esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Clinician Experiences  
Results of an analysis of variance show significant differences in the Clinician Experience Index (CEI) while using the laser for cavity preparations (\(p\)-value < 0.001, F = 12.17). However, clinicians reported an overall positive rating (12.5 \(\pm\) 2.37) (see Figure 3) on a scale with an upper limit of 15, and the mean values for each index constituent were over 4 (on a scale with an upper limit of 5). A Pearson Correlation showed no significant relationship between the CEI and participant discomfort (\(p\) = 0.16), indicating that the variability in aggregate perceived speed, ease-of-use, and precision did not have a detectable relationship with participant discomfort. Additionally, there was no significant correlation between treatment time (days since first treatment) and patient discomfort (R = \(-\)0.055, \(p\) = 0.59), indicating that patient discomfort did not appear to change even as more inexperienced clinicians became accustomed to using the laser for cavity preparations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatment Given** & **n** & **Percentage** & **Average Discomfort** & **Range Discomfort** \\ \hline Laser only, with anesthesia & 2 & 2.08 & 5.0 \(\pm\) 2.8 & 3–7 \\ \hline Laser only, no anesthesia & 45 & 46.88 & 1.6 \(\pm\) 1.8 & 0–10 \\ \hline Laser and drill, with anesthesia & 5 & 5.21 & 3.5 \(\pm\) 2.6 & 0–6 \\ \hline Laser and drill, no anesthesia & 44 & 45.83 & 1.2 \(\pm\) 1.7 & 0–6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Discomfort values by procedure type.  
Figure 3: Clinician Experience Index. Individual clinicians (n = 6) are presented on the x-axis while the sum of the 5-point scales for speed, ease of use, and precision is presented on the y-axis.
# Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
## 3 Results  
### Temporal Variations of LUE at FLUXNET Sites  
Daily variations of LUE and diffuse fraction were analyzed firstly to determine the relationship between LUE and diffuse radiation. Here, data from FLUXNET2015 and the EBR FAPAR product were used for deriving LUE values at a half-hourly scale. Data within the peak-growth seasons were selected for analysis. Figure 1 shows the variations of LUE and diffuse fraction at different times during a single day. Both LUE and diffuse fraction exhibited high values during sunrise and sunset times while lower values were observed around noontimes, which indicates a co-variation between LUE and diffuse fraction at a daily scale.  
In addition, the relationship between LUE and diffuse fraction was examined across the FLUXNET sites within the whole year (Figure 2). For a better analysis, we firstly divided the diffuse fraction into ten levels from the lowest to the highest values (0.1 intervals). The results showed that LUE increased with increasing diffuse fraction for all vegetation types, which indicated the significant enhancement effect of diffuse radiation toward LUE [28,29].  
Figure 1: Diurnal variations of LUE and diffuse fraction at FLUXNET sites.  
Figure 2: Temporal variations of LUE under different levels of diffuse radiation fraction at FLUXNET sites (The level of diffuse fraction denotes the values of diffuse fraction varying from 0 to 1 with 0.1 intervals).
## 3 Materials and Methods
### Apparatus  
For the separation and quantitation of the analyte, a Shimadzu GC-2010 Plus AF gas chromatograph (Shimadzu, Kyoto, Japan), equipped with a flame ionization detector (FID) and a split/splitless injection (SPL-2010 Plus) system, was used. Shimadzu GC solution software (Version 2.4, Shimadzu, Milan, Italy) was employed to run the instrument. The separations were accomplished by an HT-8 fused silica capillary column (25 m \(\times\) 0.32 mm I.D. \(\times\) 0.5 um). Ultra-high purity-grade nitrogen (99.999%) was used as the carrier gas at a flow rate of 1 mL min\({}^{-1}\). The GC was operated in the splitless mode for all SPME experiments. The injector and FID systems were maintained at 230 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. The column temperature was initially set at 100 \({}^{\circ}\)C (1 min), then increased to 260 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C min\({}^{-1}\) and held constant for 1 min at 260 \({}^{\circ}\)C. In this way, the total run time was 10 min. Hydrogen, zero-air (FID gases) and nitrogen (make-up gas) flow rates were set at 30, 300 and 30 mL min\({}^{-1}\), respectively. A VEGA\(\backslash\)TESCAN CM120 (TESCAN, Brno, Czech Republic) field-emission scanning electron microscope (FE-SEM) was used to study the morphology of the fiber coating. Heating and stirring of the sample matrices were performed using a Heidolph MR 3001-K magnetic heater-stirrer (Kelheim, Germany). A 40-mL SPME sample vial was used as the extraction vial and a cylindrical PTFE coated magnetic stirring bar (5 mm \(\times\) 2 mm O.D.) for stirring the sample solution. A 1-\(\mu\)L microsyringe (Hamilton, NV, USA) was used for direct injection into the GC-FID instrument. A low-cost and simple hand-made SPME fiber holder was fabricated as previously described [36] and used for handling the PPy-coated fibers.
# Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity
## 3 On the Performance Improvement of Hybrid NoCs
### Intra-Dimension Bypassing
#### 3.1.2 Dedicated Virtual Channel for Bypassing  
Our goal is to design an adaptive VC router with reduced low-load latency. Bypassing should be well designed to provide VC compatibility, meanwhile sustaining the efficiency of intra-dimension bypassing in DSR.  
An incoming flit may belong to an arbitrary VC. Deciding whether a flit can bypass the current router, firstly the VC must be decoded, and then, the availability of corresponding credits for the downstream router must be checked. Here, we assume that the flit retains its VC ID when bypassing (VA details will be covered in Section 3.2.2). Suppose the VC ID of a received flit is vc, and the output port of DoR is o. If the following two conditions are met, the received flit can bypass the current router in one cycle. Firstly, bypassing must not cause overshooting to the destination (minimal routing). Secondly, the vc at output o must be idle (ensuring a successful VA). Implementing this bypassing logic requires using the VC ID as the input to index corresponding information. This control logic will inevitably increase the critical path length of bypassing logic compared to the one in [11] due to VC decoding. For example, the implementation on the \(X\) dimension is as follows:  
```
bypassing<=(dst.x!=current.x)& vc_idle[o][vc]
```  
Preliminary synthesis result shows that the path delay for this decision-making on 16 VCs is 0.1 ns on the 45-nm standard cell library. In this implementation, the decision-making speed slows down as the number of VCs increases.  
To speed up this process, we introduce a dedicated VC for bypassing. Suppose the special VC introduced is called the slide virtual channel (SVC). We now only perform bypassing for flits belonging to SVC. To check if an SVC flit can bypass the current router, a router only needs to check if the SVC of output o is idle. Bypass decision-making is faster because we do not need to use VC ID as the index to absorb credit information or other information. The processing speed is invariant to the number of VCs. If we use SVC for bypassing. The decision making on the \(X\) dimension is as follows:  
```
if(svc)begin  bypassing<=(dst.x!=current.x)& svc_idle[o] end
```  
Path delay for this logic is reduced to 0.05 ns using the same 45-nm standard cell library.  
Figure 5: Improved 2D router for inhomogeneous 3D NoCs.  
Only SVC packets are considered for bypassing, and there is also dedicated buffer space reserved for SVC in each router. To avoid extra delays without the routers, the SVC buffer is restricted to only one slot. This design reduces the complexity of bypass decision-making. Bypassing with SVC is faster and, more importantly, invariant to the number of VCs. Adding an extra VC does not necessarily increase buffer space in the router because most NoC routers use a shared buffer between VCs [49].
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 1 Introduction
### Related Works
The relay selection overhead was reduced by implementing the greedy algorithm based on a distributed local search that improved the EE along with the convergence time through a power adjustment scheme, which was based on the improved potential game decision algorithm. As relay selection is also an integral metric for relay-aided D2D communication, we put forward some research works to gain insight into the various techniques through which we can optimally select the relay for D2D communication to aid in its seamless operation. The authors in [15] put forward a relay selection scheme for a cooperative out-band D2D network based on the channel gain value and transmission link distance. A suitable relay was selected based on the minimum distance between the user and the relay node using the quantization-and-forward (QF) protocol. A cooperative electronic relay technique was proposed in [16] for a D2D communication system. The method uses three types of cooperative approaches together, namely the compress-and-forward relay, DF relay, and AF relay. It was found through the numerical results that the D2D outage probability of the proposed method was lower than that of the traditional relay transmission, which was again further minimized by an increase in the number of electronic relays. In [17], the authors proposed a global-positioning-system-based, location-aware, centralized approach to solve the problem of relay selection. A learning-based approach was formulated to detect the presence of static, as well as dynamic obstacles present in the environment. In [18], the authors proposed an optimization scheme using exhaustive search and a fast greedy algorithm for hybrid cellular/wireless local access area network (WLAN) communication with bad channel conditions, which tried to reduce the transmission power from the eNodeB (eNB) to the users. Lastly, in [19], a Markov-chain-theory-based mode selection scheme in a multi-user scenario was proposed to study the system performances. Resource allocation was performed by employing a greedy heuristic algorithm.
# Event-Driven Dynamics Model of Operating State Evolution for Cantilever Roadheader
## 2 Dynamics Model of the State Evolution of Cantilever Roadheader
### Discretization of Operating State Based on Cutting Path Planning  
Discrete state processing transforms complex physical tunneling behavior into digitally replicable and controllable units through state space discretization, enabling the precise tracking of the state evolution path.  
Based on cutting operation requirements, an S-shaped cutting path is planned for the implementation in the heading section, as shown in Figure 2. The cutting head moves from coordinate point \(S_{A}\) to point \(S_{H}\) within the heading section to complete the cutting work. During this process, the cutting head's motion alternates between the vertical and horizontal directions, and each transition point between operation modes is defined as a key state node. According to this path, the cutting state is discretized into eight key state  
Figure 1: Logic framework of the state evolution dynamics model for the cantilever roadheader.  
nodes (\(S_{A}\) to \(S_{H}\)). Each state node is quantitatively described by the 10 operating state parameters listed in Table 1, which are categorized into the following four groups:  
(1) Fuselage position coordinates: comprising X, Y, and Z coordinate values. X represents the position along the roadway's forward direction; Y represents the lateral position perpendicular to the roadway; Z represents the vertical height. These parameters define the 3D spatial location of the roadheader.  
(2) Fuselage attitude angles: comprising \(\alpha\), \(\beta\), \(\gamma\), and three angle parameters. \(\alpha\)(Yaw) represents the steering angle around the \(Z\)-axis; \(\beta\)(Pitch) represents the tilting angle around the \(Y\)-axis; \(\gamma\)(Roll) represents the tilting angle around the \(X\)-axis.  
(3) Cutting arm attitude parameters: comprising three kinematic parameters, \(\theta_{H}\), \(\theta_{T}\), and \(d\). \(\theta_{H}\) represents the rotary platform's rotating angle; \(\theta_{T}\) represents the cutting arm's lifting angle; \(d\) represents the cutting arm's extension length. These parameters are used for the precise motion control and pose characterization of the cutting arm of the roadheader.  
(4) Timestamp: a UTC-synchronized timestamp marking the data acquisition moment, providing temporal indexing for the virtual-physical state synchronization. The pose vector \(P_{t}=[X,Y,Z,\alpha,\beta,\gamma]\) defines the fuselage state of the roadheader, while the kinematic vector \(q_{t}=[\theta_{H},\ \theta_{T},d]\) parameterizes the cutting arm motion.
## 4 Performance Assessment  
This sub-section assesses the proposed CS3's performance through comparative analysis. The simulation experiments are modeled using the Contiki Cooja simulator, where 200 mobile users are placed. An IoT cloud architecture with seven service providers, each with 1 TB of storage, is used for service distribution. The transmit power of the devices is set as 30 dBm, and it operates in 60 scheduling intervals. With these simulation details, the performance is assessed using service dissemination factor, latency, service failure ratio, outage, and power utilization metrics. In the comparative analysis, the the Iterative Searching-based Algorithm for Task Offloading and Transmit Power Allocation (ISA-TOTPA) [17], Energy and Time-Efficient Computation Offloading and Resource Allocation (ETCORA) [16], and DBWA [18] methods are used. This study extended our simulation experiments using the Contiki Cooja simulator to include a broader range of scenarios. These scenarios now encompass different numbers of mobile users (50, 100, 200, and 300), varying mobility patterns, and diverse traffic conditions. This comprehensive evaluation aims to provide a more detailed understanding of the system's performance under varying circumstances. Our results indicate that our approach consistently outperforms others regarding latency and power allocation. Moreover, extended-duration tests reveal the long-term stability of our system.
## 2 Materials and Methods
### Sampling Sites and Sample Collection  
To determine the occurrence of the selected pharmaceuticals in surface waters in Lebanon, the developed method was applied on water samples taken from 3 sites in Lebanon and 3 sites in France (Figure 1). The first sampling site (A) was located in the Litani river, the largest river in Lebanon with 170 km in length and with an average annual flow of 920 million cubic meters. This river rises in the Beqaa Valley (west of Baalbek) and empties in the Mediterranean Sea (north of Tyre). The second sampling point was in the Damour river (B). This 37.5 km coastal river with an average annual flow of 307,000 cubic meters, located in the Mount Lebanon Governorate, originates from Barouk mountain, and flows west through the Jisr Al-Kadi valley, where it receives water from the creeks of the Chouf mountains, and empties into the Mediterranean Sea, south of Damour. The third sampling point (C) was located at 5 km to the south of the capital of Beirut directly at the discharging site of Al-Ghadir wastewater treatment plant. This plant is theoretically designed for 400,000 citizens of the southern suburbs of Greater Beirut but receives wastewaters from more than one million inhabitants. Three other samplings were carried out in Southern France along the Arc River, an 83 km river rising in Pourciaux and flowing in Berre Lagoon, directly connected to the Mediterranean Sea. This river notably flows through the city of Aix-en-Provence, and receives the discharges of several wastewater treatment plants, among them two located in southwestern Aix-en-Provence (150,000 pe) and western Aix-en-Provence (30,000 pe). Samples D, E, and F were, respectively, taken at the outlet of the first wastewater treatment plant (La Pioline, Aix-en-Provence, France), at the outlet of the second wastewater treatment plant (Aix-Ouest), and F (Roquefavour), 10 km downstream of the sampling point E.  
Samples were collected in duplicates from the mentioned locations in amber glass bottles and stored in a cooler with ice until transportation to the laboratory for analysis. Moreover, samples were taken in a period between July 2019 and August 2019 (in Lebanon) and in September 2019 in France.  
Figure 1: Sampling sites in Lebanon and in France. A, B, C red circles on the right represent the samples sites located in Lebanon: (A) Litani river; (B) Damour river; (C) Al Ghadir. D, E and red circles on the left represent the samples sites located in France: (D) downstream of WWTP1; (E) downstream of WWTP2; (F) Roquefavour.
## 3 Materials and Methods
### Feature Importance Quantification Methodologies  
For a comprehensive interpretation of model decisions, feature importance metrics were calculated for each architecture to elucidate the contribution of individual water quality parameters to potability classification.  
For the enhanced tabular transformer architecture, feature importance was derived from the weights of the initial linear transformation layer:  
\[\text{Importance}j^{ETT}=\frac{1}{h}\sum i=1^{h}\big{|}W_{1,j}\big{|} \tag{34}\]  
where \(W_{1}\) represents weights from the first fully connected layer following the transformer encoder, and \(h\) denotes the hidden dimensionality. This approach leverages the absolute values of the weights to quantify how much each input feature contributes to the latent representation [30]. This method is consistent with interpretability techniques used in neural networks, where layer weights are analyzed to understand feature contributions.  
For the feature-tokenized transformer, feature importance was calculated by aggregating the absolute values of the weights across feature tokenizers:  
\[\text{Importance}j^{FTT}=\frac{1}{h}\sum i=1^{h}\bigg{|}\frac{1}{T}\sum_{t=1}^{ T}W_{j,t,i}\bigg{|} \tag{35}\]  
where \(W_{j,t,j}\) represents the weight connecting the \(j\)-th feature's \(t\)-th token to the \(i\)-th dimension of the embedding space. This approach reflects the contribution of each feature across its tokenized embeddings [28]. The aggregation captures both local and global importance within the embedding space.  
For the SAINT architecture, feature importance was derived from the weights of the initial linear transformation layer:  
\[\text{Importance}j^{SAINT}=\frac{1}{h}\sum i=1^{h}\big{|}W_{e,ji}\big{|} \tag{36}\]  
where \(W_{e}\) denotes the weight matrix of the initial feature embedding layer. This method emphasizes the role of embedding weight in capturing feature relevance, aligning with the attention mechanism's focus on feature interactions [7].  
The TAPE architecture combines weights from both the encoder and decoder layers to capture bidirectional importance: Importance\(j^{TAPE}=\frac{1}{h}\sum i=1^{h}\big{|}W_{1,j,i}\big{|}\cdot\frac{1}{h}\sum_{t=1}^{ h}\big{|}W_{1,ij}^{\prime}\big{|}\), representing the product of absolute weights in the first encoder layer and first decoder layer, capturing both the feature's importance for representation learning and reconstruction [12].
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 3 Handshake Performance Measurements
### Network Stack Configuration
#### 3.2.1 Maximum Transmission Unit and Handshake Message Size
[PERSON]'s algorithm minimizes TCP header overhead but limits the number of unacknowledged segments in flight.
## 2 Dynamic Model of Recloser-Fuse  
At present, recloser-fuses are used as typical protective devices installed in a distribution system. This paper utilizes a dynamic model to examine the coordination of recloser-fuses. Generally, the inverse-time overcurrent relay (OCR) generates a tripping signal in the recloser [4]. The dynamic model of the recloser can be imitated by inverse-time OCR characteristics. This consists of the reset time (\(F_{1}(I)\)) and the pickup time (\(F_{2}(I)\)), which are expressed in Equations (1) and (2), respectively [21]:  
\[F_{1}(I)=\left(\frac{t_{r}}{1-M^{2}}\right);\quad 0<M<1, \tag{1}\]  
\[F_{2}(I)=\left(\frac{A}{M^{p}-1}+B\right);\quad M>1, \tag{2}\]  
where \(I\) is the input current, and \(M\) is the ratio of the input current to the pick-up current. Here, \(t_{r}\), \(A\), \(B\), and \(p\) are the constant values defined in Reference [22]. Finally, the dynamic model of the recloser can be expressed as  
\[\sum\
olimits_{k=1}^{N_{0}}\!\left(\frac{1}{F_{2}(I_{k})}-\frac{1}{F_{1}(I_{k })}\right)\!\Delta t=1, \tag{3}\]  
where \(N_{0}\) is the number of fault current samples until the relay sends the tripping signal, and \(\Delta t\) is the sampling time interval. The tripping signal is sent to the recloser when the cumulative sum of Equation (3) equals one.  
Certainly, the fuse will melt if its accumulative energy (\(E_{acc}\)) exceeds the catalog energy (\(E_{cat}\)) designed by the manufacturer. The manufacturer does not usually provide \(E_{cat}\) in the data sheet. However, \(E_{cat}\) can be analytically calculated by  
\[E_{cat}=l_{m}^{2}t_{m}, \tag{4}\]  
where \(I_{m}\) is the melting current, and \(T_{m}\) is the melting time. The catalog energy equation of the dynamic model is prepared by means of curve fitting, with a catalog energy curve plotted by using Equation (4). Otherwise, the accumulative energy equation of the dynamic model is built by  
\[E_{acc}=\int_{t_{0}}^{t_{1}}l_{f\!t}^{2}dt, \tag{5}\]  
where \(t_{0}\) is the initial time when the current is larger than the rated current of the fuse at the beginning, and \(t_{1}\) is the final time when the fuse melts completely. The results obtained by the catalog energy equation and the accumulative energy equation are compared to assign the status of the fuse [23].
## 5 User Interface and Set Up
### Test Case: Voting Process
#### 5.2.3. Starting and Monitoring the Voting Process  
The administrator initiates the voting period by triggering the startVoting function, transitioning the system to the voting phase (see Figures 17-20). They monitor the voting process by:  
* **Managing Voter Authentication**: Ensuring that only eligible voters can participate;
* **Overseing Vote Casting**: Monitoring transactions to guarantee that each vote is securely recorded on the blockchain and that no double voting occurs.