Y.N.; formal analysis, J.Z.; algorithm investigation, J.Z. and Y.C.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z., Y.N. and Y.C.; supervision, Y.N.; project administration, Y.N.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Jiangsu University Philosophy and Social Science Research Fund under Grant 2022 SYB0517, in part by the Open Research Fund Project of Jiangsu Provincial Key Laboratory of Wireless Communication at Nanjing University of Posts and Telecommunications: Research on Key Technologies of Backscattering Communication Assisted by Intelligent Reflective Surface, and in part by the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20212001.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
The authors declare no conflicts of interest.
## 4 Improved Routing Protocol
### Comparative Study with Other Novel Solutions  
It has often been encountered that the survived nodes may still benefit us if the wireless sensor nodes remain somehow active after disasters. Aimed at this circumstance, specialized architectures of WSNs and fault-tolerant routing protocols have been proposed. Hierarchical query protocols such as low energy adaptive clustering hierarchy (LEACH) [30] and TopDisc [31] have been used in some extreme environments [32; 33]. The key purpose of these protocols is choosing clusters by distributed algorithms in a hierarchical network since the node membership will dynamically change, which will force clusters to evolve over time. To assure the effective communication between each cluster, these routing protocols require that the WSNs have densely distributed nodes in a two-dimensional plane and utilize boosted clusters. However, in our application, the reconstructed communication network consists of identical nodes in elongated geographic areas. To make the routing protocol more applicable for this kind of network topology, directed diffusion protocol attracts more interest [34]. It is a non-geographic query-routing method that is based on flooding all nodes with the query, with the aim of finding every possible route from the sink and the location source, and then selecting the optimal route. This protocol is designed to reach a definite position of the network, which is suitable for our application. However, it can not prevent the construction of curved paths. Specifically speaking, there may be much meandering along the path, leading to correspondingly longer routing response time and a greater amount of battery consumption. Moreover, it does not apply to transmission of small amounts of data after receiving requests for its huge cost of route discovery.  
Compared to the aforementioned novel solutions, the advantage of the proposed routing protocol is that it is a kind of table-driven routing protocol without frequent route discovery, and the route  
Figure 5: Schematic diagram of forward/backward judgment. (**a**) forward list; (**b**) backward list; and (**c**) nodes in short range.  
is locally rebuilt without discovery packets flooding in the overall network. Moreover, the route assures the directional propagation of data packets to get a lower number of hops, thus resulting in low end-to-end delay. Therefore, the proposed routing protocol based on geographic information may be more suitable for this application.
## 4 Results and Discussion
### HVSR Analysis Resonance Frequencies  
In Figure 6, some representative cases of the HVSR curves obtained along the landslide are shown. The locations of some of these sites, close to the landslide flanks, may explain the presence of some broad peaks with rather low amplitudes. At these sites, the wavefields associated with local noise sources are more complex, since the wavefields also include additional waves diffracted from the lateral heterogeneities. Experimental tests and numerical simulations have shown that, for local surface sources, the HVSR curves obtained at laterally varying structures present broader and lower maxima, which may even be hard to identify [60].  
Figure 6: Examples of HVSR curves estimated in the central area of the landslide. Check Figure 3 for the location of the sites.  
In our study case, the resonance frequency, f, associated with the contrast between the sediments and the stiffer materials below the landslide, can be related to the average shear-wave velocity of this one-dimensional stratigraphy, Vs, and the corresponding thickness, h, through the equation [33]:  
\[\text{f}\left(\text{Hz}\right)=\text{Vs}/(4\text{ h}) \tag{1}\]  
Therefore, attending to the borehole information, it can be estimated that the resonance frequencies of interest for the landslide characterization present values higher than 2 Hz. In this case, peaks below 2 Hz might be associated with deeper contrasts from the contacts between materials of the sedimentary fill of the Granada basin (or between this fill and the basement).
## 3 Background
### Linux Secure Computing Mode (Seccomp)  
To enhance the security, the Linux kernel offers several functionalities, such as Netfilter and Linux Security Modules. The Linux secure computing mode (seccomp) is one of them, which offers secure state management in system call processing. Seccomp prevents applications from executing unauthorized system calls in order to avoid negative impacts (e.g., a bug or malicious attack) on the host OS. If an application attempts to execute the unauthorized system call, the Linux kernel immediately terminates the application using SIGKILL or SIGSYS.  
Seccomp offers STRICT and FILTER modes, and users can opt for one of them to utilize the seccomp functionalities. First, the STRICT mode allows applications to execute only four system calls, exit(), sriptum(), and read() and write(), to already-open file descriptors. Second, the FILTER mode enables users to specify the available system calls for their applications depending on their purposes. This can be performed by initializing a system call filter that consists of available system calls and registering the filter to seccomp using Linux kernel APIs. For example, gVisor enables the applications in the container to execute 211 system calls out of 319 Linux system calls.
## 3 Results  
### Comparison of Stress-Strain Curves  
Figure 3 shows the stress-strain (\(\sigma\)-\(\varepsilon\)) curves of the coal specimens under the varying cyclic loading-unloading cycles and confining pressures, with representative curves illustrated in Figure 4. As shown in Figures 3 and 4, under quasi-static cyclic loading-unloading conditions, the axial stress-strain curves can be broadly categorized into four stages: compaction (I), linear elasticity (II), nonlinear crack propagation (III), and post-peak softening (IV). In the quasi-static loading curve of coal (Figure 4a), only compaction stage I was observed during the first two cycles. As the number of cycles increased, the peak stress gradually rose, and the stress-strain curves progressively extended into stage II, as showed in cycles 3-5 in Figure 4a. At the sixth cycle, the \(\sigma\)-\(\varepsilon\) curve reached stage III, approaching the maximum axial stress the material could sustain. During the seventh loading cycle, brittle failure occurred, marked by a sharp stress drop, corresponding to stage IV. Notably, the number of cycles in stage I remained consistent across the different confining pressures. In contrast, stage II exhibited an increasing number of cycles, manifested by denser \(\sigma\)-\(\varepsilon\) curves. The cycle count in stage III showed a gradual reduction with increasing confining pressure, indicating the prolongation effect of confining pressure on the failure phase (stage III). As shown in Figure 4b, under dynamic loading conditions, the axial stress-strain curves differed significantly from those under quasi-static conditions, which were divided into five stages: compaction (I), near-linear elasticity (II), nonlinear crack propagation (III), first unloading process (IV), and second unloading process (V) [31]. The critical distinction lay in the post-peak phase bifurcation into stages IV and V. Stage IV corresponds to the growth of plastic strain, with confining pressure exerting a negligible influence. Stage V represents the rebound deformation phase, characterized by elastic strain release. Elevated confining pressures enhanced the magnitude of stage V, demonstrating stronger rebound effects, which suggests that confining pressure promotes the accumulation of internal elastic strain in coal, thereby inducing greater post-peak elastic energy release [32].
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 3 Voice Maps in Practice
### Mapping Effects of Interventions
Here, green means an increase and red means a decrease, in this case in \(Q_{\text{ci}}\). We see that \(Q_{\text{ci}}\) mostly decreased (red) except on the loudest tones around 220 Hz (green). Returning to the analogy with a landscape, we see that while the overall topography pre and post is similar, the elevation has changed somewhat, and differently so in different places. The circumstance that many adjacent cells show the same direction of change gives us confidence that this is a systematic effect. Again, this effect would have been very difficult to demonstrate convincingly with just a few sustained vowels. They would have been vulnerable both to elicitation noise and to the small fluctuations from various incidental sources of variation in the voice production mechanism, as mentioned in the introduction. By sampling this mechanism over a dense grid across SPL and \(f_{\text{o}}\), the mapping operation helps to expose trends in what are inherently rather noisy data. Figure 6: Top row: maps of the EGG contact quotient \(Q_{\text{ci}}\) (**a**) pre-intervention, (**b**) post-intervention and (**c**) of the difference, to facilitate a comparison across the intervention. The horizontal color bars show the color scale for \(Q_{\text{ci}}\) (left, center) or for the change in \(Q_{\text{ci}}\) (right). In (**c**), the color red signifies a reduction in \(Q_{\text{ci}}\) after the intervention. Bottom row: the same data, after 2D interpolation and smoothing with a convolution kernel of \(3\times 3\) cells. Interpolation into empty cells is done only if there are non-empty neighbouring cells on opposing sides or corners. Although this does not add more information, the graph is easier to interpret, and the difference map (**f**) from the two interpolated maps (**d**,**e**) is less vulnerable to missing data in either of (**a**) or (**b**). Data adapted from [44]. The issue now becomes to develop and validate a methodology for using difference maps such as these, since they appear to hold promise for making clinical assessments. In particular, more applied experience will be needed in order to establish the effect sizes needed to indicate clinically relevant changes, in any given metric. This is of course always the case, also for more conventional assessment methods, but mapping shows us that it might not be meaningful to reduce such effect sizes to a single number, for comparison with a given threshold. An intervention is more likely than not to have different effects in different parts of the range of the voice. One may hypothesize that interventions should result in features on the voice map changing position, for instance a lower SPL for the threshold of vocal fold collision in soft voice, or a raised SPL for the onset of pressed phonation in loud voice. New techniques for assessing temporal changes in scalar fields (such as pre-post intervention) have recently been presented by [PERSON] et al. [45], and we plan to test these techniques in future studies.
# Speaker recognition Based on Dunge Beetle Optimized CNN
## 4 Experiments and Results
### Recognition Performance  
The CNNs using the above four sets of hyperparameters are constructed, and the produced spectrograms are imported into them for training. The loss function is SoftMax-CrossEntropy. The number of iterations is set to 50.  
As shown in Figure 6, by 50 iterations on two datasets, the loss of traditional CNN will not decrease to below 0.01, which is the worst among the models. When tested on the AI-shell dataset, DBO-CNN performs the best by reducing losses to below 0.01 after  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**CNN**} & \multicolumn{2}{c}{**PSO-CNN**} & \multicolumn{2}{c}{**SSA-CNN**} & \multicolumn{2}{c}{**DBO-CNN**} \\ \cline{2-9}  & **AI-Shell** & **VTCK** & **AI-Shell** & **VTCK** & **AI-Shell** & **VTCK** & **AI-Shell** & **VTCK** \\ \hline preprocessing layer & 32 & 32 & 24 & 30 & 27 & 19 & 21 & 32 \\ residual block 1 & 64 & 64 & 32 & 49 & 54 & 51 & 63 & 43 \\ residual block 2 & 128 & 128 & 117 & 92 & 107 & 75 & 71 & 127 \\ residual block 3 & 256 & 256 & 230 & 239 & 143 & 135 & 256 & 201 \\ residual block 4 & 512 & 512 & 386 & 504 & 424 & 278 & 272 & 256 \\ residual block 5 & 1024 & 1024 & 424 & 997 & 904 & 555 & 653 & 512 \\ extra linear layer & 512 & 512 & 272 & 403 & 394 & 352 & 259 & 512 \\ learning rate & \(1\times 10^{-2}\) & \(1\times 10^{-2}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters searched by different models.  
Figure 5: The process for recognizing speakers by using the optimized DBO.  
only 5 iterations, PSO-CNN spends 25 iterations, and SSA-CNN spends 21 iterations. It is indicated that DBO significantly improves the training speed of the model. When tested on the VTCK dataset, DBO-CNN also showed the best convergence speed. The evaluation results of the four models on the test set are shown in Figure 7. After the optimization of the intelligent algorithm, the accuracy of the model has been significantly improved. Among them, DBO-CNN has the greatest improvement with an average accuracy rate of 97.93%, followed by SSA-CNN with an accuracy rate of 96.71%, and finally, PSO-CNN with an accuracy rate of 95.72%. It is demonstrated that DBO is indeed superior to traditional intelligent algorithms such as PSO.
# Multi-Hop Question Generation with Knowledge Graph-Enhanced Language Model
## 4 Results
### Baselines and Ablation Settings  
In our comparative evaluation, we consider the several state-of-the-art models. Seq2 Seq with Attention (Seq2 Seq+Attn): a classic sequence-to-sequence architecture incorporating an attention mechanism for mapping passages to questions [30]. NQG++: an enhancement of the seq2 seq model that includes an answer-aware input representation encoder incorporating answer position, POS, and NER information, which enables the generation of answer-focused questions [19]. Answer-Separated Seq2 Seq (ASs2s): a decoder of a question from an answer-separated passage encoder using a keyword net-based answer encoder to better capture key information in the target answer and generate appropriate questions [31]. S2 sa-at-mp-gsa: an enhanced seq2 seq model incorporating gated self-attention and maxout-pointers to address the challenge of processing long sentences in question generation (QG) [6]. GPT-2: a self-attention-based generation model capable of tackling tasks such as QA, machine translation, reading comprehension, and summarization. This model serves as a baseline for comparison with the proposed model [14].  
Ablation settings for KGEL are defined as following. KGEL, which is the proposed knowledge graph-enhanced language model. KGEL-AT, which is KGEL without answer tagging (AT); it is the bi-attention component in Figure 2. KGEL-MHAR, which is KGEL without MHAR; it is the multi-head attention reasoning component in Figure 2. KGEL-EL, which is KGEL without evidence locating (EL); it is the answer-aware GAT in Figure 2.  
It should be emphasized that the ablation experiment most relevant to this paper is KGEL-EL. We exclude the entire graph neural network, which is equivalent to the model we proposed without its key component: utilizing the graph to incorporate entity information. The model we employ here is a GPT-2 network with an augmented number of model layers to ensure a fair comparison, given that our network has more layers than GPT-2. It is important to note that our enhancement is not solely based on the inclusion of an extra layer, but rather the GNN has showcased an impressive capacity to merge logical reasoning.  
**Implementation Details**. In the evaluation of the proposed KGEL model, open-source implementations of ASs2s and CGC-QG are employed on the HotpotQA dataset. The NQG++, S2 sa-at-mp-gsa, and CGC-QG models utilize a 1-layer GRU as both the context encoder and question decoder, each with 512 hidden units. The word representations are 300-dimensional GloVe pre-trained embeddings. The baselines are optimized using Adam with a mini-batch size of 32. For the KGEL model, AdamW is used as the optimizer with a mini-batch size of 10 and a learning rate of from \(3\times 10^{-3}\) to \(4\times 10^{-3}\). The warm-up steps occupy 10% of the total training steps. The graph module of the KGEL model has two layers. The remaining experimental settings are kept consistent with the configuration in [PERSON] et al. [14]. Hyperparameters of KGEL are listed in Table 1.
# A Simple AMC Antenna for Liquid Monitoring in an Infusion Bag
## 5 Antenna for Hospital Application
The noise power \(P_{n}\) is calculated to be \(8.28\times 10^{-14}\) W. From Equation (2), the output signal-to-noise ratio (SNR) is obtained as 32.1 dB. When using QPSK modulation, the link margin of the communication system is 7.45 dB, demonstrating the feasibility of integrating the proposed antenna with the existing hospital communication system to enable indoor Wi-Fi network communication. Power consumption is a critical factor in the design of the antenna, as it is intended for the continuous monitoring of infusion bags without frequent battery replacement. Therefore, this study provides an analysis of the antenna's power consumption. The primary contributor to power consumption in the communication link is the RF transmitter, which generates the transmitted signal. Given a transmission power of 10 mW and an antenna efficiency of 0.98, the required input power for the signal generator is 10.2 mW. Additionally, the energy losses within the signal generator must be considered. A low-power signal generator, typically consuming 5 mW, is used in the transmission system, resulting in a total power consumption of 15.2 mW. For practical deployment, a compact 3.7 V/500 mAh battery is employed. By implementing a 10% duty cycle mode for signal transmission, the estimated battery life extends to 1217.1 h (approximately 50.7 days). Under normal operating conditions, the antenna sensor does not require frequent battery replacement, making it a highly convenient and efficient solution.
# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 3 The Proposed Method  
### Framework  
Figure 1 shows the framework of the proposed person search method for surveillance videos. This framework consists of three components: spatial feature extraction, spatio-temporal feature generation, and feature indexing. First, a surveillance video consists of continuous frames, which contain information about many pedestrians. Each frame is fed into Yolo network to detect the bounding boxes of persons, and the corresponding spatial features are extracted by Resnet-50 model for each person. After that, the process of person search is not affected by surveillance background. Next, to explore the temporal relationship of persons between adjacent frames, the spatial features of each person are successively imported into Gated Recurrent Unit (GRU). The hidden state generated by  
GRU is used to calculate the temporal correlation of a person between adjacent frames. Since each spatial feature corresponds to a hidden state, it is not feasible to find the target person by directly computing the similarity between a large number of hidden state vectors. To solve this problem, an average pooling layer is added to the output of the GRU. A single spatio-temporal feature is generated by averaging the hidden state sequence for each person, and the computational cost is reduced in the search process. After generating spatio-temporal features, the spatial features within each frame and the temporal relationship between adjacent frames are fully exploited. Finally, due to a large number of video clips in the database, all the spatio-temporal features are organized by Locality Sensitive Hashing (LSH), thereby ensuring the efficiency of person search. The similarity between a target person and persons in the database is computed through LSH, and the persons with the greatest similarity are returned as the search results.
###### Abstract  
In an airborne passive radar, multipath (MP) clutter, which is caused by MP signals contained in the contaminated reference signal, degrades the space-time adaptive processing (STAP) performance. The MP clutter suppression algorithm before STAP can mitigate the influence of impure reference signals. However, the performances of the existing MP clutter suppression methods deteriorate when the intrinsic clutter motion (ICM) exists because the sparse model of MP clutter is disturbed. To eliminate the impacts of ICM on MP clutter suppression, a joint optimization algorithm is developed for airborne passive radar. Firstly, the sparse model of MP clutter is modified by taking ICM fluctuation into account. Subsequently, the joint optimization function of the ICM fluctuation and MP clutter profile is derived. Finally, based on the local search technique, MP clutter is suppressed with ICM error calibration and off-grid effects mitigation. A range of simulations verify the reliability and superiority of the proposed method.  
intrinsic clutter motion; joint optimization problem; space-time adaptive processing (STAP); airborne passive radar; reference signal +
Footnote †: journal: Applied  
Article  
Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars. Appl. Sci.