esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Interactions of P13#1 with SDS, LPSs and Bacterial Cells
The samples were excited at 430 nm to selectively excite the phenolate form of luciferin, and the fluorescent emission was recorded in the 450-700 nm range. The total fluorescence was plotted as a function of the peptide/peptoid concentration, and the experimental data were fitted to a model equation. The fitting  
Figure 7: Emission spectra of aLuc-GKY20 and aLuc-P13#1 in the presence of SDS, LPSs or bacterial cells. (**A**,**B**) Emission spectra of aLuc-GKY20 and aLuc-P13#1, respectively, in sodium phosphate (NaP, blue line), SDS (red line) and LPSs (green line). (**C**,**D**) Emission spectra of aLuc-GKY20 and aLuc-P13#1, respectively, in NaP (blue line) or in the presence of _E. coli_ (red line) _P. aeruginosa_ (green line) and _S. aureus_ (yellow line) cells. In each panel, the emissions in the presence of SDS, LPSs and bacterial cells were normalized to the emission in NaP. procedure provided the K\({}_{\rm D}\) values for the peptoid/LPS complexes and, in the case of the _E. coli_ LPS (whose average MW is known), also provided an estimate of the binding stoichiometry (Table 3). The K\({}_{\rm D}\) values show that the labeled P13#1 and GKY20 bind LPSs from _E. coli_ and _P. aeruginosa_ with similar affinities. Interestingly, P13#1 and GKY20 bind submicellar LPSs with slightly higher affinities than micellar LPSs. This difference could be due to fact that submicellar LPSs are more accessible to peptides/peptoids than micellar LPSs. This explanation is supported by the binding stoichiometries: in the micellar state, 1-1.5 molecules of peptide/peptoid bind one LPS molecule, whereas in the case of a submicellar LPS, 4.4-5.2 molecules of peptide/peptoid bind one molecule of the LPS.
## 3 Weak Adhesion Detection and Classification
### Simulation Data  
The experimental setup is composed of two substrates with dimensions \(120\times 350\times 2\) mm and the same adhesive structure previously described in Section 2.2. There is also one actuator located on the top substrate at a distance of 50 mm from the edge. On the lower substrate, there are five sensors equally positioned, as can be seen in Figure 10.  
As the objective is to detect damage without any dependence on its location, the damaged section with a width of 25 mm was moved in increments of 1 mm in the adhesive area, always keeping a minimum distance of 50 mm from the edges to reduce the effect of the reflections on the acquired data. In Figure 11, it is possible to see the overall size, limits, and positioning of the weak adhesion region.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**77 Exp, 15\% Noise**} & \multicolumn{2}{c}{**22 Exp, 15\% Noise**} & \multicolumn{2}{c}{**22 Exp, 5\% Noise**} & \multicolumn{2}{c}{**49 Exp, 5\% Noise**} \\ \hline  & **Simu + Exp** & **Exp** & **Sim + Exp** & **Exp** & **Sim + Exp** & **Exp** & **Sim + Exp** & **Exp** \\ \hline Case 1671 N & 288.5 & 465.2 & 470.0 & 462.8 & 415.6 & 450.9 & 470.3 & 468.5 \\ \hline Case 1902 N & 205.4 & 144.3 & 235.2 & 288.7 & 272.9 & 306.8 & 228.6 & 169.9 \\ \hline Case 2465 N & 42.3 & 134.2 & 94.3 & 256.3 & 100.4 & 259.0 & 82.0 & 140.7 \\ \hline Case 2587 N & 215.2 & 154.7 & 177.6 & 288.9 & 214.5 & 276.5 & 224.7 & 200.4 \\ \hline Case 3192 N & 24.9 & 146.6 & 134.7 & 283.9 & 76.6 & 266.2 & 136.7 & 241.1 \\ \hline Case 4562 N & 197.7 & 211.8 & 176.2 & 31.3 & 209.7 & 127.5 & 185.0 & 141.1 \\ \hline Case 5291 N & 350.8 & 161.0 & 299.5 & 187.1 & 279.3 & 195.9 & 232.1 & 162.6 \\ \hline Case 5670 N & 253.8 & 253.1 & 163.3 & 99.1 & 65.0 & 292.5 & 163.1 & 247.9 \\ \hline Case 6678 N & 427.0 & 420.2 & 321.3 & 188.9 & 312.7 & 184.3 & 373.9 & 427.9 \\ \hline Case 6680 N & 195.4 & 417.3 & 203.0 & 188.5 & 181.8 & 284.6 & 185.2 & 355.8 \\ \hline Case 7301 N & 313.6 & 328.4 & 500.2 & 326.1 & 163.0 & 333.2 & 355.9 & 357.9 \\ \hline Average & 228.6 & 257.9 & 252.3 & 236.5 & 208.3 & 270.7 & 239.8 & 264.9 \\ \hline \end{tabular}
\end{table}
Table 2: Multiple tests conducted with different levels of simulated data and noise with the chosen Cartesian direction. All values are represented in MPa.
## 3 Materials and Methods
### Loss Function  
Sub-Center Loss [33] (shown in Figure 3) is a variant of the AAM Softmax loss function, with the primary difference being how the intra-class sample diversity and center points are handled. AAM Softmax defines a single center point for each class, causing all intra-class samples to cluster around that center, which is suitable for cases where intra-class samplesare tightly distributed and inter-class differences are significant. However, when intra-class sample variance is large, a single center point is insufficient to effectively represent the class diversity. In contrast, Sub-Center Loss introduces multiple sub-centers for each class, allowing intra-class samples to cluster around different sub-centers, thereby addressing intra-class diversity with finer granularity. This approach performs better, especially on data with complex distributions. Moreover, in the high-noise environment of air-ground communication, the single center in AAM Softmax is more susceptible to outliers, whereas Sub-Center Loss, with its multiple sub-centers, effectively mitigates the impact of noise interference, resulting in a model with greater robustness.  
Sub-Center Loss essentially introduces multiple sub-centers for each class to capture intra-class sample diversity with finer granularity. For the feature vector \(x_{i}\in R^{N\times 1}\) (where \(x_{i}\) is the \(i\)-th sample belonging to the \(y_{i}\)-th class and \(N\) is the dimension of the speaker embedding). The first step is to normalize it with respect to the sub-centers \(W\) using the \(L_{2}\) norm. By performing matrix multiplication, \(W^{T}x_{i}\), the similarity score \(S\) for each sub-center is obtained. Then, a max-pooling operation is used to select the highest similarity sub-center score \(S^{\prime}\) for each class, determining the correlation between the feature vector and that class's sub-center. Next, the angle \(\theta_{i,j}\) between the feature vector and the sub-center is calculated using \(\arccos(S^{\prime})\), and an angular margin \(m\) is added to \(\theta_{i,j}\) to adjust the boundary between classes. Finally, the normalized feature vector is multiplied by a scale factor \(S\) to further optimize the distribution of the sub-centers. The cross-entropy loss function is then used to measure the difference between the model's prediction and the true label, minimizing the loss to improve the classification performance of the Chrono-ECAPA-TDNN model. The specific calculation method is as follows, as shown in Equation (21):  
\[l=-\log\frac{e^{s(\cos(\theta_{i,j\
eq i}+m))}}{e^{s(\cos(\theta_{i,j\
eq i}+m ))}+\sum\limits_{j=1,j\
eq y_{i}}^{N}e^{s\cos\theta_{i,j}}}, \tag{21}\]  
where \(\theta_{i,j}=\arccos(\max_{k}(W^{T}_{jk}x_{i})),k\in\{1,\dots,K\}\).
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 4 System Model
### Downlink Model  
Consider that the set of MSs that operate in _time-division duplexing_ (TDD) mode with the F-RANs, with _reciprocal channel state information_ (CSI) knowledge. The downlink received signal model in the RF domain \(Y_{\rm an}\) at the MS is modeled as [29],  
\[Y_{\rm an} =V_{\rm RH}{\bf Hz}+w, \tag{3}\]  
where the variables \({\bf H}\), \(z\), and \(w\) represent the complex channel, control signal, and additive white Gaussian noise (AWGN), respectively, where \(w\sim\)\(N(0,\ \sigma_{w}^{2})\), with variance \(\sigma_{w}^{2}\). Here, the variable \(V_{\rm AP}\) denotes the beamforming matrix at the F-RAN. Furthermore, the received signal \(Y_{\rm bb}\) at the MS subsequent to the combiner section, \(C_{\rm MS}\), is expressed as,  
\[Y_{\rm bb} =P_{\rm tr}C_{\rm MS}^{\rm H}V_{\rm RH}{\bf Hz}+C_{\rm MS}^{\rm H}w, \tag{4}\]  
where the variables \(P_{\rm tr}\) and \(C_{\rm MS}\) denote the transmitted signal power and the MS combiner, respectively, and where \(C_{\rm MS}\) includes baseband and analog combiners, such that \(C_{\rm MS}=C_{\rm bb}C_{\rm an}\). Furthermore, the instantaneously received signal \(Y_{inst}\) at the MS due to \(p\) beamforming and \(c\) combining vectors, where \(v\in V_{\rm RH}\) and \(c\in C_{\rm MS}\), is written as,  
\[Y_{inst} =P_{\rm tr}C_{\rm MS}^{\rm H}v{\bf Hz}+c^{\rm H}w \tag{5}\]  
In general, the poor scattering propagation nature at mmWave bands imposes the use of geometric channel models, written as [30, 31],  
\[{\bf H}\ =\ \sqrt{\frac{A_{\rm RH}A_{\rm MS}}{\Gamma_{bl}}}\sum_{l =\ 1}^{L}h_{l}V_{\rm RH}C_{\rm MS}^{\rm H}, \tag{6}\]  
where the variable \(\Gamma_{bl}\) and \(h_{l}\) in order denote the blockage path loss and the complex gain of the \(l\)-th path for \(L\) number of paths captured in \(K\) clusters. Note that path gains here are also assumed to be Rician-fading, i.e., \(h_{l}\sim\)\(R(0,\ \zeta)\), where \(\zeta\) represents the ratio between power in the first path and other paths that possess reduced power levels. Moreover, the variables \(V_{\rm RH}\) and \(C_{\rm MS}\) represent the response vectors that capture the channel at the F-RAN and MS, respectively. Therefore, the overall response vector for the beamformer at the MS is given by the RF precoding matrix and is computed by the periodic array factor for the UCA at \(\sfrac{\rm MS}{s}\) azimuth and \(\phi_{\rm s}^{\rm MS}\) elevation pointing directions for each section (likewise for the F-RAN).
## 2 Materials and Methods  
### Vineyard Site, Cluster-Thinning Treatments, and Sampling  
The experiment was conducted during the 2018 season on 'Muscat Hamburg' (_Vitis vinifera_ L.) vines located in the Sino-French Joint Venture Dynasty Winery (116\({}^{\circ}\)23\({}^{\prime}\) E, 39\({}^{\circ}\)54\({}^{\prime}\) N; Tianjin, China). These own-rooted grapevines were planted in 2008, trained on a single Guyot and spaced at 2.8 m (row) \(\times\) 2.0 m (vine). The vineyard was subjected to standard management practices including fertilizer applications, irrigation, pruning, and pesticide application management for the region and cultivar during the growing process.  
Three cluster-thinning treatments, 0.7 cluster per shoot remaining (L), one cluster per shoot remaining (M) and 1.5 cluster per shoot remaining (H) were applied. Cluster-thinning treatments were manually conducted at the pepper-corn stage, E-L 29, which occurs approximately two weeks after flowering. A randomized block design including three replicates of each treatment was used in this experiment, and each replicate contained a panel of 30 vines with similar growth vigor.  
The grape samples were collected at the following seven stages: E-L 34 (berries begin to soften; Brix starts increasing); E-L 35 (berries begin to color and enlarge); E-L 35.5 (approximately eight days before E-L 36); E-L 36 (berries with intermediate Brix values); E-L 37 (berries not quite rip); E-L 37.5 (approximately eight days before harvest); and E-L 38 (berries are harvest-ripe). The E-L stages were as described in [PERSON] et al. [22]. For each replicate, 500 berries were randomly collected at 10 AM on each sampling date. After quickly freezing in liquid nitrogen, all the samples were immediately stored at \(-80\)\({}^{\circ}\)C for subsequent RNA extraction and metabolite analyses.
## 5 Review Findings
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref.** & **Source** & **Year** & **Purpose** & **Level** & **Procedure** & **Performance Evaluation** & **Evaluation Parameters** & **Cryptography Approach** & **Key-Based** & **Security Validation** \\ \hline
[46] & Springer link & 2016 & RFID-based authentication for healthcare in mobile vehicular applications & Network & Two-way simulator & Computation overhead, complexity, and service delivery. & ECC & Asymmetric & Informal \\ \hline
[68] & Science Direct & 2017 & A lightweight, and robust two-factor authentication for personalized healthcare systems in wireless-sensor networks & Network & Two-way & NS-3 & Delivery ratio, throughput, delay, communication cost, practicality & Dynamic & Dynamic & Informal, Pseudorandom \\ \hline
[95] & Google scholar & 2017 & Authentication framework for pervasive healthcare services & User & Three-way simulator & Communication computation, storage overhead & ECC & Asymmetric & Informal \\ \hline
[121] & Springer link & 2018 & Efficient, anonymous, and secure authentication in mobile three-tier healthcare with wearable medical devices & Network & Three-way simulator & Communication computation, storage overhead & ECC & Asymmetric & Informal, BAN logic, AVSPAR \\ \hline
[93] & Springer link & 2019 & Authenticized key agreement in fog-based IoT network & Network & Three-way platform & Computation and communication costs & Bilinear pairing & Asymmetric & Informal and formal proof \\ \hline
[69] & Science Direct & 2019 & Robust and secure authentication problem with the implementation of Field-Programmable Gate Array & Network & Two-way simulator & Alterna Quartus II simulation & Computation and communication overhead & ECC & Asymmetric & Informal, BAN logic \\ \hline
[24] & Science Direct & 2019 & Anonymous and content-aware authentication protocol for IoT-healthcare systems & Device & One-way simulator & N22.35 simulator & Computation and communication overhead, energy consumption & Unlinkable shadow-IDs & Symmetric & Informal, BAN logic, ROR, Sydney tool \\ \hline
[75] & IEEE & 2019 & Authentic-enabled privacy scheme for smart-healthcare applications using IoT & User & Two-way & NS-3 simulator & end-to-end delay, throughput rate, routing, and delivery ratio & ECC & Asymmetric & Informal, ROM, BAN logic \\ \hline
[42] & Science Direct & 2020 & Two-factor authentication for healthcare services in wireless medical-sensor networks & Network & Two-way & OPNET & Computation and communication overhead & Hash function & N/A & ROR, Proverif \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of authentication schemes based on the taxonomy.
## 3 Bridges Analyzed and Method of FE Analysis  
### Bridges Analyzed and Study Parameters  
The bridge selected for analysis in this study is the Manzeki Bridge, located in Nagasaki prefecture, Japan. The main span and rise of this half-through type arch bridge are 113 m and 24 m, respectively. Box sections are adopted for arch rib and lateral bracing, and I-sections are adopted for longitudinal and cross girders and hangers. It employs quadratic parabola arch axes (see Figure 6). In order to reveal the differences between provisions in the codes for different bridge types, the girder framework of this bridge is translocated in the models to simulate the other two bridge types (through-type and deck-type) while keeping the other parameters constant.  
Figure 5: Buckling Length Factors \(\beta\): (**a**) Arch Springing Hinged; (**b**) Arch Springing Fixed; and (**c**) Arch Springing with Wind Brace.  
In the major design codes, the out-of-plane critical axial force of the arch ribs is specified instead of the critical flexure load. Therefore, the out-of-plane stability of a steel arch bridge is studied in this paper by focusing on the critical axial force. Since the influences of stiffness ratio, bridge type, rise-to-span ratio, range of lateral bracing arrangements, and arch rib spacing are concurrently provided in most major design codes, the effect of these parameters on critical axial force according to the various codes is evaluated by FE analysis. The stiffness ratio is the value of out-of-plane flexural stiffness of the arch ribs divided by the flexural stiffness around the horizontal axis of the lateral bracing; this is abbreviated as SR in the figures in the following sections.  
To reveal the influence of stiffness ratio on critical axial force, three different arch rib and bracing cross sections are used, as shown in Table 2, and the nine cases shown in Table 3 are studied to discuss the influence of rise-to-span ratio, range of lateral bracing arrangements, and arch rib spacing on critical axial force for each type of arch bridge. These cases will be identified in the form AmBn in the figures in the following sections, where A and B are the abbreviations of the arch rib and bracing, respectively, and m and n are the cross-section types of the arch rib and lateral bracing, respectively. Taking structural aesthetics and reasonable engineering into account, the rise-to-span ratios of the three bridge types modeled here were set between 0.15 and 0.5 by changing the arch rise. The range of lateral bracing arrangements is represented by the \(\gamma\)-value shown in Figure 7, which is the ratio of arch rib length provided with lateral bracing to the longness of arch.  
Figure 6: Photo of Manzeki Bridge.
## 8 Discussion
### Possibility for a Scalable IWSN  
An IWSN may include over 100 end devices such as sensors or actuators [30], though we evaluated our proposal in Section 7 under a condition that an IWSN includes only 10 end nodes. To evaluate scalability of our proposal scheduler, we increased the number of nodes from 10 to 100. At the same time, we expand polling cycles because our proposal data traffic control scheme has a TDMA-like manner. To be more precise, we enlarged the length of a QF 10 times because the length of a QF is \(\Delta T_{QF}(=\Delta t_{s}\times N_{all})\) as described in Section 4. Accordingly, we also enlarged the length of a SF 10 times. Simulation conditions are described in Table 4. Figure 16 shows that our proposal also provides a solution which has a scalability that is adaptive to a small scale network to a large scale one with the same uniformity.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Item** & **Notation** & **Value** \\ \hline The number of nodes & \(N_{all}\) & 100 nodes \\ Data collection cycle 1 & \(T_{1}\) & 20 min \\ Data collection cycle 2 & \(T_{2}\) & 40 min \\ Data collection cycle 3 & \(T_{3}\) & 80 min \\ The least common multiple of collection cycles & \(T_{M}\) & 80 min \\ The number of slots in a QF & \(S_{QF}\) & 100 \\ Slot length & \(\Delta t_{s}\) & 3 s \\ QF length & \(\Delta T_{QF}\) & 300 (\(S_{QF}\times\Delta t_{s}\)) s \\ The number of QFs in a chromosomes & \(N_{QF}\) & 160 (\(T_{M}\div T_{QF}\)) \\ The number of slots in a chromosomes & \(S_{gene}\) & 1600 (\(N_{QF}\times S_{QF}\)) \\ The number of individuals & \(N_{indiv}\) & 20 \\ MAX iteration of crossover operation & \(I_{max}\) & 20,000 times \\ Packet error rate & \(PER\) & 0–20\% \\ Max retry count & \(R_{max}\) & 2 \\ Threshold of a path quality & \(Th_{path}\) & 90\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation conditions.  
Figure 15: E2E communication ”3”y of all nodes in the top 20 individuals and average one of all nodes in each individual, where \(N_{1}=8,N_{2}=1\), and \(N_{3}=1\).
## 4 Validation and Discussion
### \(D\) Tunnel Stability Analysis: \(L/d\geq 0.5\)  
#### 4.2.1 Assumptions and Theory  
In 3D conditions, the tunnel stability would be affected by the tunnel face and then the stability number is relevant to \(L/D\). The 3D tunnel behaviour would roughly lie within the contraction of a spherical cavity and a cylindrical cavity. For simplification, we assume that:  
1. Equation (18) still holds in 3D conditions, but the shape factor \(k\) varies with \(L/D\). Although the collapse mechanism for a tunnel heading is much more complicated than that for a 2D tunnel, Equation (18) is still adopted for 3D tunnel stability analysis because of its simplification, clearness and sound theoretical basis.
2. The tunnel heading stability is exactly modelled by a spherical cavity contraction (\(k=2\)) when \(L/D=0.5\). With \(L/D\) increasing from 0.5 to \(\infty\), the tunnel collapse model would change from a spherical cavity to a cylindrical cavity and the shape factor would decrease from 2 to 1.
3. The shape factor with \(L/D\) is determined following [PERSON] [36], who investigated the ellipsoidal cavity expansion problem and presented a similar expression of \(k\) with the length-to-width ratio of a rectangular (i.e., \(L/D\) in the present paper) by the conformal mapping method. The relationship between \(k\) and \(L/D\) can be written as: \[k=2-\frac{L/D-0.5}{L/D+0.5},L/D\geq 0.5\] (19) and we chose this relationship mainly because there is only this equation available in the literature without losing simplification. Under the above assumptions, the 3D tunnel stability (\(L/D\geq 0.5\)) can then be assessed by substituting Equation (19) into Equation (18).  
Figure 3: Comparison of predicted stability numbers with 2D centrifuge model tests and FELA results: (**a**) model tests; (**b**) FELA results.
## 2 Background
### Self-Adaptive Systems  
A self-adaptive system is a closed-loop system with a feedback loop intended to adjust to runtime changes [25]. To develop a self-adaptive system suitable for changes in software variability, understanding the nature of the system will help determine the process change and the factors affecting the change [26].  
[PERSON] proposes two principles for determining what a self-adaptive system is [1]:  
* External principle: A self-adaptive system can autonomously manage changes and uncertainties due to the requirements of the environment, the system itself and its objectives.
* Internal principle: A self-adaptive system contains two components for managing communication with and monitoring of the environment, as well as for executing changes in runtime variability.  
As self-adaptive systems increasingly require autonomous behavior, runtime variability mechanisms must provide multiple binding options after implementation. The binding options can be satisfied using DSPLs. The mentioned conditions address the challenges of creating highly configurable software using runtime variability mechanisms to support automatic decision-making [27].
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 4 Experimental Setup  
In order to develop the linear network using LoRa technology and incorporate different relay widths, we undertook the task of assessing the potential coverage range. This assessment involved utilising two nodes and considering up to three hop counts. We used actual devices throughout the study (Figure 3 (configuring the relay node during the experimental trial)). The subsequent section discusses the transmission coverage estimation and design possibilities. An aerial view of the network deployment is shown in Figure 4.  
Figure 3: Configuring relay node during the experimental trial.