esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Nonlinear Vibration Isolator with Negative Stiffness Corrector  
### Modelling of Nonlinear Vibration Isolator  
The proposed nonlinear vibration isolation system is mainly composed of the positive stiffness mechanism, the QCRM-NSC, the upper loading platform (2), and the baseplate (6), as shown in Figure 6. The positive stiffness mechanism here consists of four vertical linear springs (5) which are fixed on the corners of the baseplate to carry the rated mass \(m\) (1) and ensure the reliability of the vibration isolator. The QCRM-NSC is designed based on the principle described in Section 2 and located in the middle of the isolator. As the important components of the NSC, two grooved cams (3) are symmetrically mounted on the baseplate. After being connected by the horizontal compression linear spring (7), the rollers (4) contacting with the cams are horizontally installed under the upper platform (2) by the hollow tube (8). The pre-compression of the horizontal spring can be adjusted by the nuts (9) at the bottom of the cams. Vertical adjustment nuts (10) are also adopted to eliminate the interference of static load error on the static equilibrium position of the vibration isolator.  
Since the positive stiffness mechanism and the NSC are connected in parallel, the sum of their restoring forces is the total restoring force of the entire vibration isolator. When the stiffness of each vertical spring is \(k_{\mathrm{v}}\), the force-displacement characteristic can be written as:  
\[F_{\mathrm{k}}(u)=4a^{2}k_{\mathrm{n}}u^{3}-4 ak_{\mathrm{n}}(a\Delta y^{2}+ \delta_{\mathrm{n}})u+k_{\mathrm{p}}u \tag{6}\]  
where \(F_{\mathrm{k}}\) is the restoring force provided by the vibration isolator, \(k_{\mathrm{p}}=4k_{\mathrm{v}}\) is the total stiffness of the positive stiffness mechanism, and \(\Delta y=mg/k_{\mathrm{p}}\).  
Figure 5: Non-dimensional characteristics of QCRM with \(\overline{\delta}_{\mathrm{n}}=0.7\). (**a**) Force-displacement curves; (**b**) stiffness curves.  
Figure 6: Physical model of nonlinear vibration isolator with QCRM-NSC.  
Dividing both sides of Equation (6) by \(k_{\rm p}\Delta y\), the non-dimensional restoring force \(\overline{F}_{\rm k}\) of the isolator can be derived as:  
\[\overline{F}_{\rm k}(\overline{u})=4\pi\overline{a}^{2}\overline{u}^{3}-4\pi \overline{a}^{2}\overline{u}-4\pi\overline{a}\overline{\delta}_{\rm n}\overline {u}+\overline{u} \tag{7}\]  
where \(r=k_{\rm n}/k_{\rm p}\) is the stiffness ratio, and the other non-dimensional parameters have the same definitions with those in Equation (4).  
Then, the non-dimensional stiffness \(\overline{K}\) is obtained by differentiating Equation (7) with respect to the non-dimensional displacement \(\overline{u}\), as:  
\[\overline{K}(\overline{u})=12\pi\overline{a}^{2}\overline{u}^{2}-4\pi \overline{a}^{2}-4\pi\overline{a}\overline{\delta}_{\rm n}+1 \tag{8}\]
# Software Fault Localization Based on SALSA Algorithm
###### Abstract  
In software development, debugging is the most tedious and time-consuming phase. Therefore, various automated fault localization techniques have been proposed to assist debugging. Among existing fault localization techniques, Spectrum-Based Fault Localization (SBFL) is one of the most extensively researched methods. Traditional SBFL techniques rely solely on the coverage of program execution for fault localization, which means they neglect the interactions between program entities and fault propagation paths during the execution of the program, resulting in a tie problem that reduces the accuracy of fault localization. To solve the above problem, this paper proposes SA-SBFL, a fault localization method based on the SALSA (Random Method for Link Structure Analysis) algorithm. First, a link graph of program entities is constructed, which includes interactions between program entities and fault propagation paths. Then, the suspicion values obtained from traditional SBFL methods are used as the initial weights of the link graph. Finally, the random walk model is employed to simulate the propagation of faults among program entities, analyze the importance of program entities in the fault propagation process, and obtain a ranking list of suspicious program entities. The experiments in this paper demonstrate that the SA-SBFL method significantly outperforms general SBFL methods. For instance, in the Defects4] dataset, the SA-SBFL technique outperforms traditional SBFL in terms of fault localization accuracy, with a 47% improvement in the Top-1 metric and a 10% increase in the Top-5 metric, and it also showed an average improvement of 19% in the EXAM metric.  
fault localization; program spectrum; link propagation; SALSA algorithm +
Footnote †: journal: Applied Sciences  
1
# Optimizing Geospatial Data for ML/CV Applications:
## 2 Materials
### Development Environment  
The project used Python 3.12.5. Python's strengths in data processing, geospatial analysis, and its extensive library ecosystem made it a perfect match for the research outcomes [49]. The project was structured into modules and scripts for better code reusability and maintainability. PyCharm was used for project management, code editing, and debugging. A variety of Python libraries and modules were integral to the project's functionality.  
The'requests' library was utilized to make HTTP requests to web endpoints. This allowed for interaction with various web services and APIs, such as those used for retrieving road data and parcel information.  
The're' library was used to match and extract road names based on the prefixes 'A', 'E', 'K', 'W' and 'P', which represent different road types in Poland. This allowed for the categorizing and filtering of the road data.  
'JSON' and 'GeoJSON' were used to store structures like road names, nodes, parcel information, and road segments, along with related metadata. GeoJSON preserved spatial references for paths and geometries.  
The 'OWSLib' library was utilized to interact with Web Map Services (WMS) endpoints. This allowed for the issuing of 'GetMap' requests to retrieve georeferenced map images, which was crucial for accessing orthoimagery data from geospatial web services.  
'Shapely' is a library, which was used for manipulating and analyzing planar geometric objects like point lines, multi-lines, and polygons. These geometries were essential for spatial analyses like intersection tests, buffering, and masking.  
The 'asyncio' and 'aiohttp' libraries were utilized for asynchronous programming, enabling the concurrent fetching of multiple tile images. This significantly sped up image downloads, reducing the overall runtime of data acquisition tasks.  
'Pillow' is a module used for various image processing tasks like saving and loading images, converting them to RGBA format, and creating masks based on geometric shapes to remove irrelevant areas from the images.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 5 Performance Evaluation
### Parameter Analysis
#### 5.3.3. Gathering Node Analysis  
Considering that the number of gathering nodes indicates the efficiency of data aggregation, we analyze the number of gathering nodes and the number of independent nodes, in three scenarios, i.e., SP, SC and SS, with 160 nodes. The results are illustrated in Figures 11 and 12.  
In different scenarios, the numbers of two kinds of nodes are similar with the same \(OR_{T}\). Comparing the numbers of gathering nodes in AggOR and AggOR-CP in Figure 11, we get that when \(OR_{T}\) is smaller than 0.5, no neighbor nodes are selected as gathering nodes. In Figure 12, when \(OR_{T}\) increases from 0.5, AggOR-CP has more independent nodes than AggOR. When \(OR_{T}\) is larger than 0.8, the numbers of gathering nodes and the numbers of independent nodes both reach plateaus. Specifically, all nodes in AggOR-CP are independent nodes, while AggOR has some neighbor nodes selected as gathering nodes, and hence reduces the transmission overhead.  
Figure 11. The numbers of gathering nodes in three scenarios. (**a**) Scenario SP; (**b**) Scenario SC; (**c**) Scenario SS.  
Figure 10. Results with different \(OR_{T}\). (**a**) network lifetime; (**b**) transmission overhead; (**c**) maximum hops to the sink.  
Figure 13 shows the sketch maps of three scenarios, i.e., SP, SC and SS, with 80 nodes in AggOR. The edges indicate the relationships between gathering nodes and member nodes. Due to the limited spaces, to avoid confusion caused by crossing lines, we do not show the data transmission paths from the gathering nodes to the sink. From the figure, we get that the gathering nodes are usually located near their member nodes, and most of them are closer to the sink than member nodes.  
Overall, the larger the sensor density is, the better AggOR performs. In a relatively dense network, a larger network scale leads to a greater advantage of AggOR over EEUC and HEER. Additionally, \(OR_{T}\) has a significant effect on the performance of AggOR, which peaks at \(OR_{T}=0.5\) in our experiments. In real scenarios, an appropriate \(OR_{T}\) can be obtained through sampling analysis during preliminary study. Furthermore, AggOR has a longer network lifetime and a smaller traffic overhead than AggOR-CP in the three scenarios with different sensor deployments and different locations of the sink.
# Ultrasound in Telemedicine: A Brief Overview
## 5 Discussion  
The rapidly evolving technology will likely promote the widespread implementation of tele-ultrasound procedures, especially in remote areas and in the first-responder setting. Further investigation of real-time image transfer and communication paradigms in prehospital scenarios and practical applications is warranted. Pocket and other portable ultrasound machines are the most commonly used ultrasound devices for tele-ultrasound. The potential impact of tele-ultrasound is substantial when considering both the scope of clinical fields of application (e.g., respiratory failure, hemodynamic compromise, procedural guidance) and the number of stakeholders (e.g., patients, providers, health systems). Technologically speaking, ultrasound machines have become smaller, more portable, and durable, and the relative cost has decreased dramatically. Smartphones are ubiquitous, and dozens of software applications run seamlessly on them simultaneously. Therefore, smartphones are capable of functioning as affordable handheld telemedicine platforms. Finally, global connectivity is steadily increasing, particularly wireless cellular and internet access. These advances have made tele-ultrasound feasible and have given it a durable competitive advantage compared to conventional ultrasound, which will boost its application in the near future.  
Nevertheless, tele-guided ultrasound education also carries some weaknesses and risks. No tele-ultrasound module can fully replace real hands-on learning experiences. Although one can try to teach the handling of the probe via tele-guidance, it is easier to do this physically next to a student. The digital distance, which provides some benefits, appears as a barrier that cannot be eradicated when using tele-ultrasound. Therefore, tele-ultrasound is an option for remote or poorly developed areas, as well as an innovative tool in crisis situations.  
The practical usefulness of tele-ultrasound has become more entrenched and advanced in the medical setting in recent years. A distinction must be made between tele-ultrasound and the pure transmission of ultrasound images, which does not have the aspects of real time data transmission with a corresponding second examiner. Particularly in the field of medical teaching, there is a specific difference between these two modalities.  
Tele-ultrasound is on the advance, especially in the next few years, due to further progress in digitalization and technical miniaturization. Global pandemics, such as the current COVID-19 pandemic, have particularly marked and shaped this use of technology.  
Conceptualization, F.R. and V.S.S.; methodology, F.R.; software; investigation, F.R. and E.H.; resources, F.R., E.H. and D.D.; data curation, F.R., E.H. and V.S.S.; writing--original draft preparation, F.R.; writing--review and editing, E.H. and D.D.; visualization, F.R. and D.D.; supervision, V.S.S.; project administration, F.R. and V.S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# HyperIO: A Hypervisor-Based Framework for Secure IO
## 2 Background
### Certificates  
HyperIO uses the X.509 public key certificates [16] in its communication. Each certificate contains the name and public key of a subject. The certificate itself is signed by a security authority. The verifier can verify the signature if it has the public key of the security authority. If not, the public key of the security authority can be sent in another certificate, signed by another security authority, thus creating a chain of certificates. The last certificate in the chain must be verifiable by a public key that is known to the verifier. These certificates are known as root certificates. All the web browsers are distributed with a set of pre-installed root certificates.
## 3 Materials and Methods
### Data Treatment for MNPL Polymers  
The total ion chromatograms (TIC) obtained using full-scan (FS) acquisition using the Xcalibur (Thermo Fisher Scientific) software were processed.  
Peak picking and grouping were used as the first screening step. The TIC was interrogated every 30 s by intervals of 500 Da where peaks which mass spectra showed repetitive mass losses were marked as suspected polymers. The next filtration step was considering the intensity and, when the peak area was less than three times larger than the maximum peak area in the blanks, data were discarded as background. Then, selected profiles were tentatively identified using the repetitive mass loses with an error mass within 10 ppm. The first list of suspected polymers was created (level 4) and it was subsequently filtered by comparison with a homemade polymers library that included the 100 most-used ones [1] that can be slightly soluble in toluene (level 3). The Kendrick Mass Defect (KMD) was used for further filtering and to tentatively identify the polymers (identification level 2). KMDs were calculated according to equations described elsewhere [21], taking in each case PolymerX/(round Polymer X-2) as base unit. Some of the identified polymers (PP, PE, PS, PI) have also been quantified; comparison with their standards allowed us to achieve identification at level 1.  
Data Treatment for Plastic Additives and Potential Co-Contaminants Adsorbed to Plastic Particles' Surfaces  
Raw data were processed with Compound Discoverer 3.1 software. The first step included the data alignment, the comparison with control samples (filter blank analysed with each set of samples) and the comparison with available online and in-house databases. The first automatic processing generated thousands of potential compounds. Then, all the data were filtered using a series of restrictions such as the ratio sample/blank higher than 1.5, a minimum area of 1,000,000 a.u., retention time higher than 1 min and coincidence with expected mass spectra higher than 80%. This list of compounds was filtered using Visual Basic for Applications. The suspects with an error mass within \(\pm\)2.5 ppm and present in the three replicates were identified at confidence level 5. Then, suspects from positive and negative ionisations were put together and the potential molecular formulae compared with data available in the ChemSpider and mz Cloud databases to achieve a tentative identification at confidence level 4. Further filtering steps consisted of comparing isotopic patterns, providing identification at confidence level 3. To obtain the tentative identification (level 2), the productions obtained from the MS/MS spectrum of a suspected compound were compared with the spectrum of a standard or a predicted fragmentation pattern using the information contained in the online databases.
## 3 Results
### Simulated Delivery Errors  
The average deviations from reference counts and R-values of simultaneously operated IQM and SoftDiso due to simulated delivery errors obtained from the five measurement runs are reported in Figure 4. Both SoftDiso and IQM detected all MU variations showing excellent linearity and sensitivities of \((0.53\pm 0.13)\%\)/MU and \((0.53\pm 0.01)\%\)/MU respectively. A remarkable correlation between the outputs of the two devices was also observed. Furthermore, the IQM is also able to detect small jaw position variations, showing again, good sensitivities of \((1.17\pm 0.02)\%\)/mm and \((1.05\pm\ 0.02)\%\)/mm in both closing and opening movement directions respectively. The linearity in this case is only approximate, pointing either to an intrinsically non-linear response or to an underestimation of measurement errors. Nevertheless, IQM measurement errors are negligible compared to deviations of signal consequent to delivery errors in clinical routine. On the contrary, the R-value was found to be almost insensitive to the same relatively small jaw position variations, while still showing a reasonable response linearity, with large uncertainty though.
## 4 Applications of the TPCT  
Currently, the TPCT has been widely used in the permafrost region to ensure the thermal stability of various types of foundations, such as the highways [12; 16; 51; 52], railways [53; 54; 55], tunnels [56], oil, and gas pipelines [57], tower foundations [58], dams [59; 60], and other engineering apparatuses. In this paper, we pay attention to the applications in the highways and railways.
## 4 New Routing Protocol for MWSNs
### Our Efficient Routing Strategy in MWSNs
#### 4.1.2 Building Routing Tree
_Step 4 Establish routing tree and begin data transmission._ After a mobile sink finished building the optimal routing tree in _Step 3_, it sends Routing Result (RR) packet, in which the routing tree can be stored as array, singly linked list or other forms, to its source nodes to establish the routing tree. The routing path establishment process can be detailed as follows: Assuming that \(Path_{j}=\{Source_{j}\)\(\rightarrow\)\(11\)\(\rightarrow\)\(9\)\(\rightarrow\)\(7\)\(\rightarrow\)\(3\)\(\rightarrow\)\(Agent_{i}\)\(\rightarrow\)\(Sink_{i}\)} is a routing path of the routing tree. \(Sink_{i}\) sends RR packet to its agent. When \(Agent_{i}\) received the RR packet, it firstly search its previous relay node from  
Figure 1: Illustration of (**a**) sink queries MWSN; (**b**) graphic description of MWSN. Figure 2: (**a**) Illustration for routing tree; (**b**) Routing recover for mobile moves away; (**c**) Routing recover for relay node failed. the received packet according its ID, i.e., node 3. Then it sets isRelay = 1 for node pair {_Agent_\(i\)\(\rightarrow\)node3} in its neighbor table. Next, _Agent\({}_{i}\)_ replaces the destination address of RR packet with node3 and forwards it to node3. After received the RR packet, node 3 forwards it to its previous relay node7 as the same way, and so on for each relay node in the routing path. Finally, the source _Source\({}_{j}\)_ received the RR packet, which means that the routing path is established completely and source can begin the data packet transmitting via the reverse path of RS packet towards source node. The detailed data forward process is described as follows: _Source\({}_{j}\)_ generates a data packet and queries the neighbor_ID from its neighbor table as its next relay node using the condition isRelay = 1, i.e., node11, then the data packet is send to node11. After received the data packet, node 11 immediately queries its next relay node (i.e., node 9) and forwards data packet to node 9 in the same way, and so on for each relay node in the routing path. Finally, all data packets from _Source\({}_{j}\)_ are received by _Agent\({}_{i}\)_, and then _Sink\({}_{i}\)_ receives data packet from _Agent\({}_{i}\)_ directly.
## 3 ROR: Reliable Opportunistic Routing for CRSN
### Routing Operation  
It is important to note that, although nodes have to switch between receiving and sending channels, which is the worst case scenario, node channel switching does not have to be synchronized for efficient performance. Rather, the distributed nature of the protocol operation allows for much flexibility, which ensures the availability of potential forwarding nodes without jeopardizing the downstream flow in any way. Moreover, it should be remembered that the protocol minimizes the multiple channel switching at the route selection phase, as discussed earlier in Section 3.4.  
In order to elucidate this fact, we assume a source node, Sr, has a data packet to send, as illustrated in Figure 7; it will switch to channel Cs. Note that a node will only switch to its channel Cs when it has a packet to send, which means that all downstream nodes of the source's virtual cluster and that of the next virtual clusters will be automatically tuned to Cr0, as has been discussed in Section 3.5. It should also be remembered that, according to the node configuration as already discussed, Cs and Cr1 for VCG S and VCG I will be the same. A node will only switch to Cr1 if it had earlier received a data packet and subsequently returned a zero for its \(I_{DIFD}\) operation for that packet. Otherwise, it will remain tuned to Cr0. This will become clearer in the second hop operation. Therefore, the source node broadcasts this packet on Cs. Assume all the nodes, \(s_{1},s_{2},s_{3},i_{1},i_{2}\) and \(i_{3}\), received the packet. However, only Nodes  
Figure 7: Illustration of the routing operation.  
\(s_{1},s_{2},s_{3}\) and \(i_{1}\) return a one for their respective \(I_{DIFD}\) operation, while Nodes \(i_{2}\) and \(i_{3}\) return a zero for the same operation. Thus, Nodes \(i_{2}\) and \(i_{3}\) will switch to Cr1.  
Then, Nodes \(s_{1},s_{2},s_{3}\) and \(i_{1}\) will perform the receiver contention prioritization operation. By virtue of Node \(i_{1}\) being closer to the sink than Nodes \(s_{1},s_{2}\) and \(s_{3}\), Node \(i_{1}\) wins this contention and sends its ACK packet first to the sink via Cs. After sending the acknowledgment, \(i_{1}\) then switches to Cs and broadcasts the packet. Remember that Nodes \(i_{2}\) and \(i_{3}\) are already tuned to their clusters, Cr1, while Nodes \(j_{1},j_{2}\) and \(j_{3}\) are all tuned to their clusters, Cr0, because they have not previously returned a zero for \(I_{DIFD}\). Already, since the channels, Cs, Cr1 for VCG I and Cr0 for VCG J, are all the same, all the nodes, \(i_{2},i_{3},j_{1},j_{2}\) and \(j_{3}\), will all take part in contending to forward the packet if their \(I_{DIFD}\) operation returned a one. Notice that if a node that is already in its Cr1 channel subsequently returns a zero for \(I_{DIFD}\), it will automatically reverse back to Cr0. Thus, in this manner, the packet is forwarded effortlessly towards the sink. The full operation of the protocol is as depicted in the flow chart in Figure 8.