esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges and Opportunities for Conducting Dynamic Risk Assessments in Medical IoT
## 2 Contextualisation
### Medical IoT  
Working with IoT technologies in healthcare, there are a myriad of similar notions and definitions that we differentiate next. For instance, we have encountered references to the Health Internet of Things (HIIoT) [49], the Internet of Health Things (IoHT) [50,51], the IoT-Health [52], the Internet of Medical Things (IoMT) [53, 54, 55], and the Medical Internet of Things (MIoT) [1,2,56]. In this work, we shall refer to the latter (MIoT) as a base definition as we think it captures the fundamental differences between general IoT systems versus those applied to medical/healthcare contexts with its more specific subtleties. MIoT empowers patients and clinical staff (in general) to understand care paths and plan interventions. It allows tracking, sensing and near real-time screening of patients that alert responsible personnel in case they detect or observe any anomaly using tailored algorithms that are either running on devices or using auxiliary information systems.  
Understanding risk in systems is typically founded upon understanding the system itself. Evaluating a system architecture is, therefore, a starting point. Within IoT systems, the architecture has most simply been considered as a basic three-layer approach, composed of the _perception layer_, the _network layer_ and the _application layer_[57]. While it is beyond the scope of this work to assess what is the best architecture to underpin security and privacy risk assessment, we note that these basic models constrain the system viewpoints potentially missing key IoT vulnerabilities. Hence, architecture model extensions, such as a _middleware layer_, a _business layer_, an _end-user layer_, a _processing layer_ and a _service management layer_[58, 59, 60] could offer a better perspective. Security analysts overseeing large dynamic attack surfaces may then consider threat actors attempting to circumvent controls and exploit vulnerabilities on each layer as they have specific protocols and inter-layer dynamics [61,62]. [PERSON] et al. (2018) [63] go beyond this notion and consider different scales of _IoT layers_, dividing these into three types--wearables, smart homes and smart cities--across four layers: interface, service, networking and sensing.
## 3 Methodology  
The methodology includes the following major steps (Figure 2): (1) the registration of the accuracy verification of the two DSM data, (2) the detection of elevation change information, (3) the verification and evaluation of elevation change accuracy, (4) the exclusion of non-building changes, and (5) the calculation of building expansion or demolition amount. The main software used for data processing are ArcGIS and ENVI. More details of the steps are described below.
## 4 Test Results and Discussion
### Strain Behavior  
The strains measured through the strain gauges attached to the prestressing tendon and tension steel bar were compared as the load increased, as shown in Figures 6 and 7. As shown, the strain expression mode increases continuously with the load.  
Figure 6 shows how the strain in the tension bar changes as the load is increased. The strain at the mid-span of the beam increased sharply when the initial bending crack occurred; subsequently, it increased further with the load until it reached the yield strain. Furthermore, unlike the beams with conventional anchorages, the beams with the newly proposed anchorage exhibited tension strain prior to the load being increased.  
Figure 7 shows that the strain in the prestressing tendon increased sharply as the load-sharing ratio of the prestressing tendon for the applied load increased after the yielding of the tension bar. Compared with beams with conventional anchorages, the prestressing tendon of a beam with the newly proposed anchorage exhibited strain until the load increased significantly. This is because the proposed anchorage continued to resist the load applied on the prestressing tendon until the ultimate condition, because of its efficient load transmission structure with a minimum number of stud anchors, and the improved shape of the anchorage itself.
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 4 Experiments
### Ablation Study  
To showcase the efficacy of our approach, we conducted an ablation study on the classification task in ModelNet40. Furthermore, we performed separate ablation experiments on the ResSimAM and the Squash to assess the impact of removing each component.  
In our settings (Table 3), W/O R means no ResSimAM interaction, and W/O S means no Squash. The corresponding results are shown in Table 4.  
We utilized ResSimAM, which resulted in an improvement of the overall accuracy by 0.6%. We employed Squash to squeeze the features, thus leading to a 1.4% improvement in the overall accuracy. And we employed both operations--leading to a 2.1% improvement--and obtained a state-of-the-art result of 83.9% in no-parametric point cloud classification. It  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Overall Accuracy (\%)** \\ \hline W/O R\&S & 81.8 \\ W/O S & 82.4 \\ W/O R & 83.2 \\ Point-Sim & 83.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ResSimAM and Squash ablation results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Overall Accuracy (\%)** & **Parameters** \\ \hline PointNet & 68.2 & 3.5 M \\ PointNet++ & 77.9 & 1.7 M \\ GBNet & 80.5 & 8.4 M \\ DGCNN & 78.1 & 1.8 M \\ PointMLP & 85.2 & 12.6 M \\ Point-NN & 64.9 & 0 M \\ \hline Point-Sim & 66.3 & 0 M \\ Point-SimP & 86.6 & 0.8 M \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification results on ModelNet40.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Res-SimAM** & **Squash** \\ \hline W/O R\&S & - \\ W/O S & ✓ & - \\ W/O R & - & ✓ \\ Point-Sim & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Settings of ResSimAM and Squash. where ✓ means that the module is included, and - means that the module is not included.  
has been proven that using ResSimAM can better focus on higher energy features during the feature extraction stage, which can enhance features useful for subsequent processing, while the Squash module enables the input features to be squeezed to a certain range without changing the direction in the vector space, which realizes a nonlinear feature transformation and reduces the numerical instability due to the change of vector length. With ResSimAM, we can indeed better capture features with higher energy for feature enhancement, but it is possible that features with higher energy are not the most appropriate choice in the subsequent processing, so this approach brings some enhancement to the model's classification ability but with some limitations. For the Squash operation, although squeezing the features facilitates the network to capture the relationship between the features and better perform the naive similarity matching, squeezing the features also brings some loss of feature information. These aspects still need to be improved.
## 4 Discussion
These enriched genes suggest that the pcECM-DM provides a conducive environment for cellular activities essential for cartilage repair and regeneration. These findings highlight the multifaceted role of pcECM-DM in promoting chondrogenesis and ECM production. The ability of pcECM-DM to support various molecular functions and gene enrichment makes it a promising candidate for cartilage tissue engineering applications. Similar studies provide a comprehensive understanding of the molecular functions of ECM-based materials and their potential in tissue engineering applications. The roles of biologics in cartilage repair, regrowth, and regeneration highlight the importance of ECM organization and cell adhesion in promoting chondrogenesis and ECM production [29]. Previous studies showed molecular pathways that were vital for ECM functionality in chondrocyte cell sheets. Integrins facilitated cell-ECM adhesion, regulating chondrocyte functions like differentiation, metabolism, matrix remodeling, mechanical responses, and survival. Integrin signaling maintained ECM integrity by promoting collagen type II and proteoglycan production. The Wnt pathway regulated chondrogenesis and cartilage homeostasis, promoting collagen type II and aggrecan synthesis. Nitric oxide modulated chondrocyte metabolism and ECM synthesis, balancing ECM production and degradation. The protein kinase C pathway regulated chondrocyte proliferation and differentiation, influencing ECM component synthesis. Retinoic acid signaling, a vitamin A derivative, regulated collagen and proteoglycan synthesis genes, contributing to ECM functionality [30]. In addition, for pcECM-DM fabrication, the 1% SDS decellularization process effectively removed cellular components, confirmed by the absence of cellular debris under microscopic examination. DNAse/RNAse enzymes eliminate residual DNA and RNA, which prevents immune reactions in clinical applications. However, potential limitations, such as immune responses to porcine-derived materials and long-term stability, must be addressed to ensure the successful clinical application of pcECM-DM. Future research should prioritize long-term evaluations and in vivo assessments to further substantiate the efficacy and safety of pcECM-DM in clinical settings. These findings contribute to the expanding body of evidence supporting the use of ECM-based biomaterials in regenerative medicine. Moreover, we have delineated the steps required to transition from preclinical studies to clinical applications, including the design and implementation of in vivo studies to evaluate the efficacy and safety of pcECM-DM in relevant animal models. Additionally, we have addressed potential regulatory challenges, such as obtaining approval from regulatory agencies, ensuring compliance with Good Manufacturing Practices, and conducting clinical trials to demonstrate the safety and efficacy of pcECM-DM in human patients.
# Microbes as Medicines: Harnessing the Power of Bacteria in Advancing Cancer Treatment
## 2 Mechanism of Tumor Suppression
### Bacterial Cell Death Inducing Agents
#### 2.1.1 Bacterial Toxins  
Bacteria express and release certain toxins that inhibit the production and release of antibodies and cytokines resulting in immunosuppression. Bacterial toxins are enzymatic in nature and have a high degree of specificity for cellular substrates which are signaling molecules. The toxins may be cell cycle inhibitors, mitosis blockers and may compromise immunity by preventing lymphocyte clonal expansion [15]. The bacteria on entering the tumor cells release the toxins and alter their substrates, change the functions and morphology of the cells, and eventually kill its host. Bacterial toxins can be cytotoxic or at lower concentration may result in alteration in proliferation, differentiation, and apoptosis. These alterations can cause cellular aberrations and result in carcinogenesis [16].  
The bacterial toxin widely used as an anti-tumor agent is cytolysin (CylA). Cytolysins are pore forming agents, that form multimeric pores in the eukaryotic membrane and induce apoptosis by the caspase-mediated pathway. Cytolysins are generally derived from _Escherichia coli_ or _Staphylococcus aureus_[4]. Studies have demonstrated that mice on treating with _S. typhimurium_ or _E. coli_ strains expressing the ClyA toxin demonstrated tumor growth inhibition [11; 17]. [PERSON] et al. engineered cytolysin producing _E. coli_ strain K-12 which in combination with radiotherapy retarded tumor growth in mice with tumors derived from murine CT26 colon cancer and prevented its metastatic spread [11]. The bacteria _Clostridium botulinum_ produces Botulinum neurotoxin A, which diminishes cell proliferation in prostate cancer (PC-3 and LNCaPP) [18] and induces apoptosis in breast cancer cell lines (T47D) [19; 20]. In another study by [PERSON] et al., modified Pseudomonas exotoxin A (PE38), a natural ligand of epidermal growth factor receptor (EGFR) when expressed in _Salmonella typhimurium_, led to the inhibition of the solid tumor growth in mice implanted with colon cancer cells and 4T1 breast cancer cells having considerable EGFR expression [21].
# Method for Volume of Irregular Shape Pellets Estimation Using 2D Imaging Measurement
## 2 Materials and Methods
Figure 3: Similarity of results of pellet approximation model and control equipment. (**a**) Control measuring equipment consisting of a set of sieves with different diameter holes; (**b**) the width of pellet approximation shape matches the diameter of a hole of a sieve. Figure 2: Connection between pellet and the shape that is being approximated: (**a**) area of discrepancy between real area of irregular pellet and circle shaped approximation is marked by the shaded area; (**b**) left side—real area limited by pellet (S1) and perimeter (P1); right side—circle shape formed from the area of pellet. The analyzed scan of the pellet image is two dimensional, therefore in calculation we additionally use the width of the ellipsoid, i.e., instead of one radius of the ellipsoid, which is equal to the depth of image, the other radius is used in Equation (2):  
\[V=\frac{4}{3}\pi r_{2}r_{2}\frac{d_{2}}{2}=\frac{4}{3}\pi r_{2}^{2}\frac{d_{2}}{ 2}, \tag{2}\]  
where \(r_{2}\)--radius of ellipsoid; \(d_{2}\)--length of ellipsoid (ellipsoid scheme in Figure 4). When pellets are analyzed in terms of volume distribution using Equation (2), each pellet is evaluated as a shape of the ideal ellipse. Such an evaluation will be correct only in specific cases, when the product is characterized by a high index of roundness and smoothness of surfaces. These parameters are determined by chemical composition of a pellet. It determines their fragility and adhesion of mixture. Therefore, when working with irregularly shaped pellets, the method of ellipsoid volume evaluation causes a discrepancy between the results and the real volume of such pellet.
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 4 Results
### Evaluation Results for the Case of Interpolation
The most encouraging success was the good reproduction of newly emerging and moving radar echoes in the west of the radar images (red boxes in Figure 6). This can clearly be observed for the results of missing patterns '1100000011' (the 5 th line in Figure 6) and '1000000001' (the 6 th line in Figure 6). The details of the radar echoes were indeed less distinct as the number of missing frames increased, which agreed with the evaluation results. The reconstructed radar images for the selected example with respect to different models are shown in Figure 7, but only for the missing pattern '1110000111'. The performances of the CNN-BiConvLSTM model and the 3 DCNN model were better on the whole, but with slightly more detailed echoes for the CNN-BiConvLSTM model. The reconstructed radar images from the CNN-ConvLSTM model lost much more detail compared to the ground truth. For the linear model, a large area of weak radar echo appeared around ground truth, and the strong radar echoes were diluted to some extent. For the optical flow model, the intensity of the radar echoes behaved quite well but exhibited an incorrect and fixed shape. More seriously, the reflectivity factors in some of the marginal areas in the reconstructed images were filled with zeros since the radar echoes moved out to other regions following the velocity field. Two subareas were selected to further demonstrate the effectiveness of different models for the marginal area (red and black boxes in Figure 7). The corresponding scores are shown in Figure 8. It was obvious that the traditional methods were quite unsuccessful in the two subareas, especially for subarea-2. The locations of the clear sky were quite distinct and correct for the reconstructed radar images in subarea-2 for the 3 DCNN model and the CNN-BiConvLSTM model, but were not quite correct for the  
Figure 6: Example of reconstructing missing radar frames with the CNN-BiConvLSTM model. The first line shows the input radar images (ground truths); the second line to the seventh line show reconstructed radar images for 6 specified missing patterns. other models. In addition, the movements of radar echoes in subarea-1 can also be clearly observed for the 3 DCNN model and the CNN-BiConvLSTM model but did not occur for the other models. In addition, the scores, which exclude the marginal area, were also tested, and they indeed showed slightly higher scores, but the order of the scores did not change. In summary, the CNN-BiConvLSTM model outperformed all the baseline models involved. Figure 7: Comparison of different models in reconstructing missing radar frames for the missing pattern of ‘1110000111’. The 1 st line shows the fourth to the 7 th input radar images (ground truths); the 2d to the 7 th lines show the reconstructed radar images from different models.
## 6 Numerical Results
### Numerical Results for F-O Performance Metrics  
The OP versus \(\gamma\) as well as the OP versus \(\tilde{\gamma}\) for weak \((a_{1}=5,b_{1}=7.0941,\)\(a_{2}=4.5916,b_{2}=7.0941)\), moderate \((a_{1}=2,b_{1}=4.5323,a_{2}=2.3378,b_{2}=4.5323)\) and strong \((a_{1}=1,b_{1}=3.4948,a_{2}=1.4321,b_{2}=3.4948)\) TI fading severities [14] are presented, respectively, in Figures 2 and 3. Since (23) was the C-F finite series expression, the presented numerical results for the OP were limited to integer values of \(a_{1}\). It can be observed that, by shifting from strong-to-moderate or moderate-to-weak TI fading conditions, the OP decreased. In Figure 2, it can be further noticed that, by increasing the average SNR values, the OP decreased, which in turn could provide an additional system performance improvement of the FSO RIS-ACs link over F-S TI fading propagation channels. From Figure 3, it can be observed that a higher \(\tilde{\gamma}\) caused an increase in the OP (e.g., by increasing \(\tilde{\gamma}\) from \(\tilde{\gamma}=3\) to \(\tilde{\gamma}=6\)).  
The \(P_{BER}(\gamma)\) versus \(\gamma\) for \(a_{1}=6,b_{1}=2.58,a_{2}=5.75,b_{2}=2.58\) and \(a_{1}=3\), \(b_{1}=2.1,a_{2}=2.73\) and \(b_{2}=2.1\) TI fading severity values [13], where \(a_{1}\) was an integer, and for the selected binary modulation schemes such as BFSK, BPSK, NBFSK and DBPSK are presented in Figure 4. It was obvious that less severe TI fading conditions provided a lower \(P_{BER}(\gamma)\) (e.g., by shifting from \(a_{1}=3,b_{1}=2.1,a_{2}=2.73\) and \(b_{2}=2.1\) to \(a_{1}=6,b_{1}=2.58,a_{2}=5.75\) and \(b_{2}=2.58\)). It can be further concluded that, for higher dB \(\gamma\) values, TI fading conditions had a stronger impact on the \(P_{BER}(\gamma)\) than the observed modulation schemes.  
Figure 3: Outage probability versus \(\gamma_{th}\) observed for different TI fading severity conditions.  
Figure 2: Outage probability versus \(\gamma_{th}\) observed for different TI fading severity conditions.  
The \(C_{\gamma}\) of the considered FSO RIS-ACs model over F-S \(\mathcal{F}\) TI fading propagation channels for weak \((a_{1}=4,b_{1}=7.0941,a_{2}=4.5916,b_{2}=7.0941)\), moderate \((a_{1}=2\), \(b_{1}=4.5323,a_{2}=2.3378,b_{2}=4.5323)\) and strong \((a_{1}=1,b_{1}=3.4948,a_{2}=1.4321,\)\(b_{2}=3.4948)\) TI fading severities [14], where \(a_{1}\) took integer values, is shown in Figure 5. As expected, the \(C_{\gamma}\) could be increased by shifting from severe to less severe TI fading conditions (e.g., by shifting from strong-to-moderate or moderate-to-weak TI fading severities). A similar behaviour for an FSO RIS AC under the IM-DD modulation technique was noticed in [24; 27].  
Figure 4: Average BER versus \(\gamma_{th}\) observed for different TI fading severity conditions and different modulations technique.  
Figure 5: Ergodic capacity versus \(\gamma_{th}\) observed for different TI fading severity conditions.
# BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
## 6 Conclusions  
In this study, to achieve fast user pairing for realistic downlink NOMA systems, we designed a user pairing scheme based on a laser chaos decision-maker. In our proposed scheme, the user pairing is accomplished by the laser-chaos-based decision-making algorithm based on responses from the users, reflecting the bit errors using a realistic system model. Simulation results showed that our proposed LCDM-NOMA scheme exhibits superior BER performances compared to conventional methods. Moreover, to improve the performance in terms of BER for our proposed scheme, error-correcting codes are introduced in the system. Furthermore, parameter dependencies in the user pairing by a laser chaos decision-maker are investigated to minimize the BER of the NOMA system. We demonstrated that error-correcting codes and the fine or coarse tuning of decision-making impact the system performance depending on the cell radius. Moreover, simulation results demonstrated that our proposed scheme considering error-correcting mechanisms exhibits superior performances in BER compared to other conventional schemes. As the cell radius of the NOMA system gets smaller, the superior on the BER of our proposed scheme gets bigger. Specifically, our proposed scheme can decrease the BER from \(10^{-1}\) to \(10^{-5}\) compared to the conventional schemes when the cell radius is 400m. Based on these insights, we demonstrated an adaptive pairing scheme to optimize operation parameters to minimize BER of the NOMA system under study. Further adaptive principles are of interest for future studies, including adaptive modulation format and resource block assignments as well as experimental demonstrations.  
Conceptualization, A.L., M.N. and M.H.; methodology, M.S., A.L., Z.D., M.N. and M.H.; software, M.S., A.L. and Z.D.; validation, M.S.; formal analysis, M.S., A.L., M.N. and M.H.; investigation, M.S., A.L., Z.D., M.N. and M.H.; resources, M.S., A.L. and M.H.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, A.L., M.N. and M.H.; visualization, M.S. and A.L.; supervision, A.L., M.N. and M.H.; project administration, M.N. and M.H.; funding acquisition, A.L., M.N. and M.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported partly by the CREST project (JPM)CR17N2) funded by the Japan Science and Technology Agency and Grants-in-Aid for Scientific Research (JP20H0233) funded by the Japan Society for the Promotion of Science.  
The authors declare no conflict of interest.  
Figure 12: BER comparison with C-NOM, UCGD-NOMA, and \(\epsilon\)-greedy-based user pairing scheme.