esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Security Solutions
### End Device Security  
#### 6.1.1 Secure Execution Environment  
Software attacks compromise the software executed on the IoT devices. A Secure Execution Environment (SEE) can provide essential security for IoT software in the face of untrusted applications and operating systems. A SEE can be achieved by using trusted computing techniques, such as Trusted Platform Module (TPM) [106].  
A TPM chip ensures protected execution of security-sensitive applications and operating systems. A processing unit embedded with the TPM chip isolates the execution of the trusted software and untrusted software. The isolation is performed based on either physical or logical separation of processes. In physical separation, trusted software is executed on a dedicated secure processor, such as the secure co-processor developed by IBM [107]. In contrast, in logic separation, a single processor is capable of supporting a secure mode, which isolates sensitive and untrusted code by using an additional software layer [145] or hardware support, such as Intel TXT and SGE platforms [108] and ARM TrustZone [109].
# A Hierarchical Federated Learning Algorithm Based on Time Aggregation in Edge Computing Environment
## 3 Dynamic Weighted Hierarchical Federated Learning Algorithm
### Hierarchical Federated Learning Algorithm
#### 3.3.2 Hierarchical Federated Learning Training Process Based on Dynamic Weighting
The hierarchical federated learning algorithm based on dynamic weighting is presented in Algorithm 1:  
```
1:Global epochs \(T\), Local epochs \(E\), learning rates \(\eta\), all clients \(N\), training minibatch size \(B\), participating clients \(S\);
2:Global model parameters \(\omega\);
3:Central server:
4:for each global epoch \(t=1,2,\ldots,T\)do
5:for each edge server \(m=1,2,\ldots,M\)do
6: Send global model parameters \(\omega^{t}\) to edge server \(m\) in parallel;
7: Receive the aggregated model \(\omega_{m}^{t}\) from edge server \(m\);
8:endfor
9:Global aggregation: \(\omega^{(t+1)}\leftarrow\frac{\sum_{m\in M}|D_{m}|\omega_{m}^{t}}{|D|}\);
10:endfor
11:Edge server:
12:for each edge server \(m=1,2,\ldots,M\)do
13:for each client \(s\in S\)do
14:\(\omega_{s}^{t+1}\leftarrow\mathit{ClientUpdate}(s,\omega_{m}^{t},P_{m})\);
15:endfor
16: Calculate weights: \(\alpha_{s}=\frac{\left(1-EMD_{(s,m)}\right)/\left(1+EMD_{(s,m)}\right)}{\sum _{s=1}^{s}\left[\left(1-EMD_{(s,m)}\right)/\left(1+EMD_{(s,m)}\right)\right]}\);
17: Edge aggregation: \(\omega_{m}^{t+1}=\sum_{s=1}^{s}\alpha_{s}\omega_{s}^{t+1}\);
18:endfor
19:Client devices:\(\mathit{ClientUpdate}(s,\omega_{m}^{t},P_{m})\)
20:Calculate \(EMD_{(s,m)}\): \(EMD_{(s,m)}=\|\sum_{k=1}^{k}P_{s}(y=k)-P_{m}(y=k)\|\);
21:for each local iteration \(l\in\{1,2,\ldots,E\}\)do
22:for each batch \(b\in B\)do
23: Local Updates: \(\omega_{s}^{t}\leftarrow\omega_{s}^{l-1}-\eta\
abla F_{s}(\omega_{s}^{l-1})\);
24:endfor
25:endfor
26:return Local model parameters \(\omega_{s}^{t},EMD_{(s,m)}\) to the central server. ```  
**Algorithm 1** The hierarchical federated learning algorithm based on dynamic weighting (FedDyn)  
The concept behind the hierarchical federated learning algorithm, which is based on dynamic weighting in local training and aggregation, is a modified version of the conventional federated learning algorithm. By introducing the one edge aggregation step,the hierarchical federated learning algorithm based on dynamic weighting reduces the need for direct communication with the central server and significantly lowers communication costs during training. The addition of an aggregation process in the hierarchical federated learning algorithm presents a challenge for improving training efficiency. The specific efficient aggregation strategy will be explained in Section 4.
# Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations
## 2 Vehicle Performance Modelling
### Gearbox and Gearshift Logic  
The gearbox model converts the wheel torque and speed to the corresponding ones at the input of the gearbox. A simple vehicle speed-based gearshift strategy is implemented in the model, accounting for the constraint of ICE maximum speed. Gearshift speeds are defined by analyzing the gearshift experimental data from Argon National Laboratory (ANL) Figure 3 shows the gearshift logic. To avoid frequent gear shifting, upshifts and downshifts are performed with a hysteresis that varies with the engaged gear.  
The required torque \(T_{gb}\) and angular speed \(\omega_{gb}\) at the gearbox input can be computed from the final gear ratio \(i_{f}\) and the gear ratio of the gearbox \(i_{gb}\), as:  
\[\left\{\begin{array}{l}T_{gb}=\frac{T_{angle}}{i_{f}\cdot i_{f}\cdot j_{gb} \cdot\eta_{gb}}\ \ \ if\ T_{axle}>0\\ T_{gb}=\frac{T_{angle}\cdot i_{f}\cdot\eta_{gb}}{i_{f}\cdot j_{gb}}\ \ \ if\ T_{axle}\leq 0\end{array}\right. \tag{5}\]  
Figure 3: Speed dependent gearshift logic.  
where the efficiencies of the final gear and the gearbox are considered as \(\eta_{f}=\eta_{gb}=0.98\).  
\[\omega_{gb}=\omega_{w}\cdot i_{f}\cdot i_{gb} \tag{6}\]  
In a conventional vehicle \(T_{gb}\) and \(\omega_{gb}\) are the ICE torque and speed at the gearbox. When considering a HEV, the supervisory controller splits the same torque between the ICE and the EM depending on the established rules.
## 3 Results
### Confirmation of Antigen-Binding Efficiency  
We examined antigen-binding activity for each antibody fragment by performing indirect ELISA. We seeded commercially available mPD-L1 protein into a 96-well plate and blocked the wells using skim milk. Subsequently, we added each antibody fragment as the primary antibody followed by HRP-conjugated anti-Flag antibody as the secondary antibody (Figure 3A). As a result, we obtained the signals of all three antibody fragments, which increased in an antigen concentration-dependent manner. In contrast, without antigens, negligible signals were observed, indicating that each antibody fragment retained the antigen-binding activity. When 100 ng scFv, 1000 ng VH, and 1000 ng VL were used, the EC50 values of scFv, VH, and VL were 0.325 \(\pm\) 0.021 ng/\(\upmu\)L, 1.127 \(\pm\) 0.144 ng/\(\upmu\)L, and 0.429 \(\pm\) 0.144 ng/\(\upmu\)L, respectively (Figure 3B-D). The limit of detection (LOD) values for scFv, VH, and VL, were 0.026 ng/\(\upmu\)L, 0.523 ng/\(\upmu\)L, and 0.077 ng/\(\upmu\)L, respectively. The binding efficiencies of 100 ng VH and 100 ng VL were lower than the binding efficiency of 100 ng scFv. This finding was as predicted because sdAbs only have one binding site as opposed to two of scFvs. However, when we used increased amounts of VH and VL (1000 ng), those EC50 and LOD showed similar values to the 100 ng scFv, indicating the usefulness of VH and VL as antibody fragments for detecting PD-L1 with high sensitivity.  
Figure 2: **(A)** Schematic representation of DNA plasmids and antibody fragments. L, H, and F indicates GS linker, His-tag, and Flag-tag, respectively. **(B)** Fractions at the purification step of anti-PD-L1 scFv. M, T, S, F, W, W’, and numbers indicates marker, total reagent after sonication, supernatant after centrifugation of sonicated protein, flow through fraction, washing fraction using a buffer including no imidazole, washing fraction using a buffer including 20 mM imidazole, and the order of eluted fractions using a buffer including 250 mM imidazole, respectively. **(C)** SDS-PAGE analysis of purified scFv, VH, and VL after buffer changing using an ultrafiltration.
# Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests
## 4 Discussion
Also, considering the uncertainty band of the sensors, it is possible to evidence that some events produced an important tension variation on the screw, at least on some of them and particularly during the tests performed on the second day. * FBG for temperature--Since the temperature increased all day, the FBG sensors in the other fibers responded to the temperature. The noise contributions in this temperature measurement originate mostly from the FBG interrogator. This causes a noise band up to \(\sim\)0.3 \({}^{\circ}\)C. Resampling of the data to a lower frequency could reduce the noise band. Other noise contributions to the temperature sensor are negligible since the temperature sensor is mounted without strain. FBG sensors react to temperature and strain, so drift in the strain measurements can originate from temperature changes. However, is it possible to correct this when the temperature is known or when a second FBG is used as a temperature sensor for more accurate results? The obtained results validate the feasibility of integrating FBGs into the building envelope to measure temperature, aiming to prevent thermal shock damage. In comparison to conventional sensors such as thermocouples, FBGs offer a valuable alternative due to their compact dimensions, both for the sensor and connectors, and the ability to incorporate multiple tens of sensors in a single fiber. Similar considerations could be applied to the following:
* FBGs for strain and vibration--The obtained results could be compared to those obtained from the conventional force washers. Indeed, the outcomes show the strain and vibration registered during the test provoked by accidental impacts or dynamic pressure. The FBG integration within the facade represents a potential solution due to their characteristic with a particular focus on their dimensions compared to conventional force washers, which have larger dimensions. FBG sensors are solid-state sensors of glass and have no moving or active components. Therefore, the lifetime of the sensors is not critical. Furthermore, no effects are expected on the structural integrity since the sensors are passive and the fiber is less than 1 mm in diameter. * With respect to the state of the art, this paper presents a methodology based on the use of different types of sensors for measuring a wide number of physical parameters to make identifying possible damages in the SHM field more robust.
# RACF: A Multimodal Deep Learning Framework for Parkinson's Disease Diagnosis Using SNP and MRI Data
## 2 Materials and Methods
### Multimodal Fusion Strategy  
The multimodal deep learning framework (RACF) in this study employs an advanced fusion strategy that effectively combines SNP and MRI data, thereby enhancing the performance of PD classification. The fusion strategy within this framework primarily consists of feature alignment, cross-modal attention mechanisms, and contrastive learning. Through the collaborative operation of these modules, the model is able to better capture the latent associations between the different modalities. The entire experimental flowchart is shown in Figure 3.  
Figure 1: SNP Feature Extraction Architecture Diagram.  
Figure 2: MRI Feature Extraction Architecture Diagram.  
Feature Alignment: In the initial stage, the features from SNP and MRI data are aligned through linear projection. Since the feature dimensions of SNP data and MRI images differ, a linear transformation is applied to map them into a unified feature space. This ensures that the features from both modalities can be fused at the same level. This process can be expressed mathematically as shown in Formula (3).  
\[\mathbf{f^{\prime}}_{\text{mri}}=\mathbf{W}_{m}\mathbf{f}_{\text{mri}}+\mathbf{ b}_{m},\quad\mathbf{f^{\prime}}_{\text{snp}}=\mathbf{W}_{s}\mathbf{f}_{\text{ snp}}+\mathbf{b}_{s} \tag{3}\]  
Cross-modal Attention Mechanism: After feature alignment, the model concatenates the SNP and MRI features to form a joint feature representation. The concatenated features are then processed using a 4-head attention mechanism, employing a multi-head attention strategy to model the interactions between the different modalities. The attention mechanism allows the model to assign varying weights to the features from each modality, thereby focusing on the most crucial information for the classification task, which further enhances the performance of the model. This process is illustrated in Formula (4).  
\[\text{Attn}(\mathbf{F})=\text{MultiHead}(\mathbf{F},\mathbf{F},\mathbf{F}) \tag{4}\]  
Contrastive Learning: During the fusion stage, we also introduce a contrastive learning module, which maps the features to a shared space through projection heads and optimizes the model using contrastive loss. This approach enables the model to effectively learn both the common and discriminative features across different modalities. Contrastive learning facilitates the fine-grained fusion of features between modalities, enhancing the model's ability to perceive latent disease characteristics. The contrastive loss function is shown in Formula (5).  
\[\mathcal{L}_{\text{contrast}}=-\log\frac{\exp\bigl{(}\sin(\mathbf{z}_{\text{mri} },\mathbf{z}_{\text{smp}})/\tau\bigr{)}}{\sum\limits_{k=1}^{K}\exp\Bigl{(} \sin(\mathbf{z}_{\text{mri}},\mathbf{z}_{\text{smp}}^{(k)})/\tau\Bigr{)}} \tag{5}\]
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 4 Discussion and Outcomes
### Outcome of Ultrawideband Notch Antenna
#### 4.2.2 Current Distribution  
Figure 22 depicts the surface current distributions at notched frequencies of 5.3 GHz and 8.1 GHz with parasitic resonators, illustrating the role of parasitic inverted-U-shaped and iron-shaped resonators in achieving the notched bands. At notched frequencies, surface currents on the elliptical patch flow opposite to the surface current on the ground plane, which contains parasitic elements. Accordingly, the radiation has little to no effect at the notch frequencies. It makes two notches at around 5.3 GHz and 8.1 GHz.
## 5 Results and Discussion
### Analysis of the Relative Importance of Input Parameters on AOp Generation  
To predict the AOp from construction rock excavation blasting, the significance of the influential parameters of the model to the AOp was investigated to avoid developing a complex model with little or no practical value. In this study, the cosine amplitude method (CAM) [49] was adopted to investigate the effects of the nine input parameters on AOp. The relevance importance between the parameters and the AOp is calculated as shown in Equation (28).  
\[RI=\frac{\sum_{i=1}^{n}{(X_{i}Y_{i})}}{\sqrt{\sum_{i=1}^{n}{X_{i}}^{2}{\sum_{i= 1}^{n}{Y_{i}}^{2}}}} \tag{28}\]  
where \(RI\) denotes the relative significance of each input parameter, \(X_{i}\) denotes the set of the nine input parameters, \(Y_{i}\) denotes the output of the model (AOp), and \(n\) is the total number of employed datasets.  
Based on the calculated relevance importance scores for the parameters, the significance of each model input parameter on AOp generation from construction rock excavation blasting was appraised, as shown in Figure 10. From Figure 10, it can be concluded that all model input parameters have significant relevance to AOp. Therefore, these parameters must be considered when predicting the AOp at construction sites. It should also be noted that the rock mass rating (RMR) and powder factor (PF) have the most and least impact on the AOp, respectively. The two important parameters usually employed when planning a blasting round at construction sites (charge per delay (Q) and monitoring distance (DIS)) also have significant relevance to AOp, with RI values of 0.9797 and 0.9564, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Weights**} & \multicolumn{8}{c}{**Biases**} \\ \cline{2-13}  & \(B\)
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 5 Performance Analysis
### Network Performance Analysis
#### 5.2.2 Average Delay  
In this section, we compute the average delay versus the voltage for the overall situations. With Wireshark capture files, we have the information about the delta time from the previously captured frame and the time reference since the first frame. The average delay defines the average sum of the difference delay of each data packet received by the sink and the time the source sends a data packet. It is calculated as follows:  
\[A_{d}=\frac{\sum_{i=1}^{N_{P}}(T_{ri}-T_{ei})}{N_{P}} \tag{7}\]  
where \(T_{ri}\) is the time when a packet \(i\) is received by the sink, \(T_{ei}\) the time when a packet \(i\) is sent by the source and \(N_{P}\) corresponds to the total number of the received packets. In this case, we limit \(N_{P}\) to 100.  
The obtained results are depicted in Figure 7. The results for LOS configurations are in Figure 7a. As can be seen, the average delay increases with the distance. The average delay for the LOS configuration is equal to 0.4 s. We can also note that the 16 kV have the most significant delay in the first case and the lowest one when we increase the distance. Another remark is that the average delay is less important for the LOS situation than the NLOS situation, as expected. This can be explained by the fact that in the NLOS situation, we have the delay due to the multi-path phenomenon.  
The results for the NLOS configuration are depicted in Figure 7b. However, we can note that when the distance increases, the delay decreases, contrary to the LOS situation. The worst delay is obtained when the voltage is equal to 12 kV for 2 m between the transmitter and the receiver. This is expected and can be explained by the IO graphics in which the received packets are more important in LOS than those obtained in NLOS. The average delay for the NLOS situation in all configurations is equal to 0.67 s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Voltages**} \\ \hline
**PDR** & **12 kV** & **14 kV** & **16 kV** & **18 kV** \\ \hline \(PDR_{\text{LOS1}}\) & 0.45 & 0.43 & 0.39 & 0.44 \\ \(PDR_{\text{LOS2}}\) & 0.31 & 0.33 & 0.35 & 0.32 \\ \(PDR_{mean_{\text{LOS}}}\) & 0.38 & 0.38 & 0.37 & 0.38 \\ \hline \(PDR_{\text{NLOS1}}\) & 0.25 & 0.3 & 0.29 & 0.28 \\ \(PDR_{\text{NLOS2}}\) & 0.32 & 0.32 & 0.31 & 0.34 \\ \(PDR_{mean_{\text{NLOS}}}\) & 0.28 & 0.31 & 0.3 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Packet delivery ratio for all voltages in different configurations.
# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
## 8 Conclusions  
Public lighting is currently the core of many smart city initiatives around the world. By replacing traditional street lights with LED-based lamps, utilities can cut energy and operations costs by 46% or more. Further savings can be achieved introducing an adequate dimming control during offpeak hours as well as by networking streetlights, also introducing the capability of sensing actual conditions (traffic, weather, luminance conditions) and dimming lights accordingly. Enabling such functionalities, thus turning the lighting infrastructure into a smart one, could lead to an overall saving of 80% or more.  
Beyond energy savings, since each smart streetlight needs to be provided with bidirectional communication capabilities, an ubiquitous city-wide communication network arises from the lighting infrastructure. Streetlights are no longer isolated elements, but could establish a capillary, multifunctional, city-wide communication network, capable of relaying information, gathering data and delivering services to and from millions of IoT devices. Streetlights could thus support city-wide IoT services, which will make them key enablers of the smart city revolution.  
In this paper we provided a thorough discussion on network architectures and communication technologies that could be adopted for this application, showing the benefits and downsides of each. In particular, we argued that the realization of a private network with a mesh topology, although more challenging for the infrastructure holder, is more suited to support both the smart lighting service and additional IoT services. Moreover, we outlined the steps required for the deployment of a smart public lighting infrastructure, each discussed in accordance with the network topology under consideration. Again, we showed that mesh networks are less prone to coverage issues and more responsive when real-time dimming is required.  
Finally, we introduced some of the additional services that a smart public lighting infrastructure could support and we discussed the benefits that would arise from the integration with the upcoming 5G cellular network.  
The first author wrote the paper, with the contributions of the other authors for specific topics and sections. All authors contributed to the text revision.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BLE & Bluetooth Low Energy \\ BMN & Bluetooth mesh networking \\ CLO & Constant Light Output \\ ICT & Information and Communications Technologies \\ IoT & Internet of Things \\ LoRa & long-range \\ LoRaWAN & LoRa wide area network \\ LC & ligh controller \\ LPWAN & low-power wide-area network \\ LTE & long-term evolution \\ MAC & medium access control \\ MF & Maintenance Factor \\ NB-IoT & narrowband-IoT \\ PAN & personal area network \\ PLC & power line communication \\ WAN & wide area network \\ WPAN & wireless personal area network \\ WSN & wireless sensor network \\ \end{tabular}
# Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires
## 3 Sensor-Based Network Weight Update Methodology  
### Calculation of the Equivalent Length of a Roadway  
When a disaster occurs underground, the planning of the evacuation route should not only consider the length of the roadway, but also the conventional properties that affect the traffic efficiency, such as the roadway type, slope, width, and other structures that affect traffic. It should also consider whether there is smoke flow in the roadway and the smoke flow's concentration, temperature, visibility and other properties during the disaster period. In order to comprehensively measure the impact of these parameters on the difficulty of roadway, the ratio of the roadway speed when affected by a certain factor to the roadway speed when not affected by a certain factor is used to define the roadway difficulty coefficient of a certain factor [31]. The formula is as follows:  
\[k_{attr}=\frac{v_{attr}}{v_{norm}} \tag{1}\]  
where \(k_{attr}\) is the travel difficulty coefficient affected by a certain factor. \(v_{norm}\) is the speed when it is not affected by a certain factor. \(v_{attr}\) is the speed of traffic affected by a certain factor.  
The equivalent length of a roadway is used to measure the efficiency of a roadway under the influence of all factors. The main factors affecting the efficiency of the roadway are combined and converted into the accessibility coefficient and the equivalent length of the roadway is obtained by multiplying the actual length by the accessibility coefficient and adding the sum of the equivalent lengths of the local obstacles inside the roadway. The equivalent length of the roadway \((i,j)\) from node \(i\) to node \(j\) is expressed as \(w_{ij}\), then the equivalent length of the roadway is given by,  
\[w_{ij}=k_{h}k_{w}k_{g}k_{v}k_{s}l_{ij}+\sum_{m=1}^{n}l_{m} \tag{2}\]  
where \(k_{h}\) is the travel difficulty coefficient of the roadway height. \(k_{w}\) is the travel difficulty coefficient of the roadway width. \(k_{g}\) is the travel difficulty coefficient of the roadway slope. \(k_{v}\) is the travel difficulty coefficient of the wind speed in the roadway. \(k_{s}\) is the visibility difficulty coefficient of the roadway. \(l_{ij}\) is the actual length of the roadway, m. \(n\) is the number of obstacles in the roadway from \(i\) to \(j\). \(l_{m}\) is the equivalent length value of the obstacle in the roadway from \(i\) to \(j\), m.