esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### 2D Cross-Sections  
In order to show the variation of the estimated Vs profiles along the city, the results of the RWE inversion are illustrated in a couple of two-dimensional cross-sections (Figure 9). We have chosen the central part of the knoll as the intersection point of both profiles. In this way, the subsurface shape of this knoll is clearly identified. Figure 9a shows clearly that the Ante-Neogene bedrock outcrops in the central part of the knoll, between measurement points V1 and 29.  
Figure 8: Examples of the estimated shear-wave velocity profiles for each zone. The left panels represent the computed fundamental mode of Rayleigh wave ellipticity curve (dark grey curve), and the inverted part of the calculated Rayleigh wave ellipticity (black dotted curve) for each zone linked via misfit values. The right panels represent the obtained shear-wave velocity models. The model in black corresponds to the one with the minimum misfit, while dark grey corresponds to models with misfit +10%. The rest of the generated models are represented in light grey.  
The shear-wave velocity for the topmost layer of the zone Z1, which consists of Holocene alluviums, ranges between 280 m/s and 330 m/s. The maximum thickness is 10 m (Figure 9). The velocity contrast between this layer and the underlying Pleistocene formation is responsible for the second peak at higher frequencies. Within the city area, the topmost layer consists of Pleistocene deposits. The thickness varies between 2 and 10 m in the center (Figure 9), where the bedrock is at very shallow depths, as reported by WCC Report [7]. In the rest of the study area, the thickness varies between 13 and 58 m (Figure 9). The shear-wave velocity ranges between 615 m/s to 710 m/s. The Vs values for the bottom sediment layer range between 950 m/s and 1350 m/s. The thickness varies from 13 to 16 m in the zone Z4, and from 40 to 162 m in the surrounding plain (Figure 9). The Vs values for the Ante-Neogene basement vary between and 2000 m/s and 2600 m/s. The velocity values are in agreement with the ones obtained by [PERSON] et al. [17].  
The higher depths to bedrock are observed in the western and southern parts with a maximum value of 198 m (point 62, Figure 9b), which is not far from the 200 m obtained from gravity modeling [24].
## 4 Frequency Up-Conversion of QPSK Data
The EVM of the simultaneous ten up-converted signals at the SOA-MZI output is given in Figure 16. Besides, it was only obtained at the mixing frequency \(10f_{s}+f_{m}\) at the bit rate of 100 Gbit/s. The EVM augments with the channel rank for the standard and differential modulation modes in co- and counter-directions. Furthermore, it strongly ameliorated for the differential modulation in both configurations compared to the standard modulation. This amelioration was about 20% over the entire range of the channel rank. In the counter-direction, the EVM inclined 2% for all channels compared to the one in co-direction one for both modes. Figure 14: EVM of up-converted QPSK signals from \(f_{1}=1\) GHz to the mixing frequencies \(nf_{s}+f_{1}\) at the bit rate of 100 Gbit/s for the four cases. The co-directional conversion exhibits a better performance (higher contrast ratio (CR), higher conversion gains, and lower EVMs) at high speed than the counter-directional conversion, because the interaction time between the IF signal and the sampling signal in SOAs is longer in the co-directional propagation [44]. In counter-direction, the frequency conversion based on a SOA-MZI is working well as CR and output power are concerned. The speed limit of the counter-direction with respect to co-direction was an issue for frequency mixing over the entire range of the mixing frequency at different bit rates used in the simulations. The speed in this case can be also limited by other, more serious, parameters such as differential carrier lifetime, SOA saturation power, SOA gain, and SOA current. However, these parameters are chosen to be the same for the co- and counter-directions in standard and differential modes in order to obtain on the fair comparisons. As a result, the speed was influenced by the propagation of direction schemas. On the other hand, the differential configuration depended on SOA-MZI led to a clear enhancement of the system performances [22] comparing to the standard configuration. As a result, the  
Figure 16: EVM of simultaneous ten up-converted signals at \(10f_{s}+f_{m}\) related to the tenth harmonic of the sampling signal at the SOA-MZI output at 100 Gbit/s. Figure 15: EVM of up-converted QPSK signals from \(f_{2}=2\) GHz to the mixing frequencies \(nf_{s}+f_{2}\) at the bit rate of 100 Gbit/s for the four cases. differential modulation mode in co-directions schemas was the best of the four considered scenarios thanks to the quality and efficiency of optical transmission systems through the conversion gain and EVM.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 3 Results
### Evaluating Causes of Striping and Nonuniformities  
It has been noted from several sources that the cause of striping may be that drifts occur over the long monthly cadence of the calibration table update [15; 17]. This could explain the striping in the  
Figure 5: Nighttime scene of Typhoon Vongfong with striping from 7 October 2014 at 17:03 UTC from black to white. The image is 656 by 940 pixels and is located near center of swath. Gray scale is linear from \(2\times 10^{-9}\) to \(3.6\times 10^{-8}\) W\(\cdot\)cm\({}^{-2}\)\(\cdot\)str\({}^{-1}\).  
Figure 6: Nighttime scene with striping from 25 June 2015 at 15:32 UTC, showing cloud cover over the Ross Sea and Antarctica.. This images uses NCC and shows1275 by 1900 pixels.  
MGS and HGS scenes, but it does not explain striping in the LGS that has a much shorter cadence, the same as the other RSB. In discussing the LGS dark cal sector data, we have noted previously that differences between the SV and SD offsets could not cause significant striping, but there is noticeable striping in LGS scenes, greater than 10% in several places and therefore there must be another cause [5]. We have also noticed by comparing the scenes with a similar ones from 31 days later that the fixed patterns are almost identical for both MGS and LGS. Therefore, there seems to be a systematic error other than drifts that is occurring in both the SD LGS calibration and the LGS-to-MGS cross-calibration. The two dates that were separated by 31 days were at or close to new moon, so lunar stray light was not an issue. However, the DNB cal sectors are sensitive to lunar stray light reflecting off the Earth during a quarter moon or full moon [17].
## 3 Interference Mitigation Schemes
### MAC Approach
#### 3.2.2 Interference-Aware Channel Switching  
Interference between WBASNs can be monitored by using an interference framework that decides to switch the operating channel of interfering WBASNs to another channel [26]. An interference framework includes a set of network nodes, a set of device state models, and the interface interconnecting them. The interference framework is able to record the duration of nodes and notify the current state of the node. In the Interference-aware Channel Switching (InterACS) algorithm, the signal-to-interference ratio (SIR) of the sender is monitored by the coordinator. If the SIR is below a threshold value, the coordinator switches the sender to a new channel. A threshold value is in the range of three values: low, moderate, and high; in these cases, the coordinator switches the sender to the next 2-hop channel, 3-hop channel, and 4-hop channel, respectively.
## 3 The Acceptability and Acceptance Study of SHERPAM
### Measures
#### 3.3.2 Qualitative Data Analysis  
In accordance with the initial hypothesis, a grid was developed to characterize the comments according to the role assumed by the participants during the test phase and according to the valence (positive or negative) of the comment. This assumption made it possible to classify their comments along two axes:  
* The first identifies the relationship with the technology endorsed by the participant, which leads him either to remain in his role as a tester (\"T\" category, which includes judgments against the technology) or to project himself as a future user (\"U\" category, which includes judgments about himself as a user).
* The second axis determines the valence of this response (favorable, \"+\", or unfavorable, \"-\", to the monitoring system).  
Thus, three independent judges (a Professor, a Ph.D., and a psychologist specialized in acceptability), classified the verbatims, each independently of the other two (before pooling the results of their categorizations), according to the grid developed. This made it possible to characterize the role of tester (i.e., comments on the ergonomics of the device) or future user (i.e., judgment on oneself as to the intention to use the device). A measure of inter-judge agreement was carried out to check that the different judges categorized the verbatims in the same way. This was calculated using the kappa (\(k\)) agreement coefficient [32]. This indicator is between 0 and 1; the closer the number is to 1, the more consistent the observations are with each other. For the first axis the Cohen kappa is considered as average (\(k\) = 0.54; 69.21%) and good for the second axis (\(k\) Cohen = 0.70; 79.88%) [33].
## 2 Proposed Architecture
### Recovery Analysis
#### 2.2.4 SEMNU at Q-QB  
When an SEMNU occurs and flips the state of the storage node pair Q and QB, QB transitions from '0' to '1' while Q changes from '1' to '0'. This results in P8, N2, and N6 turning off while P7, N1, and N5 turn on. For the S0 node, both transistors P8 and P2 remain off; as a result, S0 maintains a high impedance (high-Z) state, preserving its initial value. For the S1 node, with P1, P7, and N1 all activated, both the pull-up and pull-down paths are available. However, because P1 carries a significantly stronger drain current compared to the combined effect of three N1 transistors and P7, S1 is effectively pulled up by P1, ensuring that it retains a value of '1'.  
As a result, Q is restored to '1' by P3. Subsequently, when N6 turns back on, QB is restored to '0' by N6 and N4. Consequently, the proposed cell demonstrates the ability to recover from single-event upsets (SEUs) affecting Q, QB, and S0, as well as from SEMNUs impacting the Q-QB node pair. If an SEMNU occurs in the Q-S0 and QB-S0 pairs, the values stored in the cell may be flipped. However, as illustrated in Figure 2, the layout design ensures that the drain terminals of the NMOS and PMOS transistors (P8/N5 and P6/P4) are separated by a minimum distance of 1.62 \(\upmu\)m, thereby reducing the likelihood of SEMNUs occurring in structurally unrecoverable regions like Q-S0 and QB-S0 [10, 18]. Additionally, during the layout fabrication, elements and wiring were arranged symmetrically to maintain consistent symmetry.
## 3 Overall Design of the Communication Channel Reconstruction
### Capacity Analysis  
Sampled-value and phasor criterion are used for current differential protection, both of which need to transmit corresponding current data. Assuming that sampled waveforms of current at \(N_{\mathrm{s}}\) samples/cycle are adopted, the data rate of sampled-value is calculated by Equation (12):  
\[R_{\mathrm{bs}}=8L_{\mathrm{o}}N_{\mathrm{s}}f_{0}, \tag{12}\]  
where \(L_{\mathrm{o}}\) is the length of PHY packet in octet and \(f_{0}\) is the fundamental frequency. Based on the packet frame in Figure 4, \(L_{\mathrm{o}}\) is 37 octets. Therefore, to meet the IEEE 802.15.4 data rate constraints (250 kbps PHY data rate at 2.45 GHz) [26], \(N_{\mathrm{s}}\) can be set as 16. If using Fourier algorithm by extracting current in a cycle data window to form a phasor, the data window will slide backward once every integer times of sampling period \(nT_{\mathrm{s}}\). Specifically speaking, given 16 current values are sampled in a cycle, a new data window is constructed by adding the latest sampled current value to the end of the remaining 15 values after 1.25 ms. The data rate of phasor criterion is calculated by:  
Figure 4: WSN data packet frame.  
\[R_{\rm bp}=\frac{8L_{\rm o}}{nT_{\rm s}}. \tag{13}\]  
It can be concluded that the data rate is proportional to sampling frequency and the WSN can meet the need for both sampled-value-based and phasor-based differential protection when \(N_{\rm s}\) is set to 16. Focusing on the performance of RCC for protection, the rest of this paper selects widely-adopted phasor-based differential protection for illustrative purposes.
## 4 Case Study
### Visual Collaborative Design  
Compared to the traditional collaborative methods and BIM-based methods, BIM and VR collaborative design can realize speedy uploading of the model to the cloud and instant sharing with relevant personnel. The stakeholders can roam in the virtual scenes to annotate design feedback, directly observe the overall model and detailed constructions, and verify design model errors through collaboration with various specialties. Additionally, BIM and VR models are bidirectionally interconnected. Model modifications can be made at any stage during the prefabricated building design phase, no matter the changes in objects or contents, which synchronously will be linked between the two models.
## 3 Reliability Models of WSNs
### Impact of Routing on the WSN Reliability  
As mentioned in Section 3.1, the routing strategy has a key impact on the WSN reliability. Each routing algorithm defines a different set of nodes that compose the path from a region to the sink node. Hence, the reliability of a particular region depends on the adopted routing algorithm. Figure 12 illustrates a WSN used to describe the Region Models created by different routing algorithms.  
This WSN has 20 nodes, a sink node (node 1), and a region with only one sensor node (node 13). The reliability model of this region will be shown in the following considering different routing protocols: DIRECT (single hop), FLOODING (flat) and LEACH (hierarchical). These protocols were selected for the following reasons: each one represents a category of routing protocol (see Section 2.1); they are easy to implement; they are widely adopted; and they have different behaviors (they have different strategies to define a path). For example, LEACH works by creating clusters, while FLOODING creates multipath. Furthermore, these protocols were used to illustrate that the proposed reliability model is independent from the adopted routing protocol.
# Enhancing Small Language Models for Graph Tasks Through Graph Encoder Integration
## 4 Experiments  
### Experimental Set-Up  
#### 4.1.1 Datasets and Baseline Models  
This study employs the Citeseer and Ogbn-arxiv datasets [6] for node classification and link prediction tasks. Other graph benchmark datasets, such as Cora, PubMed, and Ogbn-products, were excluded due to restricted access or unavailability.  
The small language models (SLMs) used in this study are diverse in terms of size and characteristics. The SmoLM series [17; 25] consists of lightweight models developed using synthetic data generated from the Mistral LLM [11]. SmoLM-360M is a lightweight model with 360 million parameters designed for general natural language processing (NLP) tasks. SmoLM2-360M, an improved version of SmoLM-360M, aims to enhance performance by refining the pretraining data and architecture. Additionally, SmoLM-360M-Instruct is an instruction-tuned variant of SmoLM-360M optimized for instruction-based text generation and inference tasks. SmoLM2-360M-Instruct, a more advanced instruction-tuned model based on SmoLM2-360M, is designed to achieve better performance in instruction-based tasks. SmoLM2-135M-Instruct, a Hugging Face-developed model, is a further lightweight version of SmoLM2-360M, with 135 million parameters while being optimized for specific tasks through instruction tuning. Nano-Mistral [30], another lightweight model, reduces the original Mistral LLM to 170 million parameters, balancing efficiency and performance. Pythia, part of the Pythia Scaling Suite [31], is developed to facilitate interpretability studies. This suite includes models of various sizes, ranging from 70 M to 12 B parameters. In this study, we evaluate the 160 M model, containing 160 million parameters.  
Four tuning methods are applied to the models. The first is prompt tuning, where the original parameters of the SLM are frozen and the model is trained in a manner similar to soft prompt tuning. The second is fine-tuning, in which the parameters of the SLM are partially or fully updated to adapt to specific downstream tasks. The third is prompt tuning (with GCN-T), which employs the proposed graph encoder that combines GCNs and graph transformers to learn graph embeddings, subsequently integrating them into the SLM input for prompt tuning. Finally, fine-tuning (with GCN-T) combines the graph embeddings with the SLM input and fine-tunes the entire model, effectively capturing relationships between the nodes and edges.  
In addition to these models, we utilize Llama-2-7B [13], a 7 billion parameter large language model developed by Meta. Llama-2-7B demonstrates strong performance in both helpfulness and safety for a wide range of natural language processing tasks. Demonstrating similar performance in graph-related tasks with significantly fewer parameters is a meaningful contribution, emphasizing the potential of parameter-efficient models in resource-constrained environments.
## 3 Results  
### Sample Characteristics  
Data from all patients who completed treatment between 1 June and 30 June 2022 (2080 patients) were available for analysis without exclusion (comprehensive sample). Of the 2080 patients, 1488 (71.5%) were female and 592 (28.5%) were male, with a median age of 29.0 years ranging between 18-70. The least predominant age group was those over 55 years old with only 18 patients (0.9%), followed by middle-aged adults ranging between 36-55 years old with 447 patients (21.5%). The majority of the group was young adults (18-35 years, \(n\) = 1611, 77.6%).  
Of all patients, 493/2080 (23.7%) reported previous orthodontic treatment, and 1587/2080 (76.3%) reported no previous orthodontic treatment. Notably, only 10/493 (2.0%) of the patients with previous orthodontic treatment reported wearing fixed retainers at the time of the initial consultation for re-treatment.  
A total of 468/2080 (32.6%) patients indicated \"crowding\" as the most important motivating factor for seeking CAT, whereas 465/2080 (32.4%) patients indicated \"protruding teeth\", 271/2080 (18.9%) patients indicated \"tooth spacing\", and 233/2080 (16.2%) patients indicated \"other functional or dental health\" motives.  
The majority of all patients (90.5%, 1883/2080) had public insurance, 190/2080 (9.1%) had private insurance, and 7/2080 (0.3%) did not answer the question.  
The average planned treatment duration was 19.8 weeks (with a range of 4-56). During CAT, interproximal enamel reduction (IPR) was planned and performed in the majority of patients (66.8%, 1389/2080), while 691/2080 (33.2%) patients did not require IPR during treatment. Of all patients treated with IPR, most patients (566/1389, 40.8%) had 2 IPR sessions, followed by patients (417/1389, 30.0%) with 1 IPR session, while 406/1389 (29.2%) had >2 IPR sessions during treatment.  
On average, patients had 3.86 (SD = 2.32) clinical visits during treatment. Patients were asked several times during treatment to rate their satisfaction with treatment on a 6-point Likert scale from 0-5. Three different surveys were sent out: at the beginning of treatment, after clinical visits, and during treatment. At the beginning of the treatment, 525/2080 (25.2%) patients responded to the survey, while 1555/2080 (74.8%) did not respond. The mean score for this survey was 4.36. Of the survey sent after clinical visits, 930/2080 (44.7%) responded and 1150/2080 (55.3%) did not respond to the survey. The mean score was 4.42. Patients were re-surveyed during the course of treatment. 681/2080 (32.7%) responded and 1399/2080 (67.3%) did not respond to the survey. The mean score was 4.07.
## 2 Related Work
### Sensor Network Platforms
The most similar to the REIP platform is the Array of Things (AoT) [16; 17], an urban sensing system designed to collect real-time data from the environment leveraging a sensor platform called Waggle [24]. The AoT comprises an open-source API with multiple sensing modalities; however, it does not meet the device-agnostic criteria, as it depends heavily on the Waggle platform which itself cannot be easily produced at scale by other institutions/researchers. Similarly, WaspMote [18] offers a modular hardware and software architecture integration, with an open-source API to create its application pipelines. Some popular use cases for the system include smart cities, water and agriculture applications, most of which utilise low bandwidth wireless technologies such as LoRa or ZigBee. WaspMote was designed for low volume IoT applications on constrained and generally battery powered edge devices such as Micro Controller Units (MCUs), which are not typically suited for processing high volumes of audio or video data [32]. REIP, in contrast, is targeting higher performance Linux-based SBCs to tackle such data intensive sensing applications. The USC test-bed [19] currently under construction is to be a scalable HW/SW integrated sensor network with sensors, actuators and wireless radios to support experimental research on sensing, processing, algorithms and software for IoT. However, an open-source codebase and user-friendly API are so far not announced as part of their design goals. Ultimately, the aim of REIP is to provide a sensor network development platform that meets all of the before-mentioned criteria in a balanced way. As the first step, we implement and present in this work the REIP SDK--an open-source device-agnostic SDK/API, that supports multiple sensing modalities and hardware/software integration, and which is not only user-friendly but is also scalable and extensible. The following section details existing software frameworks that are relevant to the creation of data acquisition/processing _pipelines_ such as the ones created using the REIP SDK for sensing platforms with edge computing capabilities (bottom half of Table 1).