esa-sceva/satcom-chunk-collection huggingface dataset:
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
###### Abstract
It achieves a higher channel capacity than using the common receiving antenna allocation for all subcarriers because of the spatial diversity gain. The proposed scheme uses the limited CSI feedback sequence based on [20] and the receiving antenna decision method based on [21]. First, in this sequence, each STA uses the training preamble transmitted from the AP to estimate channel responses as the CSI of all receiving antennas, and then feeds back to the AP just the response of the antenna, with the maximum norm assuming that the spatial correlation value between receiving antennas is higher than a threshold. Otherwise, each STA feeds full channel responses back to the AP. By using this approach, the amount of CSI feedback is decreased with only a small performance penalty. Moreover, the computational complexity incurred by the receiving antenna allocation at the AP is also decreased. The receiving antenna decision method also decreases the overhead associated with notifying each STA of the allocated antenna information; each STA estimates the allocated antenna by a power comparison of the receiving antennas at each STA. Moreover, the proposed method considers the antenna decision error to improve the accuracy of the antenna decision. To clarify the effectiveness of the proposed scheme, we have measured the channel responses between AP and STAs in an indoor school environment and then analyzed the measured results. We have evaluated it by computer simulations using measured channel responses. The results show that the proposed scheme maintains the channel capacity of the downlink MU-MIMO-OFDM transmission with a greatly reduced overhead and computational complexity. Moreover, we show that the proposed method offers high accuracy in the antenna decision. This paper is organized as follows: Section 2 presents a system model of the downlink MU-MIMO-OFDM transmission with the receiving antenna allocation; Section 3 describes the proposed scheme, including the limited CSI feedback sequence and the receiving antenna decision method; simulations using the measured results are shown in Section 4; and, finally, in Section 5, the paper is concluded.
## 4 Methodology
### Data Preprocessing  
In general, the acquired raw dataset contains unwanted noise, which may be introduced by either external vibration or movement of the sensor against the body due to loose coupling. Essentially, all measured body movements are contained within frequency components below 20 Hz [35], thus, we use a third-order low-pass Butterworth filter to remove the high frequency noise with cutoff frequency at 20 Hz. Afterwards, the dataset is filtered by a third-order median filter to remove abnormal noise spikes.
## 3 Materials and Methods
### Automated Detection
#### 3.3.2 Baseline  
The automated inspection of the assembled vehicle is performed by an ad hoc hierarchical deep learning-based architecture introduced in [10] and improved upon in [30]. It is represented at the top of Figure 3: in a first stage, Detectron2 [39] performs object detection, considering macro categories, i.e., the location (front, back, left and right) and part (e.g., mirror, light, etc.) codes. Each detected object is cropped and given to the CBIR module of its detected category. In this second stage, a feature extraction network (ResNeXt-50 32x4d [40] pretrained on ImageNet) obtains a latent representation of the cropped object, which is compared via Euclidean distance to the representations of all the training set objects of that same macro class. Then, the part codes of the top-3 instances with the lowest distances are voted to obtain the predicted code of the object. This code now includes the brand, model and material information for each object (e.g., the light of a commercial Peugeot). After all images of a vehicle are processed, a list of the detected parts is generated for comparison with the corresponding production list.  
The hierarchical nature of the system is based on the premise that new vehicle components are far less likely to be introduced than new versions of existing components. As such, this two-stage approach allows increased scalability and versatility since introducing a new version of a given part (e.g., another version of a tire cover) only implies adding (unlabeled) examples to the corresponding CBIR database and avoids retraining the whole object detector from scratch.  
Figure 3: Overall architecture of the hierarchical automated detection module (**top**) and its iterative training process (**bottom**). First, an object detection neural network identifies vehicle parts (e.g., left rear view mirror) and each object is forwarded to the corresponding retrieval sub-module, responsible for outputting the final code for each part (e.g., left rear view mirror of a commercial Peugoot). The training process can be viewed as a semi-supervised object detection pipeline with 3 main stages: pretraining on labeled simulated images which are later converted into more realistic versions, which, in turn, are used to fine-tune the detector. Training is completed by combining both labeled converted images with pseudo labeled real images, in a teacher-student framework with Exponential Moving Average (EMA) updates of the teacher. The dashed arrows represent the weight transfer between the models obtained at each stage.
# Mechanical Properties of Cement Concrete with Waste Rubber Powder
## 3 Results and Discussion
### Influence of Waste Rubber Powder Concrete Composition Materials  
The response surface analysis elucidated the response behavior concerning variable alterations [29]. The 7-day and 28-day flexural strength were scrutinized concerning variations in the input variables, namely the water-cement ratio (A), sand ratio (B), and waste rubber powder content (C). Figures 13-15 depict the response surface plots delineating the concrete strength derived from the multivariate regression equations (Formulas (4) and (5)). These plots facilitated discerning the influence of any two factors on the 7-day and 28-day flexural strength, thereby identifying the optimal range of component materials based on these strengths.  
Figures 13 and 14 indicate that as the water-cement ratio decreased, there was a steady rise in both the 7-day and 28-day flexural strengths of the cement concrete. Meanwhile, adjustments in the sand ratio had a limited impact on these strengths, with a tendency to initially rise before declining.  
According to Figures 15 and 16, the 7-day and 28-day flexural strengths of cement concrete decreased progressively with an escalation in the waste rubber powder content. This trend stemmed from the compromised interfacial bonding between the cementitious elements induced by the presence of the waste rubber powder and the consequent reduction in flexural strength.  
The data presented in Figures 17 and 18 indicate that changes in sand ratio minimally affected the 7-day and 28-day flexural strengths. In contrast, an increase in waste rubber powder content resulted in a gradual decrease in both the 7-day and 28-day flexural strengths of the cement concrete. Notably, among the three influencing factors, the water-cement ratio demonstrated the most significant impact on flexural strength, while the sand ratio exhibited the least effect.
## 2 Related Work
### HF-Based Data Integrity  
The authors of [30] propose a scheme to protect the identity privacy of IoT users by preventing identity leakage in transactions. The study uses the ring signature method to obscure the real identity of the user who proposes the transaction, and the aggregated signature method to shorten the time and space required for k signature verification to 1/k, improving the efficiency of the system. The correctness and efficiency of the scheme are also proved through theoretical analysis and experiments.  
[PERSON] et al. in [31], present a proposed decentralized data integrity auditing scheme, called HF-Audit, that uses HF to establish two separate communication channels for User-TPA(third-party auditor)-CSP(cloud service provider). HF is chosen as the communication platform in HF-Audit because of its attributes such as tamper-proofing, access permission, anonymity, efficient processing, and private channel features.  
The authors of [32] propose a secure data transfer scheme based on Hyperledger Fabric blockchain for enterprise data sharing in the Industrial Internet of Things (IIoT). The raw data collected by enterprises is encrypted and stored in the InterPlanetary File System (IPFS) network, while the keyword-index table is designed in HF blockchain to enable data sharing among enterprises. The framework comprises three layers: Hyperledger Network Layer, Client Layer, and Storage Layer. The proposed scheme constitutes a significant contribution to secure data sharing, privacy protection, and scalability in IIoT's applications.  
The aforementioned related work is summarized in Table 1.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 2 Literature Review
### Proportionally Fair (PF) Scheduling  
PF scheduling is a channel-aware-scheduling technique which aims to strike a balance between ensuring throughput and fairness [14]. The prioritization coefficient is \(P_{c}(t)\) where \(P_{c}(t)=\
icefrac{{1}}{{\mu_{p}(t)}}\) is given to every MS based on their anticipated resource consumption. The channel-state information short-term average evolution is \(\mu_{p}(t)\). It is obtained using the moving average over a window \(W\).  
\[\mu_{p}(t)=\mu_{p}(t-1)\biggl{(}1-\frac{1}{W}\biggr{)}+\sum_{l\in B_{Z}}\ \iota_{p,l}(t)\frac{W_{p,\ l}^{Z}(t)}{W} \tag{9}\]  
\(\iota_{p,l}(t)\) shows if MS \(q\) in scheduling period \(t\) during \(n\)th subcarrier can communicate, i.e.,  
\[\iota_{q,n}(t)=\left\{\begin{array}{ll}1,\ when\ MS\ p\ is\ scheduled\ on\ carrier\ l\ in\ t\\ 0,\ else\end{array}\right. \tag{10}\]  
Instantaneous SINR information is exploited by the PF scheduler which is experienced by all MSs \(q\in\mathcal{M}_{A}\) and allocates subcarrier \(\ l\in\mathcal{F}_{\text{A}}\) to MS \(u\in\mathcal{M}_{A}\), satisfy the condition:  
\[u=arg\max_{q\in\mathcal{M}_{A}}\Bigl{\{}P_{c}(t)W_{p,\ l}^{Z}(t)\Bigr{\}} \tag{11}\]  
\(\mathcal{M}_{A}\) represents the total MSs number in a certain cell area, \(A\).
## 3 Background  
The IEEE 802.1Q-2018 [27] is one of the main reference standards in the TSN family. It defines the functionality of the switches (bridges according to the IEEE terminology) as well as the operation of one or many switches in a Switched Ethernet network. A relevant standard is the IEEE 802.1 AS-2011 [20], which specifies protocols to synchronize the local clocks of end nodes and switches. Its 2020 revision, i.e., the IEEE 802.1 AS-2020 [24], improves the clock synchronization reliability and introduces replication mechanisms to cope with switch fault or frame loss.  
According to [27], in the switches and the end nodes, each Ethernet port has a number of transmission queues, ordered by priority from 0 to 7. The frame to be transmitted is chosen according to the Strict Priority (SP) selection algorithm defined in [27], which picks the frame that is the head of the highest priority queue available for the transmission. Figure 1 shows an example of Ethernet port forwarding architecture. The SP is the green block in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multicolumn{2}{c}{**RT**} & \multirow{2}{*}{\begin{tabular}{c} **Scheduled** \\ **Traffic** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **IEEE 802.1Q** \\ **Standard** \\ **Compliance** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Scheduling** \\ **Granularity** \\ \end{tabular} } \\  & & & & \\ \hline AVB [21; 22] & - & - & yes & per-class \\ AVB+ST [21; 22; 25] & - & ✓ ✓ & yes & per class \\ ATS [33] & ✓ ✓ & - & yes & per-flow \\ EDSched [32] & ✓ ✓ & ✓ ✓ & yes & per-class \\ D-TSN [29] & ✓ ✓ ✓ & - & yes & per-frame \\ Approach in [52] & ✓ & ✓ ✓ & yes & per-flow \\ Approach in [53] & ✓ & ✓ ✓ ✓ & yes & per-flow \\ Hybrid-TSN [54] & ✓ ✓ ✓ & ✓ ✓ & no & per-flow \\ E-TSN [55] & ✓ ✓ & ✓ ✓ & yes & per-class \\ D-ST & ✓ ✓ ✓ & ✓ ✓ & yes & per-frame \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of state-of-the-art transmission approaches.
## 4 Conclusions  
Two types of kraft lignin, including softwood (SL) and hardwood (EL) fractionated in common industrial solvents, were successfully modified with methacrylochloride. The modified and unmodified lignins were then used for the synthesis of polymeric microspheres with St and/or DVB. The structural characteristics of the lignin fractions, such as degree of condensation, number and type of various hydroxyl groups, and molecular weight, influenced the modification process and the properties of the materials. Thermal properties of materials were studied by DSC and the materials were found to have a high thermal resistance. The incorporation of methacrylated lignin into the microspheres resulted in the greatest specific surface area and porosity. The largest specific surface area and the largest total pore volumes were found in the materials with SL-m and EL-e fractions, which had a higher molecular weight than the corresponding ethyl acetate fractions SL-ea and EL-ea. SL-m and EL-e fractions had a higher content of aliphatic hydroxyl groups than the ethyl acetate fractions.  
Figure 5: Photomicrographs of the microspheres. 1-SL-ea-Met + St + DVB, 2-SL-ea + St + DVB, 3-1 SL-ea-Met + DVB, 4-2 SL-ea-Met + DVB, 5-3 SL-ea-Met + DVB, 6-SL-ea + DVB, 7-SL-m-Met + St + DVB, 8-SL-m + St + DVB, 9-1 SL-m-Met + DVB, 10-2 SL-m-Met + DVB, 11-3 SL-m-Met + DVB, 12-EL-ea-Met + St + DVB, 13-EL-ea + St + DVB, 14-1 EL-ea-Met + DVB, 15-2 EL-ea-Met + DVB, 16-3 EL-ea-Met + DVB, 17-EL-ea + DVB, 18-EL-e-Met + St + DVB, 19-EL-e + St + DVB, 20-1 EL-e-Met + DVB, DVB, 21-2 EL-e-Met + DVB, 22-3 EL-e-Met + DVB, 23-EL-e + DVB. The scale bar (see sample 1) is the same for all images.  
These groups can also react with methacryloyl chloride, and, thus, can give a more homogeneous distribution of methacrylic groups in the lignin macromolecules and, therefore, better characteristics of the final products. The microspheres swelled most in acetone and chloroform. They did not swell at all in distilled water. Materials with EL-e had the lowest swellability coefficients, which may be related to their high degree of crosslinking. The most homogeneous size distribution of microspheres was obtained with EL-e-Met lignin and DVB.  
Conceptualization, M.G. and B.P. Formal analysis, M.G., B.P., and O.S. Investigation, M.G., B.P., O.S., and A.C. Methodology, M.G. and B.P. Project administration, M.G. and B.P. Resources, M.G., B.P., O.S., and B.G. Supervision, B.P., O.S., and B.G. Visualization, M.G., B.P., O.S., and A.C. Writing--Original draft, M.G. Writing--Review & editing, B.P., O.S., and B.G.  
This research received no external funding.  
The authors would like to thank [PERSON] (Research Laboratory, Nippon Paper Industries Co., Ltd., 5-21-1 Oji, Kita-ku, Tokyo 114-0002, Japan) for the preparation and characterization of lignin fractions used in the synthesis of microspheres and the COST Action CA17128 Establishment of a Pan-European Network on the Sustainable Valorization of Lignin for making it possible to exchange an experience with the other scientists.  
The authors declare no conflict of interest.
# Simplified Polydispersion Analysis of Small-Angle Scattering Data
## 6 SANS Experiment  
The effect of a concentration of polydisperse silica particles LUDOX HS30 was investigated by small-angle neutron scattering [29]. Different samples with a volume fraction ranging from 0.3 up to 16.5% were considered, and the SDF of the silica particles was calculated from the SCS of the sample with the lowest volume fraction by fitting the SCS with the Weibull SDF.  
The SPA with user-defined functions and non-linear fits was applied here to the same data, and the results are shown in Figure 9a, where in Figure 9b, the SCSs are shown with the fit using the traditional fitting procedure with a Weibull SDF, taking into account corrections for the wavelength spread (\(\Delta\lambda/\lambda\) = 18%) and for multiple scattering. The Weibull SDF obtained with the fitting procedure and the lognormal SDF calculated with the SPA are compared in Figure 10.  
Figure 8: SDFs reconstructed with the parameters (\(N_{o}\), \(R_{o}\), and \(\sigma\)) calculated with the SPA for the case \(\sigma=0.4\) without (red line) and with additional backgrounds of 1.0 \(\times\) 10\({}^{-5}\) (B1—blue line), 1.0 \(\times\) 10\({}^{-4}\) (B2—green line), and 1.0 \(\times\) 10\({}^{-3}\) cm\({}^{-1}\) (B3—yellow line). The original SDF is also shown for comparison (black line).  
\begin{table}
\begin{tabular}{l c c c} \hline  & \multicolumn{2}{c}{**Background (cm\({}^{-1}\))**} \\  & **1.0 \(\times\) 10\({}^{-5}\)** & **1.0 \(\times\) 10\({}^{-4}\)** & **1.0 \(\times\) 10\({}^{-3}\)** \\ \hline calculated values & & & \\ \(N_{o}\) (10\({}^{23}\) cm\({}^{-4}\)) & 2.77 \(\pm\) 0.83 & 2.89 \(\pm\) 1.25 & 3.35 \(\pm\) 1.58 \\ \(R_{o}\) (Å) & 25.3 \(\pm\) 2.3 & 24.9 \(\pm\) 3.2 & 23.9 \(\pm\) 3.3 \\ \(\sigma\) & 0.34 \(\pm\) 0.02 & 0.34 \(\pm\) 0.02 & 0.35 \(\pm\) 0.02 \\ \hline \end{tabular}
\end{table}
Table 3: Calculated values of the three parameters of the lognormal SDF of spherical inhomogeneities obtained by the SPA of the simulated SCS with the backgrounds added to the case of \(\sigma\) = 0.4.  
The Ludox HS30 average radius and the volume fraction were found to be 61.3 \(\pm\) 19.0 A and 0.41 \(\pm\) 0.13% with the SPA and 62.3 \(\pm\) 2.3 A and 0.25 \(\pm\) 0.01% with the fitting procedure, respectively. The values and the SDFs obtained by the SPA and by the fitting procedure are in surprisingly good agreement with each other, bearing in mind the differences in the two procedures. The estimated uncertainties in the case of the SPA are larger, but still acceptable, as this is largely justified by the simplicity of the SPA method.
## 5 Conclusions  
In order to solve the problems of time consumption, relative inefficiency, equipment constraints, and the influence of surveyors, this study proposes a non-contact detection method for measuring sow backfat thickness based on the feature visualization of a residual network. Compared to the VGG16 model and that proposed by [PERSON] et al. (2017), the R\({}^{2}\) of the model in this study was 0.94 in the test set, which is higher than the R\({}^{2}\) of the other two models, and the MAE was 0.44 mm, which is lower than the MAE of the other two models, indicating that the performance of the model in this study is better than the other two. Secondly, compared to the model before segmentation, the R\({}^{2}\) increased by 3.3% and the MAE decreased by 18.5%, which verifies the feasibility of removing irrelevant features and improving the accuracy of the sow backfat thickness detection model.  
Feature visualization not only provides a method to improve the accuracy of the model but also validates the conclusions of some researchers on sow backfat thickness. We also found a sow contour area feature that is strongly correlated with sow backfat thickness. Whether establishing a model of the feature information of these areas to determine the backfat thickness of sows can improve the accuracy of the data, reduce the model size, and improve the accuracy of the model needs to be further studied.  
Conceptualization, T.C. and D.X.; methodology, T.C.; software, T.C.; validation, X.L. ([PERSON]); formal analysis, X.L. ([PERSON]) and D.X.; investigation, T.C.; resources, X.L. ([PERSON]) and X.L. ([PERSON]); data curation, H.L.; writing--original draft preparation, T.C.; writing--review and editing, D.X.; visualization, H.W.; supervision, D.X.; project administration, X.L. ([PERSON]) and D.X.; funding acquisition, X.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Hubei Provincial Science and Technology Major Project (2022 ABA002); Wuhan Biological Breeding Major Project (2022021302024853); Huazhong Agricultural University--Chinese Academy of Agricultural Sciences, Shenzhen Institute of Agricultural Genomics Cooperative Fund Project (SZYJY2022031); Huazhong Agricultural University Independent Science and Technology Innovation Fund (2662022 XXYJ009); Intelligent Phenotypic Testing of Livestock and Poultry for Major Agricultural Projects (2022 ZD0401802).  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & **Split Pre-MAE** & **Split Pre-R\({}^{2}\)** & **MAE after** & **R\({}^{2}\) after** \\
**Indicators** & & & **Splitting** & **Splitting** \\ \hline Test set 1 & 0.48 & 0.95 & 0.46 & 0.95 \\ Test set 2 & 0.45 & 0.89 & 0.45 & 0.91 \\ Test set 3 & 0.66 & 0.91 & 0.65 & 0.92 \\ Test set 4 & 0.56 & 0.92 & 0.26 & 0.98 \\ Test set 5 & 0.55 & 0.95 & 0.4 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of 5 sets of test sets before and after segmentation.
# Recent Progress and Challenges Regarding Magnetic-Based Nanoparticles for Targeted Drug Delivery
### Magnetic Nanoparticles for Biomedical Application
Photodynamic therapy (PTD) is a new therapeutic approach based on the light activation of a photosensitizer (PS; e.g., chlorine e6 attached to magnetite core) at the tumor site. The PS is irradiated using an appropriate wavelength. After that, toxic reactive oxygen species (ROS) are generated, which destroys tumor cells. After irradiation, PS is excited from the ground singlet state. The photosensitizer acquires energy through fluorescence or phosphorescence. Thus, the PS directly interacts with an electron/hydrogen-bearing agent, forming peroxides or reactive oxygen species that destroy cancer cells through oxidation. Alternatively, energy is transferred to molecular oxygen, creating highly reactive and toxic singlet oxygen. The biomedical sector uses different imaging methods, but the most common diagnostic techniques include computed tomography (CT), magnetic particle imaging (MPI), magnetic resonance imaging (MRI), and positron emission tomography (PET). Nevertheless, magnetite nanoparticles are predominantly investigated as elements for MRI. MRI allows for the visualization of anatomical structures, physiological functions, and the molecular composition of tissues. It relies on the absorption and emission of energy within the electromagnetic spectrum's radiofrequency (RF) range. It distinguishes itself from other techniques, such as computed tomography (CT), by providing outstanding soft tissue contrast without harmful ionizing radiation [17]. The contrast in MRI results from variations in signal intensity, influenced by factors such as the concentration of water molecules within the tissue, the relaxation times of water protons, and the mobility of water molecules. The use of contrast agents can further enhance image contrast. Magnetite nanoparticles are extensively studied as potential contrast agents exhibiting enhanced specificity and biocompatibility compared to clinically used gadolinium chelates [18]. Finally, magnetite nanoparticles are significantly researched as components of nanocarriers for gene or drug delivery. We present the underlying assumptions of such systems to gain a deeper understanding of their application in drug delivery systems (DDSs), particularly in targeted DDSs for cancer therapy.