esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Thermal Comfort in the Context of Office Chair  
The interaction between a user's body and a chair affects comfort, which is influenced by factors such as contact area, posture, and duration of sitting. The thermal comfort of a user is closely linked to the temperature at the interface between the body and the chair, with studies indicating that comfort is often achieved when the seat pan temperature is lower than the user's body temperature [21].  
The effects of seat materials on heat retention, dissipation, and moisture management are influenced by the thermal properties and structure of the material. For instance, foam materials have low thermal conductivity, allowing them to quickly accumulate heat and act as insulators. This characteristic can lead to a rapid increase in skin temperature within a short period. Despite their insulating properties, foam cushions may allow some air circulation due to their open-cell structure which can help in moisture dissipation, although this effect might not be significant without frequent movement [22; 23]. Gel materials, known for their higher thermal conductivity, can conduct heat away from the body more efficiently, potentially enhancing thermal comfort by reducing heat retention. Mesh chairs, with lower insulation values, may offer better heat dissipation due to their structure, which allows for more airflow than solid materials [10].  
In contrast, materials like leatherette may trap moisture, increasing the risk of discomfort and skin-related issues [10; 23; 24]. Infrared thermography studies [25] have shown that different chair materials can lead to varying heat retention and dissipation rates. The use of advanced materials and technologies, such as thermoelectric devices in heated/cooledchairs, can actively regulate the surface temperature of the chair, providing a more consistent thermal environment and enhancing comfort in both warm and cool conditions [26].  
The need for ergonomic design, including features like adjustable armrests and lumbar support, that adapts to different positions and activities by promoting proper posture and reducing musculoskeletal strain, can affect the body surface available for heat exchange and user's thermal comfort [21; 25; 27; 28]. The sitting posture and contact area between the body and chair can modify the effective thermal insulation of the chair, as they affect the distribution of heat and pressure, potentially leading to discomfort if not properly managed [9]. The duration of sitting is another important aspect, as prolonged sitting can lead to discomfort. Movements or posture changes are often a response to discomfort and can be influenced by the thermal properties of the seating surface [21; 25]. Gender differences have also been noticed, with women generally perceiving environments as cooler than men, which may affect their comfort levels differently during prolonged sitting [10].
## 5 Simulation Results
### Cost Indexes for Performance Assessment  
To better quantify the results to be shown in Section 5.3, four cost indexes will be used. These indexes evaluate control performance for every case simulated with the aim of making the comparison easier. The cost indexes are as follows:  
* \(J_{1}\), which is based on the \(\ell_{2}\)-norm, and its goal is to provide a measure (in meters) about how accurately the path is followed: \[J_{1}=\frac{1}{l}\sum_{k=1}^{l}\min_{1\leq k^{\prime}\leq l}\sqrt{\left(X_{k}^ {T}-\left(X^{ref}\right)_{k^{\prime}}^{T}\right)^{2}+\left(Y_{k}^{T}-\left(Y^ {ref}\right)_{k^{\prime}}^{T}\right)^{2}}\] (19) where \(l\) is the number of iterations at period \(T\) required by the vehicle to reach the final point of the path, \((X,Y)_{k}^{T}\) is the current vehicle position, and \((X^{ref},Y^{ref})_{k^{\prime}}^{T}\) is the nearest kinematic position reference to the current vehicle position. It is worth noting that, despite using a dual-rate control scheme, the position data may be available at period \(T\) (intersample behavior; see, e.g., [40]) in the simulator environment.
* \(J_{2}\), which is based on the \(\ell_{\infty}\)-norm and is defined to know the maximum difference (in meters) between the desired path and the current vehicle position: \[J_{2}=\max_{1\leq k\leq l}\left\{\min_{1\leq k^{\prime}\leq l}\sqrt{\left(X_{ k}^{T}-\left(X^{ref}\right)_{k^{\prime}}^{T}\right)^{2}+\left(Y_{k}^{T}-\left(Y^ {ref}\right)_{k^{\prime}}^{T}\right)^{2}}\right\}\] (20)
* \(J_{3}\), which measures the total amount of time (in seconds) elapsed to arrive at the final destination: \[J_{3}=lT\] (21)
* \(J_{4}\), which is based on the \(\ell_{2}\)-norm, and its goal is to provide a measure (in meters) about how accurately the estimation is made: \[J_{4}=\frac{1}{l}\sum_{k=1}^{l}\sqrt{\left(X_{k}^{T}-\widehat{X}_{k}^{T} \right)^{2}+\left(Y_{k}^{T}-\widehat{Y}_{k}^{T}\right)^{2}}\] (22) where \((\widehat{X},\widehat{Y})_{k}^{T}\) is the estimated vehicle position.
###### Abstract  
Compaction quality of railroad subgrade relates directly to the stability and safety of train operation, and the core problem of the Intelligent Compaction of railroads is the transmission and evolution characteristics of vibration wave. Aiming at the shortages in exploring the transmission and evolution characteristics of the vibration signal, the typical subgrade compaction project of Jingxiong  
Intercity Railway Gu'an Station was selected to carry out the field prototypes tests, and the dynamic response from the vibratory roller to filling materials was monitored in the whole compaction process, and some efficient field tests data will be obtained. Based on this, the transmission and evolution characteristics of the vibration wave from the vibratory roller to filling materials in the compaction process are studied from the time domain, frequency domain, jointed time-frequency domain and energy domain by using one new signal analysis technology--Hilbert-Huang Transform. Some conclusions are shown as follows: first, the vibration acceleration peak gradually decreases with the increase of buried depth, and when the buried depth reaches 1.8 m, the vibration acceleration peak is closed to zero. At the same time, when the vibration wave propagates from the wheel to the surface of filling, the attenuation rate of acceleration gradually increases with the increase of rolling compaction times, while the attenuation rate of other layers in different buried depths gradually decreases. Second, the vibration wave contains fundamental wave and multiple harmonics, and the dominant frequency of the fundamental wave is nearly 21 Hz. With the increase of buried depth, the amplitude of fundamental, primary, secondary, until fifth harmonics decreases exponentially and the concrete functional relationship among different amplitudes of harmonics can be summarized as y = Ae-BX. Third, the vibration energy focuses on the fundamental wave and primary wave, which can increase with the increase of rolling compaction times, and when the rolling compaction time reaches five, their energy reaches maximum. However, when the filling reaches a dense situation, the energy of the primary wave gradually decreases. Therefore, the maximum rolling compaction time is five in the practical engineering applications, which will be helpful for optimizing the compaction quality control models and providing some support for the development of the Intelligent Compaction theory of railway subgrade.  
high-speed railway; subgrade compaction; field test; nonlinear transmission mechanism; time-frequency characteristics +
Footnote †: journal: Applied Physics Letters  
_Appl. Sci._**2020**, _10_, 2008; doi:10.3390/app10062008
## 4 Influence Factors on Damage Location Identification
### Influence of Vehicle Speed on Damage Location Identification  
The change of the vehicle excitation frequency will affect the dynamic response of the vehicle-bridge coupling system. Naturally, it will have a certain impact on the bridge damage identification. When the vehicle speed is within a specific range, the vehicle acceleration signal changes drastically, and the wavelet transform coefficient peak value is obvious, which is beneficial to the location of bridge damage. The inspection vehicle weighed 1000 kg. The parameters of other vehicles and bridges remain unchanged, and the vehicle speeds were calculated as 10 m/s, 20 m/s, and 30 m/s, respectively, to study the influence of the speed change of the detected vehicle on the bridge damage identification. Working condition 3 in Section 3.3 (single damage; damage location: 20 m; damage degree 30%) was selected for calculation, and discrete wavelet transform was performed on the difference in acceleration response of the vehicle body to perform bridge damage identification, as shown in Figure 11.  
When the vehicle speed is 10 m/s, 20 m/s, and 30 m/s, the damage can be accurately identified, and as the vehicle speed increases, the wavelet transform peak value first decreases and then increases. In theory, the test vehicle can travel at a speed of 10-30 m/s. However, if the sampling rate of the sensor is considered, it is required to collect enough sampling signals in a single test run; therefore, it is suggested that the speed should not be too high; when the speed is at 20 m/s and 30 m/s, the wavelet coefficients near the support will affect the damage identification. In addition, if the speed is too low it will lead to insufficient bridge excitation. Considering detection speed and detection effect, a vehicle speed of 10 m/s is recommended in this article.  
Figure 10: Wavelet transform coefficients and partial enlarged diagrams under different vehicle weights.
# A Survey on Cyber Risk Management for the Internet of Things
## 4 Cyber Risk Assessment for IoT Survey Results
### IoT Cyber Risk Identification
#### 4.1.3 Identification of Threats
While existing threats are well documented, a major problem for IoT threat modelling is the consideration of unknown threats that may quickly emerge due to new technologies and improved attacker skills. For IoT, the new attack surface due to new functionalities and vast number of devices is unpredictable, with unexplored attack techniques and unknown attacks that may emerge [62]. [PERSON] et al. [49] suggest that new attack scenarios that were previously unknown are entered into a threat database, including attacks on specific IoT systems. IoT intrusion detection systems can aid in uncovering new attacks, with [PERSON] [56] taking an anomaly detection approach to detect attacks. Here, a baseline model of system behaviour is formed through off-line training, with this model being used to detect anomalous behaviours that deviate from the baseline, which triggers an alert. The author suggests that the proposed system can detect any attack as well as configuration and misuse with fewer false alarms. Meanwhile, the IDS from [PERSON] et al. [65,66] also carries the ability to detect known and unknown attacks. Flooding and replay attacks are used to train the system, then two new attacks not used in training, PulseDoS and HTTP GET, are used to test the detection ability [66]. The authors find that the framework can detect known and unknown IoT attacks with high detection rates and low false alarms [65,66]. _Insight 3:_ Another factor of RQ1 relates to identifying potential threats that exist for an IoT domain in an accurate fashion. The most common way to model IoT threats is using STRIDE [42,44,49,59,69,70, 42]. While effective, the use of well-known threat models may not allow for all threats to be uncovered, for example, STRIDE requires other models like DREAD and LINDDUN to uncover privacy risks, like within the work of [PERSON] et al. [71]. STRIDE and other well-known models are not explicitly for IoT, which may be an issue when finding an exhaustive set of threats within an IoT domain. However, with the influx of new technologies that benefit attackers and users, emerging and unknown IoT attacks make it near impossible for an exhaustive list of threats to stay the same. Such new threats need to fit into threat models easily to facilitate dynamic decision making despite the unpredictable new threats. Uncovering threats requires a good understanding of assets, users, and the needs they possess, where it is important to ensure that all potential threats towards assets and users are accounted for, with critical threats not being forgotten.
## 6 Network Architectures for IEC61850-90-5 Multicast over Public WAN
### P2M Architecture with mGRE in DMVPN  
R-SV/R-GOOSE messages can also be transmitted within DMVPN architecture, which can be implemented over either IP or IPsec. DMVPN builds Hub-and-Spoke topology in which the Hub router, the main central site, is connected to multiple remote sites (Spoke routers) via mGRE, as shown in Figure 18. DMVPN utilizes three [43] main technologies: mGRE, Next Hop Resolution Protocol (NHRP), and dynamic routing protocols to establish a DMVPN connection between the hub and spokes. The mGRE protocol enables the hub router to establish the GRE tunnel with multiple destinations to the spoke routers. While the regular GRE tunnel has a destination address, mGRE has no specific destination. The NHRP  
Figure 17: P2M architecture for multicast R-SV/R-GOOSE over public WAN (Internet) with separate GRE tunnels.  
is a client-server protocol used by the routers to inform each other's public IP addresses. NHRP supports authentication to ensure that only configured routers can communicate. The spoke routers (NHRP clients) register and report their public IP addresses to the hub router that is the NHRP server. The hub router keeps track of all public IP addresses (spoke routers) that want to be destination addresses of the mGRE tunnel. In order to run mGRE connections, both hub and spoke routers should also use a dynamic routing protocol such as Open Short Path First (OSPF), Routing Information Protocol (RIP), Enhanced Interior Gateway Routing Protocol (EIGRP), etc. Accordingly, mGRE connections efficiently enable multicast communication of R-SV/R-GOOSE from Substation 1 to Substation 2 and 3, as shown in Figure 18.  
Both proposed architectures, in Sections 6.1 and 6.2, can be used for transmitting multicast R-SV/R-GOOSE messages over public WAN in P2M scenarios. The second architecture (mGRE in DMVPN) is considered as the preferred solution with high scalability since any new router (spoke) can be added to the system without changing hub router configuration. However, implementing DMVPN requires extensive knowledge of computer networking standards. Table 5 shows a comparison of networking technologies that can be used in P2M architecture for both public and private WAN.  
Figure 18: P2M architecture for multicast R-SV/R-GOOSE over public WAN (Internet) with mGRE within DMVPN.
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
## 8 Experimental Results
### Experimental Results
#### 8.3.3 Results of Coordinated Approach with DSH  
Table 2 (columns \"DSH\") shows the respective number of solved instances for the _coordinated+DSH_ approach. Furthermore, Figure 7 includes the average runtime distribution between the answer set solver and the background theory for solved runs of instances.  
By comparing the solved instances between the _coordinated_ approach with the _coordinated+DSH_ approach we make two main observations. First and most obvious, the scaling by using DSH is significant better which is evident by looking at the \"0/5\" bin column starting at the 5 \(\times\) 5-70 instance set. Second, by looking at the instances where more runs of an instance where solved (\"5/5\" and \"4/5\" bin), noticeable more runs of instances were solved in the regime of harder instance sets, i.e., those sets with 70% platform utilization. Thus, by using our domain knowledge encoded in the DSH, we are able to make the coordinated synthesis approach even more scalable and more robust (since more runs of instances are solved).  
Regarding the time to derive a feasible system synthesis, by comparing the bars in Figure 7, the average time spend in the answer set solver and background theory solver is also always lower in the _coordinated+DSH_ approach, and therefore is the average total synthesis time (with the exception of the single data point (single solved run) for 6 \(\times\) 6-70-coord in Figure 7 which is neglected in our statement due to statistical relevance.)  
Note that the answer set solver does not report any overhead in terms of additional variables or constraints if domain-specific heuristics are used within the coordinated approach. As described in [17], the additional formulation of the domain-specific heuristics are only used if non-deterministic choices have to be performed during the solving process and have no immediate contribution to the underlying complexity to the problem that needs to be solved.  
While we also observed that the _coordinated_ approach can spend a considerable amount of time within the answer set solver (see Table 4), this issue does not occur anymore if we use domain-specific heuristics together with the coordinated approach. Furthermore, not one synthesis run reaches the memory limit anymore.  
Overall, our experiments show that the combined _coordinated+DSH_ approach enriched with DSH considerably increases the scalability of the solely _coordinated_ approach. Additionally, the average synthesis times are reduced. However, its limitations become more noticeable especially for the hardest instance set where almost half the instances in the 6 \(\times\) 6-70 set could not be solved.
## 4 Discussion
[29] demonstrated that the mean elasticity value with SWE was significantly higher in malignant lesions (153 kPa +/ - 58) compared with that of benign ones (46 kPa +/ - 43), establishing an optimal cut-off value of 80 kPa. In summary, both SE and 2D SWE have shown considerable potential for increasing diagnostic accuracy in the characterization of breast lesions. However, there are still only a few studies that have compared these techniques. [PERSON] et al. [30] reported that the AUC for the elasticity values of strain elastography was slightly higher than that of 2D SWE but without a statistically significant difference (strain ratio 0.929, \(p\) > 0.05). Combined SE and 2D SWE showed significantly higher diagnostic accuracy than the methods used alone (\(p\) = 0.031). [PERSON] et al. [31] indicated that the combined use of SE and 2D SWE together increased the performance of B-mode ultrasound alone. In particular, 2D SWE was more useful than SE. No statistically significant differences were found between the qualitative and quantitative assessments. Another paper by [PERSON] et al. demonstrated that SE, 2D SWE, and pSWE had higher diagnostic performance individually than B-mode US alone for distinguishing between malignant and benign breast masses with similar diagnostic performance; the AUC for each of these elastography methods was 0.93, 0.93, 0.98, 0.97, 0.98, and 0.96, respectively (\(p\) < 0.001 for all measurements) [32]. In our study, strain elastography (cut-off = 1.69) showed a greater AUC than 2D SWE and pSWE (equal to 0.84, 0.64, and 0.61, respectively), with a sensitivity comparable to pSWE (respectively, of 0.81 and 0.84) but with a greater specificity (respectively, of 0.86 and 0.55), proving to be an accurate method in the discrimination between benign and malignant breast lesions. The association between SR, an experienced radiologist, and \"SmartBreast\" had the best sensitivity amongst the different methods (0.99), despite less specificity (0.62) that was the best for SR taken alone (0.86), demonstrating that SR is a valid ultrasound method. pSWE showed the lowest sensitivity and specificity (0.71 and 0.50, respectively), proving to be the worst elastographic method for breast lesion evaluation in our study. These data could be related to the high prevalence of malignancies in our cohort and to the technology we used, for which there are still no universal cut-offs for stiffness values to be used, but they confirmed the potential role of these technologies in supporting the radiologist's diagnosis in the evaluation of breast focal lesions.
## 2 Methodology
### Static Modal Derivatives
#### 2.2.2 Static Modal Derivative Selection  
After the computation of SMDs, the next important step is the selection of the most relevant ones. The selection criterion is based on the Maximum Modal Interaction (MMI) criterion proposed by [PERSON] [27] for reduced-order models of geometrically nonlinear structures. This selection criterion is modified to provide higher accuracy and is adopted for structures with geometric and friction nonlinearities. The modified strategy allows the selection of the static derivatives of the static modes in case they are excited because of higher harmonics of the nonlinear forces or geometric coupling (the modes that are excited due to nonlinearity).  
In its original formulation, the MMI criterion requires that a linear run under external loading over time [0, T] is first performed, and then the weighting of each SMD is computed as [20]:  
\[W_{ij}=\max\lvert\eta_{i}(t)\rvert\cdot\max\lvert\eta_{j}(t)\rvert \tag{19}\]  
where \(\eta_{i}(t)\) represents the time-varying amplitude of the \(i\)th mode. The SMDs with the highest value of \(W_{ij}\) will be selected. In such a manner, only the derivatives of linearly excited modes are selected, and nonlinearity is treated as a second-order effect [20] neglecting higher orders.  
Here, the matrix \(\mathbf{Q}\), which was previously used for the selection of the dominant modes, is employed. Matrix \(\mathbf{Q}\) is a matrix of generalized coordinates \(\mathbf{Q}=[\mathbf{q}^{1},\mathbf{q}^{2},\ldots,\mathbf{q}^{n_{v}}]\) where \(\mathbf{q}^{iT}=[\eta_{1}^{j},\eta_{2}^{j},\ldots,\eta_{m}^{j}]\), \(n_{v}\) is the number of vectors in \(\mathbf{U}\), and \(m\) is the total number of selected dominant modes. The weighting factor of SMDs \(\theta_{ij}\) will be computed using a similar formulation to Equation (19), as follows.  
\[W_{ij}=\sum_{k=1}^{n}\lvert\eta_{i}^{k}\eta_{j}^{k}\rvert \tag{20}\]  
A modal derivative (\(\theta_{ij}\)) whose weighting factor \(W_{ij}\) exceeds a certain threshold is selected and added to the reduced space. Here, the weighting factors are all normalized to the largest weighting factor (\(W=[W_{11},W_{12},\ldots]/max(W)\)), and a threshold of 0.0001 is considered.  
Unlike the original MMI approach, in this manner, the participation factors of the modes excited by nonlinear forces, such as internally resonant modes or geometrically coupled modes, are non-zero, allowing their derivatives to be selected. This results in considering the higher-order effects of nonlinearity. As a higher level of geometric nonlinearity is activated, the superior accuracy of this approach over the original MMI becomes increasingly evident.