esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Proposed No-Reference Depth Map Quality Evaluation Model
#### 3.1.2 DEC Measurement-Based No-Reference Quality Evaluation Model  
The proposed DEC measurement-based quality evaluation model has been developed using a curve-fitting operation. The target of this operation is to exhaustively search and find a mathematical model that can best represent the mean opinion score (MOS) values recorded for the rendered views during subjective testing with respect to the obtained DEC measurement of the depth maps used in rendering those views.  
For the purpose of developing this mathematical representation, the 2D video subjective assessment results presented in Section 4.1 are utilised. The 2D MOS values are divided equally (at a 50%-50% fraction) into two sets: A training, and a testing set, respectively. The aim of dividing these MOS results into two halves is twofold: First to utilise the training set to develop the proposed quality evaluation model through dataset training on one half of the dataset only, and then to independently employ the testing set to assess the performance of the established model on the remaining half. In turn, this ensures obtaining results with dataset independence. The mean value (measured over the total number of frames per video test sequence) was calculated with respect to the DEC measure for each of the corresponding depth maps.  
Several mathematical models were examined by performing the curve-fitting process to identify the best matching mathematical relation between the 2D MOS results with the equivalent mean DEC measurement values. The results were classified in terms of correlation coefficient (CC), and root mean square error (RMSE) values. The curve-fitting operation was performed with the prediction bounds set within a 95% confidence interval. From the several curve-fitting steps tested, the top-performing equation was selected for constructing the DEC measurement-based model, and the constant parameters of the model were tuned to produce the maximum correlation for the training set. The resulting graph for the selected model obtained from the curve-fitting process is depicted in Figure 5.  
The resulting model from the curve-fitting process between the 2D MOS and corresponding mean DEC measurement values is represented by the following equation:  
\[Y=a/(X^{2}+bX+c), \tag{1}\]  
Figure 5: 2D MOS vs. mean DEC curve-fitting results for building the quality evaluation model.  
where \(Y\) is the model's output MOS value for the rendered view; \(X\) is the mean DEC value for the depth map used in rendering the view; and \(a\), \(b\) and \(c\) are the constant coefficients equal to 0.85, 1.544 and 1, respectively.
# Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety
## 3 Experiment of the Proximity Warning System Based on Bluetooth Beacon and Smart Helmet  
### Performance Evaluation of Personal PWS Based on Smart Helmets  
To evaluate the performance of the developed smart helmet-based personal PWS, a field experiment was conducted at the Sungshin Minefield underground limestone mine (37\({}^{\circ}\)17\({}^{\prime}\)12\({}^{\prime}\) N, 128\({}^{\circ}\)43\({}^{\prime}\)53\({}^{\prime\prime}\) E) located in Jeongseon-gun, Gangwon-do, Korea. Figure 6 shows the tunnels that have been tested in the field on a two-dimensional and three-dimensional map and an actual photograph. As shown in Figure 2, a Bluetooth beacon was attached to the back of the room mirror in front of the truck and the front of the heavy equipment, and the workers wore a smart helmet programmed with a personal PWS system. A Bluetooth module capable of receiving BLE signals is placed at the back of the helmet to recognize the proximity of equipment outside the worker's view. A field experiment was conducted to measure the detection distance of the BLE signal received by the smart helmet by adjusting the Tx power of the Bluetooth beacon. The angle between the Bluetooth beacon and the smart helmet was adjusted to measure the detection distance of the smart helmet receiving the BLE signal.  
Figure 5: The process of the operating algorithm for the smart helmet-based personal proximity warning system.  
Figure 7a shows the smart helmet measuring the detection distance of receiving the BLE signal for each Tx power. The Bluetooth module that receives the BLE signal was installed at the rear of the helmet, and the Bluetooth module and Bluetooth beacon attached to the vehicle were arranged to face each other. The Bluetooth beacon, attached to the truck, approached a pedestrian standing on a mineway transport route 100 m away at a speed of 10-20 km/h. We then measured the detection distance at which the personal PWS receiving the BLE signal began warning pedestrians. The Tx power was set at 4 dBm intervals--from \(-12\) dBm to 4 dBm--and measured 10 times for each Tx rower (50 times total).  
Figure 7b shows an experiment that measures the detection distance of a smart helmet receiving a BLE signal via adjusting the angle between the Bluetooth beacon and the smart helmet. Similar to the above experiment, the truck approached at speeds of 10-20 km/h, and the detection distance at which the warning commenced was measured. The angles between the smart helmets and beacons were set at 45\({}^{\circ}\) intervals--from 0\({}^{\circ}\) to 180\({}^{\circ}\), and measured 10 times for each angle (50 measurements in total).  
Figure 6: Underground map of the study area (Sungshin Minefield underground limestone mine, Jeongsun-gun, Gangwon-do, Korea) in 2- and 3-dimensions, and an actual photograph.  
Figure 7: _Cont._
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
## 8 Discussion  
In the future, we plan to investigate the actual implementation of the proposed PMLS by using embedded devices such as Raspberry Pi or Arduino. For this practical implementation, we will use an open NDN protocol stack called Named Data Networking Forwarding Daemon (NFD). However, since NFD is implemented as a general computer target, additional porting work will be required to operate NFD in the above embedded devices. In addition, we will apply the proposed producer mobility link service to the NDN protocol stack. Through this practical implementation, we will verify whether the proposed PMLS is applicable in real environments. In addition, we plan to research a solution that can support producer mobility more robustly even when mobile producers move very quickly and randomly and to prove detailed performance analysis with existing anchorless methods.
# Detachable Soft Actuators with Tunable Stiffness Based on Wire Jamming
## Materials and Methods
### Modeling  
In this section, the stiffness characteristics of the wire jamming structure are further analyzed using theoretical models. The wire jamming structure consists of an airtight envelope, a vacuum line and wires. The principle of operation is to transform wires from an unjammed state to a jammed state by changing the vacuum pressure inside the envelope. The friction between wires is varied by the vacuum pressure, thus achieving a tunable stiffness (Figure 2i). We present the three most common states, where \(F_{1}\) is the applied force in normal working state, while \(F_{2}\) and \(T_{1}\) are often found in the applications of robotic grippers and wearable exoskeletons, such as grasping irregular objects or joint rotation (Figure 2j) [17; 22; 23]. The sliding between wires is relatively stable under \(F_{1}\) and \(F_{2}\), but the rotation under torque \(T_{1}\) complicates the contact surface between wires. Additionally, these three states reflect the three rotational degrees of freedom (DOF) at the free end of the wire jamming structure, the combination of which allows for a tunable stiffness in three dimensions.  
In practice, the main bending state of the wire jamming mechanism can be referred to as a cantilever beam, where one end is fixed to the platform and the other end is under the force \(F_{\text{e}}\). As shown in Figure 2k, \(n_{1}\) and \(n_{2}\) are the numbers of wires in thickness and width, respectively. \(a\) is the distance from applied force \(F_{\text{e}}\) to the fixed platform, and \(v\) is the deflection under the applied force \(F_{\text{e}}\). Since the gap between wires cannot be neglected, we assume that each wire acts as an independent beam when the vacuum is off. The wires enter the jammed state after the vacuum is on. Thus, these wires are analyzed as a single beam. According to the Euler-Bernoulli equation for the cantilever beam, we can obtain a theoretical model (see Section S4 and Figure S7 in the Supplementary Materials for details). The stiffness of the wire jamming structure is related to the number of wires, the diameter of a single wire and the material properties. Moreover, it can be found that stiffness increases remarkably with the increase of \(n_{1}\). In order to quantify the effect of geometric parameters on stiffness performance, as well as to address the assembly issues with the fiber-reinforced soft actuator, we determined the dimensional parameters of the wire jamming structure and the number of wires inside the airtight envelope (see Section S5 and Figure S8 in the Supplementary Materials for details).
###### Abstract
However, several lines of evidence indicate that DksA and (p)ppGpp actions could be independent or even antagonistic [13; 14; 15; 16]. DksA is involved in the regulation of transcription initiating from \(\sigma^{70}\)-dependent promoters, as well as in \(\sigma^{54}\)-driven transcription [17]. DksA is defined as a global transcription regulator, interacting with the RNA polymerase via its secondary channel [18]. The role of DksA in some Gram-negative bacteria has been described and the presence of _dksA_ homologs has been reported in many proteobacteria [19; 20]. Mutants lacking _dksA_ exhibit pleiotropic defects, e.g., in cell growth and division, stringent and stress response, quorum sensing, and DNA repair [19; 21; 22; 23], indicating its important role in numerous cellular processes. A thorough transcription analysis aiming at DksA function was performed e.g., using microarrays to compare (p)ppGpp and DksA-deficiency in _E. coli_[14], transcriptomic analysis in _Pseudomonas aeruginosa_[24], _Borrelia burgdorferi_[25], or _Haemophilus ducreyi_[26]. The virus-host interaction requires a complex interplay between two opposite targets: phage strategy of reprogramming the host machinery to serve its purposes of propagation, and host cell defense against the infection [27]. Thus, at the molecular level, a complicated arrangement of gene expression alterations takes place, the process that can be studied employing various methods, e.g., computational approaches or Raman tweezers [28; 29]. In this work, we attempted to resolve the phenomenon of improved lytic development of P1 phage in the DksA-deficient _E. coli_ host by using a high-throughput approach to obtain a general view on the phage-host interaction. Here, we present the second part of our scientific story which is still getting _curiouser and curiouser_, which makes the reader amazed and brings to mind \"_[PERSON]_'s _Adventures in Wonderland_\" by [PERSON]. We would like to encourage the reader to follow the beginning of this story in our accompanying paper: _Virus-Host Interaction Gets Curiouser and Curiouser. PART I: Phage P1 vir Enhanced Development in an E. coli DksA-Deficient Cell_[30].
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 7 Conclusions  
In this paper, LoRaWAPS, a low-cost and low-power outdoor positioning system with multi-anchor wireless mesh networking and multi-dimensional data fusion is designed based on LoRa technology. A hardware platform for LoRaWAPS is designed and built, based on which, a low-power on-demand routing protocol is designed to realize mesh networking between nodes, and compared with other wireless mesh protocols, the route discovery and route maintenance process of LoRa Mesh protocol designed in this paper is simple and more energy-efficient. Furthermore, a distance estimation algorithm for TOF and RSSI data fusion and a position estimation algorithm for minimizing the posterior of RSSI errors are designed for LoRaWAPS. The experimental results show that the designed outdoor positioning system based on LoRa Mesh utilizing low-cost and low-power hardware can achieve high-precision positioning through the wireless mesh, enabling multiple anchor points to work together and reducing the interference caused by multipath effects, making it convenient for applications such as outdoor warehouse goods tracking, personnel management, and remote grazing in emerging IoT scenarios.  
However, due to the increasingly complex and dynamic outdoor environment and the limited frequency band resources, the current systems cannot meet the actual needs of future outdoor wide-area positioning with low power consumption and low cost. In future, we will design the positioning system by improving anchor point structures with integrated multiple antenna technologies for angle and distance measurements in stereoscopic scenes, realize multi-target real-time localization with limited spectrum resources by efficiently allocating spectrum resources and avoiding interference among users, and achieve indoor and outdoor fusion positioning by designing indoor-outdoor scene switching and positioning data fusion algorithms.  
Methodology, B.L. and Y.X.; Software, Y.X.; Validation, B.L. and Y.X.; Investigation, B.L., Y.X. and Y.L.; Writing--original draft, Y.X.; Writing--review & editing, Y.L.; Supervision, Z.S.; Funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT2023B25) and the National Natural Science Foundation of China (No. U21A20456).  
Figure 14: Localization system test: (**a**) Normal case, 5 m \(<\) positioning error \(<\) 15 m; (**b**) Good case, positioning error \(<\) 5 m; (**c**) Bad case, positioning error \(>\) 15 m.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 3 Measurement Results and Discussion
### Experiments under Cloudy Weather Conditions  
#### 3.3.1 Measurement Location and Environmental Conditions  
Lastly, cloudy days were selected as the target urban environment, mainly at the Imperial Palace, and measurements were taken during October. A cloudy day was defined as a day when the weather at the time of measurement in Tokyo was cloudy, according to meteorological data from the Japan Meteorological Agency [35]. The weather data are shown in Table 3.
## 4 Classification of Routing Protocols
### Cluster-Based Routing Protocols  
The third class of WBSNs routing protocols is cluster-based routing protocols, where the bio-medical sensor nodes or sensing nodes are divided into one or more clusters. Different methods are used to select one of nodes of the specific cluster, as a Cluster Head (CH) and the data is send to the sink (base station) through these CHs to reduce the direct communication between the sensing nodes and the sink. The following sections give the overview of some of the important cluster-based routing protocols for WBSNs.
## 4 Results and Discussion
### SC Type 0  
The electrical spectrum of SCs type 0 \(n=72\) NR (50 MHz) channels at the input of RX DSP obtained by FDMA and TDMA aggregation is shown in Figure 4. The red line is used for FDMA and the blue line is used for TDMA. The total bandwidth occupied by FDMA is obtained as \(n\times 61.44\) MHz = -4700 MHz (including 300 MHz guard from DC), where 61.44 MHz is the sampling rate of each NR channel. Conversely, the total bandwidth occupied by TDMA aggregation is \(n\times 50\) MHz = -3500 MHz, where 50 MHz is the bandwidth of each NR channel. For the same number of channel aggregation, TDMA saves \(\sim\)1 GHz bandwidth when compared with FDMA. The spectral roll off towards higher frequency is due to the frequency response of the system. In the case of FDMA, the performance degradation of higher channels is pre-compensated by the pre-emphasis block shown in Figure 2.  
Figure 3: Time division multiple access (TDMA) TX/RX DSP block scheme.  
Before the analysis of the EVM performance of the system, we first optimize the signal clipping ratio. The clipping technique is implemented to reduce the high peak-to-average-power ratio (PAPR) associated with OFDM modulation. A small clipping ratio gives rise to a large clipping distortion, whereas large values of clipping ratio reduce clipping distortion, but generate a small signal, which is less resilient to the receiver noise. Therefore, there is an optimum clipping value. The clipping ratio versus EVM (computed as the mean value of the EVM of all channels) for both PUCCH and PUSCH obtained at 29 dB ODN loss (a typical target value for PON access networks) is shown in Figure 5a,b, respectively. The optimum clipping ratio for both signal types and DSP aggregation techniques is 12 dB.  
Figure 4: Power spectral density (PSD) of aggregated 72 NR (50 MHZ) SCs type 0 channels at the receiver DSP input obtained at 20 dB optical distribution network (ODN) loss.  
Figure 5: Clipping optimization for 72 NR (50 MHz) channels at 29 dB ODN loss. (**a**) Physical uplink control channel (PUCCH); (**b**) physical uplink shared channel (PUSCH). EVM—error vector magnitude.  
The EVM (mean) as a function of the ODN loss, evaluated at the optimum clipping for PUCCH and PUSCH, is then reported in Figure 6a,b, respectively. The mean EVM performance of the TDMA scheme is slightly better for both signal types (PUCCH and PUSCH). This is associated with TDMA's spectral compactness when compared with FDMA, which is less affected by the optical channel response towards the higher channels.
## 7 Conclusions  
In summary, we proposed a hierarchical federated-learning framework designed to address the challenges posed by heterogeneous IoT environments, focusing on improving communication efficiency and ensuring data security through lightweight encryption. By employing hierarchical aggregation, asynchronous communication, and lightweight stream encryption, our framework achieved a significant reduction in communication cost compared to the traditional HierFAVG, with an average reduction of approximately 20% in round-time cost. Additionally, the use of SALSA encryption provided data security with minimal computational overhead, averaging 0.8 s per round, demonstrating its feasibility for resource-constrained devices. Despite the presence of hardware and network heterogeneity, our experimental results showed that the proposed framework achieved comparable model accuracy to HierFAVG, with a final accuracy difference of only 0.4%. These findings indicate that our framework provides an efficient, secure, and scalable solution for federated learning in dynamic IoT environments. Based on this, our future work will focus on extending the evaluation to larger-scale IoT networks, incorporating hundreds or thousands of devices, to analyze the latency and scalability in such environments. Furthermore, adaptive privacy-preserving mechanisms, such as secure aggregation protocols and dynamic encryption schemes, will be explored to address heightened privacy concerns in real-world deployments.  
[PERSON] performed formal analysis, methodology, software, original draft, and review; [PERSON] and [PERSON] performed conceptualization, investigation, and data curation; [PERSON] performed visualization, validation, and draft review and editing; [PERSON] performed resources, conceptualization, and draft review and editing; [PERSON] performed supervision, draft review, and funding acquisition. All authors have read and agreed to the published version of the manuscript. This research was funded by JSPS Grant-in-Aid for Scientific Research (C) 23K11103.  
The data used in this study are publicly available datasets. The HAR dataset is available at [[https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones](https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones)]([https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones](https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones)) (accessed on 10 September 2024). We would like to thank [PERSON] (Guangxi Academy of Sciences) for his valuable contributions in reviewing the manuscript and providing insightful comments that greatly improved the Introduction and Related Work sections. The authors declare no conflicts of interest.
## 3 Preliminary Knowledge  
The process of quantization is the process of converting floating-point numbers into fixed-point numbers, and it is also the process of changing from continuous values to discrete values. The quantization methods are generally divided into training-aware quantization and post-training quantization, as shown in Figure 1a,b, and Figure 1c briefly illustrates the clipping-based post-training quantization method that we propose.  
The basic ideas of the three strategies in Figure 1 are as follows.  
1. [label=()]
2. Training quantization: The model is directly quantized, and the input and weights are converted to 8-bit numbers during model training. At last, inverse quantization is performed during model inference.
3. Post-training quantization: The model is still trained with 32-bit floating point numbers, and the weight distribution is obtained to derive the quantization parameters, and finally the quantized model is deployed in the inference stage.
4. Clipping-based post-training 8-bit quantization: The weights and inputs are clipped before training so that they are trained as 32-bit fixed-point numbers, and then the quantization model is deployed to the inference stage.  
This is only a brief description of the method. The details of the operational mechanism, formulae derivation, and strengths and weaknesses analysis will be described later in Sections 3.1, 3.2, and 4.1.  
Next, we analyze the defects in training-aware quantization and post-training quantization methods.  
Figure 1: Demonstration of training-aware quantization, post-training quantization, and clipping-based post-training 8-bit quantization. (**a**) Training-aware quantization. Both input and weights are quantized and the model is trained the quantized to 8-bit number. (**b**) Post-training quantization. The input and weights are pseudo-quantized to 32-bit integer for training and further quantified after training. (**c**) Clipping-based post-training 8-bit quantization. The input and weights are pseudo-quantized and clipped to a predesigned range, and then further quantified after training.