esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Modeling and Implementations of Software-Defined Vehicular Networks for V2X  
### Definition of Software-Defined Networks  
Software-defined networks (SDNs) [30] are based on the separation of data and control planes. In SDNs, communication between the control layer and network layer takes place through the SDN control protocol because the control plane and forwarding plane are decoupled. Based on this principle of decoupling data and control plane, a standard protocol with multivendor support was needed for enabling communication between SDN's layers. As a result, OpenFlow was developed for this purpose [30]. OpenFlow was the first open-source control protocol for communicating between the SDN controller and the network devices. OpenFlow enables the implementation of a user application program to manipulate directly network devices without implementing various network protocols. Furthermore, OpenFlow maintains what it calls a flow table [31] on the network device (forwarding devices). The flow table contains information on how the data needs to be forwarded [30]. The SDN controller can then use OpenFlow to program the network devices of an OpenFlow-enabled switch by altering this flow table [32]. To program the forwarding information and set up the path across the network, the OpenFlow architecture supports two modes of operation, reactive and proactive [33]. The reactive mode is the default method of implementing SDN using OpenFlow and assumes that there is no intelligence of a control layer running on the network devices. In this mode, the first packet of the data traffic received on any of the forwarding nodes is sent to the SDN controller, and then the SDN controller uses this information to program the flow across of the whole network. In proactive mode, the SDN controller is preconfigured with some default flow values, and the traffic flow is programmed preemptively as soon as the switch is brought up. SDN controller and switches exchange the flow of information over the network using a secure channel such as Secure Socket Layer (SSL) or Transport Layer Security (TLS) while the OpenFlow manages communication between network layer and control layers [34; 35].
# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 4 Experiments
### Error Analyses  
Subsequently, we conduct error analyses to further substantiate the effectiveness of our approach. As illustrated in Figure 6, we randomly select four categories for error analysis, encompassing objects of diverse scales. Our approach outperforms the baseline method across various thresholds. When disregarding localization errors, MSBA surpasses the baseline, attributed to our approach's ability to offer more accurate classification information. Furthermore, when excluding errors associated with similar classes from the same supercategory and different classes, our method exhibits noteworthy enhancements compared to the baseline. This underscores MSBA's superior location accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **MSBA** & \(\mathbf{AP^{b}}\) & \(\mathbf{AP^{b}_{S}}\) & \(\mathbf{AP^{m}}\) & \(\mathbf{AP^{m}_{L}}\) \\ \hline \multirow{4}{*}{RetinaNet} & PVT-Tiny & \multirow{4}{*}{\(\surd\)} & 36.6 & 21.9 & - & - \\  & & & 37.8 & 23.0 & - & - \\ \cline{3-6}  & & & **(+1.2)** & **(+1.1)** & **-** & **-** \\ \cline{2-6}  & & & 40.4 & 24.8 & - & - \\ \cline{2-6}  & PVT-Small & \multirow{4}{*}{\(\surd\)} & 40.9 & 25.3 & - & - \\ \cline{3-6}  & & & **(+0.5)** & **(+0.5)** & **-** & - \\ \hline \multirow{4}{*}{Mask R-CNN} & Swin-Tiny & \multirow{4}{*}{\(\surd\)} & 42.7 & 26.5 & 39.3 & 57.8 \\  & & & 43.6 & 27.8 & 39.9 & 58.4 \\  & & & **(+0.9)** & **(+1.3)** & **(+0.6)** & **(+0.6)** \\ \cline{1-1} \cline{2-6}  & & & 46.0 & 31.3 & 41.7 & 59.7 \\ \cline{1-1} \cline{2-6}  & & & 47.1 & 31.9 & 42.4 & 60.5 \\ \cline{1-1} \cline{2-6}  & & & **(+1.1)** & **(+0.6)** & **(+0.7)** & **(+0.8)** \\ \cline{1-1} \cline{2-6}  & & & 48.2 & 32.1 & 43.2 & 62.1 \\ \cline{1-1} \cline{2-6}  & Swin-Small \({}^{\ddagger}\) & \multirow{2}{*}{\(\surd\)} & 48.7 & 32.8 & 43.4 & 62.8 \\ \cline{1-1} \cline{2-6}  & & & **(+0.5)** & **(+0.7)** & **(+0.2)** & **(+0.7)** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with transformer-based backbone on object detection: Performance comparisons paired with Mask R-CNN. The baseline methods are integrated with FPN. \(\ddagger\) represents the models trained with extra tricks such as multi-scale crop and longer training schedule.
## 3 Data and Methodology
### Data Pre-Processing
#### 3.1.2 Imputation of Missing Values  
The imputation of missing values was performed using the Python's interpolate function from the Pandas library, which fills in the missing values of a data series with estimated values based on the existing values before and after the data gap. This approach allowed us to fill in all the intervals from which data was missing, except for the 38-day failure recorded between February and March due to its extended duration.
# In-Field Quantum-Protected Control-Based Key Distribution with a Lossy Urban Fiber Link
## 5 Results
Note that key postprocessing is performed after accumulating data over intervals of \(\tilde{N}=3\) to 10 cycles. Consequently, the plotted \(R\) is an average computed over each \(\tilde{N}\) interval, appearing as a constant value within that range. The dashed horizontal line indicates the global average of \(R\). frequencies. We find that the mean deviates from the ideal \(n/2\) on average by a negligibly small value, in the order of one thousandth of a percent. Similarly, the variance discrepancy remains within two standard deviations (see Section 7.4 in Methods for details on their calculation) from the ideal \(n/4\). In this scope, it is important to note that these properties of the key lie within statistical error caused by a finite length of the samples across the full range of \(n\) observed in the experiment. In other words, the fidelity of the statistical model applied to experimental data is high, though it decreases slightly for a small fraction of block lengths. Additionally, in Figure 6c, we show the fluctuations of the mean along with its autocorrelation function for a fixed block length (\(n=10^{4}\)). In support of this, the key passes the full suite of NIST statistical tests, as summarized in Table 1. Taken together, these results affirm the statistical quality of the secret key and absence of correlations. Figure 6: Statistical analysis of the final key. The blue line represents the final key statistic fitted to the theoretical model. The mean (**a**) of the sum of bit values within blocks is compared to the theoretical prediction for a wide range of block lengths. Further, the variance (**b**) in this sum across the sample is compared to the theoretical model. Fidelity in these figures stands for the percentage of the lengths of the blocks for which experimental results fit with the experimental model within the \(2\sigma\) confidence interval. The latter is represented by the error bars, and a fit to the population central moments as a function of \(n\) is depicted by a green line. Panel (**c**), which depicts the parameter \(p\) that provides the closest fit to the theoretical model (see Section 7.4 in Methods), shows changes throughout multiple Transmission and Control cycles, and its autocorrelation (black line). For this demonstration, the length of the blocks was taken as \(n=\) 10,000.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 3 Design of Proposed Optimal Producer Mobility Support Solution
### Mobility Interest Packet  
The _MI_ packet is designed as an extension of the producer's application. The packet comprises three fields--current prefix name, new prefix name, and mobility status tag, as shown in Figure 2. The namespace of the prefix name contains the hierarchy of the producer name, the access point name, the router, and the domain name. For example, let us assume we have a producer _p1_ attached to access point _ap1_ and content router (CR) _cr1_ in domain _d1_; the name prefix will be _p1/ap1/cr1/d1/_. For content called movie, the prefix is _p1/ap1/cr1/d1/movie_.  
Figure 2 shows a representation of our newly designed _MI_ packets that have the current prefix... _/d1/data(1,2,3...n)_ in domain 1 (D1) and the new prefix... _/d2/data(1,2,3..n)_ when the mobile producer moves to the new domain called domain 2 (D2). The mobility flags representing the normal prefix when the producer is at rest, move within the same domain, and moving to another domain are 00, 01, and 11, respectively. The mobility flag is a data bits used to indicate the movement status of the mobile producer when it is at rest or moving. The _MI_ packets are tagged with a mobility flag to make the intermediate router aware of the movement and decide whether to make the update or ignore it. The update is ignored when the mobility status tag is 00.
## 2 Descriptions of Multiobjective DWTA Problems
### Objective Functions of the DWTA Model
#### 2.3.2 Ammunition Consumption  
In addition to meeting tactical requirements, a WTA decision should also consider operational costs. The second objective function is to minimize the ammunition consumption of weapons at all stages. The corresponding formulation is expressed as:  
\[C=\sum_{s=1}^{S}\sum_{j=1}^{T}\sum_{i=1}^{W}\beta_{i}u_{ij}(s)x_{ij}(s) \tag{5}\]  
where \(u_{ij}(s)\) is the ammunition consumption allocated by weapon \(i\) to target \(j\) at stage \(s\), \(\beta_{i}\) is the ammunition unit cost of weapon \(i\). We assume that all weapons are the same, and \(\beta_{i}\) is assumed to be constant. The ammunition consumption matrix is \(U(s)=\big{[}u_{ij}(s)\big{]}_{T\times W}\).
# Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data
## 2 Methods
### Data Acquisition Geometry  
The training data pairs required for the DNN approach taken in this paper consist of weld models generated by Equation (1) and the associated travel-time data arising from their modelled linear phased array inspection (see Section 2.3). Three separate ultrasonic transducer array configurations are considered: a through-transmission configuration (Figure 3a), where an array of 32 transmitting elements with 1 mm pitch is placed on the top of the weld, and an array of 32 receiving elements with 1 mm pitch is placed directly opposite on the bottom of the weld; an offset through-transmission configuration (Figure 3b), where the 32 transmitting elements are placed at the top of the weld and  
Figure 2: (**a**) Grain orientations given by the Ogilvy model (Equation (1)) used as an underlying background model for the training data. (**b**) Example grain orientation maps used to generate training data, computed as uniformly random perturbations to the Ogilvy model within \(\pm 10^{\circ}\) range.  
the 32 receiving elements are placed at the bottom, offset to the right by 20 mm; and a pulse-echo configuration (Figure 3c), where a single transducer array of 32 elements (with 1 mm pitch) is placed on top of the weld, and each transducer element acts both as a transmitter and a receiver. For each of these configurations (defined by **c**, a matrix containing the transmitter and receiver co-ordinates), the time of flight (ToF) matrix \(\mathbf{T_{m}}\) is constructed from \(32\times 32=1024\) travel times, representing the time of flights between every transmit-receive pair of elements. Note, that in the pulse-echo configuration, this time of flight does not represent the direct path between two elements (the surface wave), but instead the time taken for the transmitted wave to reflect from the back wall and return to the receiving element.  
Figure 3: Transducer elements are positioned in three configurations: (**a**) a through-transmission configuration, where an array of 32 transmitting elements are placed on the top of the weld, and an array of 32 receiving elements are placed directly opposite on the bottom of the weld; (**b**) an offset through-transmission configuration, where transmitting elements are placed at the top of the weld and the receiving elements are placed at the bottom, but offset to the right by 20 mm (to avoid the weld bead); (**c**) a pulse-echo configuration, where the array is placed on top of the weld, and each transducer element acts both as a transmitter and a receiver.
## 3 Results and Discussion
### Extraction Efficacy
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DOC, mg\(\cdot\)L\({}^{-1}\)** & **E\({}_{\text{e}}\), \%** & **(Ni\({}^{2+}\))\(\cdot\)10\({}^{8}\), M** & **(NiCl\({}^{+}\))\(\cdot\)10\({}^{8}\), M** & **(NiAH)\(\cdot\)10\({}^{8}\), M** \\ \hline
0 & 30.15 \(\pm\) 1.95 & 6.34 & 2.13 & 0 \\
3.97 & 26.82 \(\pm\) 0.66 & 5.46 & 1.84 & 1.18 \\
8.71 & 22.71 \(\pm\) 2.73 & 4.74 & 1.59 & 2.16 \\
12.58 & 18.85 \(\pm\) 0.75 & 4.27 & 1.44 & 2.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Extraction efficacy and theoretical major species distribution depending on DOC concentration. Figure 2: Extraction efficacy dependence on dissolved organic carbon (DOC) concentration. (\(\bullet\)) 0 mg\(\cdot\)L\({}^{-1}\); (\(\blacksquare\)) 3.97 mg\(\cdot\)L\({}^{-1}\); (\(\blacklozenge\)) 8.71 mg\(\cdot\)L\({}^{-1}\); (\(\blacklozenge\)) 12.58 mg\(\cdot\)L\({}^{-1}\).
###### Abstract  
This paper addresses the challenge of decarbonizing residential energy consumption by developing an advanced energy management system (EMS) optimized for cost reduction and energy efficiency. By leveraging the thermal inertia of building envelopes as a form of thermal energy storage (TES), the proposed EMS dynamically balances energy inputs from the electrical grid, photovoltaic (PV) systems, and battery storage, while regulating HVAC operations. Employing model predictive control (MPC) and integrating real-time data on energy prices, weather conditions, and consumption patterns, the system minimizes energy costs, while maintaining indoor thermal comfort. Simulations conducted for both summer and winter scenarios demonstrate significant reductions in energy costs. Laboratory and real-world testing confirm the EMS's efficacy in enhancing energy efficiency, increasing PV self-consumption, and reducing reliance on grid energy, marking a step forward in sustainable energy solutions for residential applications.  
energy management system; thermal energy storage; model predictive control; renewable energy integration; residential heating and cooling; HVAC optimization; sustainable energy systems; real-time energy management; smart homes 2020 rights rights 10.1
## 2 Results
### Direct Functionalization of PLA Backbone: Towards PLA-Based Graft Copolymers
#### 2.2.5 Other PLA-Based Graft Copolymers  
PLA-_g_-Nitrilotriacetic Acid (PLA-_g_-NTA)  
In the field of \"green packaging\", it is desirable to have non-migratory, metal-chelating and biodegradable materials. To this end, metal-chelated nitrilotriacetic acid (NTA) was grafted onto PLA using a classical radical mechanism, as shown in Figure 19[96]. The grafting was evidenced by ATR-FTIR and XPS. Significant radical scavenging and metal-chelating efficacies as well as the ability to delay the degradation of ascorbic acid showed the antioxidant capacity of PLA-_g_-NTA. However, this NTA grafting was not followed by any polymer grafting, even if the grafted carboxylic acids are functional groups.  
PLA-_g_-Vinyltrimethoxysilane (PLA-_g_-VTMS)  
PLA-_g_-VTMS was prepared by a free radical reaction of vinyltrimethoxysilane (VTMS) on PLA in the presence of DCP in a Brabender at 190 \({}^{\circ}\)C for 5 min. The trimethoxysilane was then hydrolyzed to allow the crosslinking of the PLA chains to improve the mechanical properties of the PLA nanofibers (Figure 20) [97]. Electrospun nanofibrous mats based on PLA/NCC and PLA-_g_-silane/NCC nanocomposites were fabricated and compared. PLA-_g_-VTMS was used to improve the mechanical properties of PLA/nanocrystalline cellulose (NCC). In particular, the impact of NCC on improving tensile strength was notable, even though no chemical reaction of PLA-_g_-VTMS with NCC is reported. In addition, a cytotoxicity assessment showed the biocompatibility of the modified nanofibers, making them good candidates for tissue engineering applications.  
Figure 18: Surface-initiated ATRP of methacrylate monomers (from [PERSON] et al. [93], copyright Elsevier, reproduced with permission).  
PLA-g-Poly(Vinyl Pyrrolidone) (PLA-g-PVP)  
A PLA film was treated with a solution of N-vinyl pyrrolidone (NVP) in methanol and AgNO\({}_{3}\) using \({}^{60}\)Co \(\gamma\) - radiation polymerization at a dose of 1-30 kGy at room temperature. After washings, a PLA-g-PVP film was formed on the surface. Silver nanoparticles were also immobilized on the film surface. A surface grafting ratio, in the range of 25-49%, is assessed by the FTIR ratio of the bands at 1660 cm\({}^{-1}\) of PVP and the sum of the bands at 1660 cm\({}^{-1}\) and 1750 cm\({}^{-1}\) of PLA [98]. There is no indication of the grafting degree of PVP onto the PLA backbone. It is noted, however, that PVP grafting significantly accelerated PLA degradation and does not impede cell proliferation [99]. Controlled variation in the grafting ratio could broaden the applications of this material in tissue engineering scaffolds, drug delivery, and the prevention of post-surgical adhesion.
## 3 Results
### Study Characteristics  
The characteristics of the 11 RCTs included for the qualitative synthesis are summarized in Table 2. All eight RCTs selected for the meta-analysis evaluated the efficacy of different type of retention appliances (both removable and fixed) after orthodontic treatment with a multi-bracket appliance; the majority of trials took place in university settings. The total number of pooled observed patients was 987. Six studies [27; 30; 31; 32; 33; 34] included both male and female participants, while in two studies [28; 31], the authors did not specify the numbers of male and female participants. Five studies [27; 29; 30; 33; 34] reported the patients' ages, with an age range between 13 and 17 years. The appliance features were heterogeneous among the selected studies: Six trials evaluated the effects of vacuum-formed retainers [27; 30; 31; 32; 33; 34], four trials evaluated the effects of bonded fixed retainers [27; 28; 29; 30; 31], two trials evaluated the effects of a Hawley retainer [33; 34], one trial evaluated the effects of a Begg retainer [31], and one trial evaluated the effects of a positioner [27].  
Seven studies [27; 29; 30; 32; 33; 34] reported the percentage of patients treated with or without extraction, as shown in Table 2. The times of daily wear for the removable appliances (vacuum-formed retainers, Hawley retainers, Begg retainers, and positioners) varied between full-time and night-time among the RCTs, and the observation time varied from 6 to 24 months. The inter-reviewer agreements for study selection were suitable, with a kappa value of 0.968.  
Figure 1: Flow Chart.
## 3 CNN and Bi-LSTM Model with MODWT and ANFN Layers
### ANFN (Adaptive Neuro-Fuzzy Network) Layer  
Deep learning extracts nonlinear features from large-scale input data and transforms them into high-dimensional feature channels. However, the transferred data are cumbersome to handle in general fuzziness, creating problems in the course of the dimension and increasing amount of computation. The fuzzy c-means (FCM) clustering method can be used to estimate appropriate clusters with the transferred data and compress them to the desired number of output channels. The FCM algorithm is one of various data clustering techniques, representing the degree to which a data point belongs to each cluster as a probability, and it generates a rule value by synthesizing the probability of belonging to the set clusters. As shown in Figure 3, the clusters are generated for each input data channel using FCM clustering. The data point expresses the membership value to which it belongs to the cluster for each channel as the probability of a real number between 0 and 1. Thisrepresents to which cluster each data point belongs to more strongly. The membership values are then leveraged to generate and learn the rules through rule consequents and rule weights, and the final output is calculated. The output value calculates the loss value with the MSE (mean square error) compared to the actual ECG and updates the training parameters contained in CNN and Bi-LSTM with an ANFN layer. The pseudo-codes of the ANFN are described in Algorithm 1.  
```
1:Initialization: center, sigma, ruleConsequents, ruleWeights
2:X = feature data propagated by the Bi-LSTM
3:NormalizedData = (X - min(X))/(max(X) - min(X))
4:For C = 1:numClusters
5:sig = sigma(C,:), cnt = centers(C,:)
6:Distance = NormalizedData - cnt, Membership = 0
7:SquaredDistance = sum(Distance), Squaredsig = sig2
8:For K = 1:input_channel
9:Membership = Membership + exp(-SquaredDistance/(2 \(\times\)Squaredsig(K)))
10:End
11:MembershipValues(k) = Membership
12:End
13:ruleOutput = MembershipValues \(\times\) ruleConsequents
14:weightedSum = sum(ruleOutput \(\times\) ruleWeights)
15:sumWeights = sum(ruleWeights)
16:finalOutput = weightedSum/sumWeights
17:loss = mse(finalOutput,trueSignal)
18:Update of weights and biases in the CNN and Bi-LSTM center, sigma, ruleConsequents, ruleWeights in the FAL
```  
**Algorithm 1** Pseudo-code of ANFN  
Figure 3: Process procedure in the design of the ANFN.
## 2 Materials and Methods  
### Model Preparation  
The sample size was chosen based on a previous study [17] that used 10 samples per study group. For this study, the entire 30 samples were made by using a standard maxillary dentoform as a template. First, the maxillary dentoform was scanned in Standard Tessellation Language (STL) file format by a dental laboratory 3D scanner (Straumann) CARES(r) CADCAM; Institute Straumann AG). Next, the STL file was exported to the 3D software program \"MeshmixerTM\" to design the right maxillary molar dedentulous ridge as the simulated alveolar ridge to mock up a real clinical situation. The alveolar ridge morphology was designed as 14 mm in depth from the coronal to the apical region and 7 mm in bucco-lingual width. The uppermost coronal position of the alveolar ridge was placed at 3 mm below the gingival margin of the right maxillary first molar. Then, the STL file of the previously designed template model was printed with a laboratory 3D printer (Straumann CARES(r), P30, Institute Straumann AG). These template models were coated with lacquer and then duplicated in a silicone mold using drillable polyurethane (Axson, Germany) for 30 models, 10 models per group. All of these models were quality controlled by caliper measurements at the base of the individual model to confirm the correctness of duplicated models' dimensions. All models were weighed by a digital analytical balance ([PERSON], Germany). Then, all models with various weights were distributed equally into each group to ensure that every group had a similar distribution of the model in terms of weight.